Cross-Layer Energy Optimization for IoT Environments

energies Review

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities Kirshna Kumar 1 , Sushil Kumar 1 , Omprakash Kaiwartya 2 and Nauman Aslam 2 1 2 3

*

ID

, Yue Cao 2, *

ID

, Jaime Lloret 3

ID

School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India; [email protected] (K.K.); [email protected] (S.K.) Department of Computer and Information Sciences, Northumbria University, Newcastle NE2 1XE, UK; [email protected] (O.K.); [email protected] (N.A.) Department of Communications, Polytechnic University of Valencia, Camino de Vera, 46022 Valencia, Spain; [email protected] Correspondence: [email protected]; Tel.: +44-7709694048

Received: 27 October 2017; Accepted: 28 November 2017; Published: 6 December 2017

Abstract: Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues. Keywords: Internet of Things; energy efficiency; smart technologies; green computing; heterogeneous networks

1. Introduction The Internet of Things (IoT) is revolutionizing and extending existing fundamental research areas into new dimensions by integrating the concept of intelligence or smartness [1,2]. The new domains, including intelligent transportation systems, smart cities, smart homes, smart industries, autonomous vehicle, smart healthcare are but a few examples of this revolution [3,4]. Some other prominent IoT application domains include automated security devices such as alarms and surveillance systems, automated grids used in industrial metering, vehicular telematics as support for navigation and fleet management, remote maintenance as in vending machine control and industrial automation, and manufacturing control as in production chain monitoring [5,6]. The integration of IoT in almost every aspect of human lives is due to the focus of inventions towards a greener and smarter world for sustainability reasons [7]. IoT use case implementations are increasing day by day. In 2003, 500 million devices were connected to Internet, while the population of the world was 6.3 billion. It is predicted that the number of connected devices will be approximately 50 billion by 2022, which is equivalent to Energies 2017, 10, 2073; doi:10.3390/en10122073

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of the world was 6.3 billion. It is predicted that the number of connected devices will be 2 of 40 approximately 50 billion by 2022, which is equivalent to four times the world’s population. This estimated steep growth shows both the exponential expansion rate of IoT in the world and our dependence IoT enabled devices [8,9]. four times theon world’s population. This estimated steep growth shows both the exponential expansion realizing a smarter world viaonIoT devices, there are two major rate ofTowards IoT in the world and our dependence IoTenabled enabled connected devices [8,9]. constraints forrealizing tiny and asmarter communication anddevices, computing power constraints Towards smarterdevices, world namely, via IoT enabled connected there are two major due to the limitation of smarter energy resources. The main power supply resource in most sensor-enabled constraints for tiny and devices, namely, communication and computing power constraints IoT to devices is a battery, whereby operating sensors consume power forsensor-enabled collection and due the limitation of energy resources. The main power supplybattery resource in most transmission data among neighborhood devices. The accuracy of sensed and collected IoT devices is of a battery, whereby operating sensors consume battery power for collection and information of is data enhanced analysis ofdevices. the data. the analysis increases information the energy transmission amongvia neighborhood TheHowever, accuracy of sensed and collected consumption in analysis IoT devices [10].data. The sensor-enabled smart devices operate, i.e., they sense, receive, is enhanced via of the However, the analysis increases the energy consumption in compute and disseminate information continuously, to facilitate automating intelligent decisions. IoT devices [10]. The sensor-enabled smart devices operate, i.e., they sense, receive, compute and The optimization of energy consumption in sensor-enabled IoT devices has become one of the disseminate information continuously, to facilitate automating intelligent decisions. The optimization fundamental issues due the integration sustainability in recent and smarter world of energy consumption in to sensor-enabled IoTofdevices has become one ofgreener, the fundamental issues due research [11]. Technical standardization bodies such and as the Institute of Electrical and Electronics to the integration of sustainability in recent greener, smarter world research [11]. Technical Engineering (IEEE), Partnership Projects (3GPP) and the European standardization bodies 3rd such Generation as the Institute of Electrical and Electronics Engineering (IEEE), Telecommunications Standard Institute (ETSI) introduced various energy efficient approaches 3rd Generation Partnership Projects (3GPP) and have the European Telecommunications Standard Institute for sensor-enabled IoT various devices energy [12]. In efficient the related literature, various techniques been[12]. suggested (ETSI) have introduced approaches for sensor-enabled IoThave devices In the for optimizing energy consumption in sensor-enabled IoT devices. The techniques span focusing related literature, various techniques have been suggested for optimizing energy consumption in different aspects IoT communication for optimizing energy consumption in sensor-enabled IoTofdevices. The techniquesand spancomputation focusing different aspects of IoT communication and specific use cases. This energy-oriented diverse IoT domainuse cancases. be systematically explored diverse using a computation for optimizing energy consumption in specific This energy-oriented five-layered architecture (see Figure 1). Theusing crossalayer optimizations have been proven1). better the IoT domain can be systematically explored five-layered architecture (see Figure The in cross literature due to the consideration diverse aspects of communication and computing for scalable layer optimizations have been provenofbetter in the literature due to the consideration of diverse aspects IoT use case implementation environments. Figure 1 shows a communication architecture of communication and computing for scalable IoT use case implementation environments. Figure of 1 different IoT use case implementations focusing onuse thecase energy consumptionfocusing aspects.on It the consists of shows a communication architecture of different IoT implementations energy five layers aspects. including sensing layer localsensing processing and layer (LPSL), consumption It consists of five layers(SL), including layer (SL), localstorage processing and storage network/communication layer (NCL), cloud processing and storage layer (CPSL) and application layer (LPSL), network/communication layer (NCL), cloud processing and storage layer (CPSL) and layer (AL). layer The layer haveissues been also application (AL).wise The issues layer wise havehighlighted. been also highlighted. Energies 2017, 10, 2073

Layer

Component

Issues

Application Intelligent Transport

Smart home Street monitoring Fire Detection

Cloud Processing and Storage Cloud storage

Cloud services

Network /Communication

IPSO Internet

addresing

IoT gateway

Local Processing and Storage Parallella

Espruino Respberry pi

Arduino

Sensing Smart Devices

RFID

Camera

Sensors

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Energy saving in presentation Understand-ability User friendly Smart visualization

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Energy saving in Computation Reliability Data portability Cloud managing Efficient Storage

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Energy saving in communication Routing energy efficiency Congestion control Link and path stability

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Local energy saving Computation Analytics Efficient Storage

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Energy saving in data collection Load balancing Self organization

Figure 1. 1. Energy Energy consumption-oriented consumption-oriented layered layered architecture architecturefor forIoT. IoT. Figure

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This paper presents a comprehensive survey on energy optimization in IoT environments focusing on layerwise studies in the area. The energy-oriented techniques have been categorically criticized in terms of their technical contributions and limitations considering different energy-related metrics. Specifically, the contributions of this paper can be divided into following three aspects:

• •

•

Firstly, the significance of cross-layer energy optimization in IoT environments is highlighted focusing on different use case implementations. Secondly, following the layered taxonomy, a model-based qualitative review is carried out on energy optimization in IoT environments focusing on major functional components, strengths and weaknesses. Finally, open research issues and challenges in energy optimization for IoT are identified as future research and development directions in the area.

The remaining sections of the paper are structured as follows: in Section 2, the significance of cross-layer energy optimization in IoT environments is highlighted. In Section 3, related literature surveys on IoT have been revisited. Section 4 presents a comprehensive review on energy optimization in IoT focusing on layered taxonomy, model-based discussion and comparative analysis of recent developments. In Section 5, open research issues and challenges for energy efficiency under IoT environments are identified, followed by conclusions presented in Section 6. 2. Significance of Energy Efficiency in IoT Use Cases In this section, a description about the significance of cross-layer energy optimization in IoT environments is presented, while focusing on various use case implementations. 2.1. Case Study: Smart Home IoT is a technological phenomenon that connects the physical and the computing worlds, while enabling ubiquitous communication at any time using various services for smart home applications. For domestic purposes such as heating control, security, lighting, etc., the smart home concept provides efficient management mechanisms, with increasing usage of smart devices. The smart home control system based on Coordinator-based ZigBee networking (CoZNET) mitigates the interference effect and minimizes the energy consumption of domestic appliances [13]. Moreover the workings of a smart home system based on CoZNET is divided into three sections, namely Smart Interference Control System (SICS), Smart Energy Control System (SECS) and Smart Management Control System (SMCS). SICS enables the smoother communication with lesser packet loss, while mitigating interference effects using multi-attribute decision making-based channel assignment mechanism in the entire smart home. SECS further reduces energy consumption in the smart home, with the usage of a light control system and a household appliance control system, then a number of facilities are provided to users for exploiting the control of the entire smart home system, while implementing a fundamental unit called SMCS. Figure 2 shows the smart home scenario including various domestic appliances such as an air conditioner, refrigerator, dishwasher, washing machine, lights, television, etc. Among these appliances, some appliances consume more electricity, namely the refrigerator, air conditioner, etc. and some consume less electricity such as the lights, television, etc. Moreover electricity consumption for a single appliance also varies based on the execution cycle phase considered. For example, in a washing machine, the electricity consumption of a drying cycle is more than the electricity consumption of a washing cycle. Therefore it is challenging to manage the usage of these domestic appliances in a smart home system efficiently, while minimizing the electricity consumption. Therefore, the issue of energy efficiency plays a significant role to make a smart home system efficient.

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Refrigerator Dish washer Wash basin Exhaust fan Light sensor ZigBee sensor Home User

ZigBee coordinator Air condition

Television Management station

Camera

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Refrigerator Dish washer Wash basin Exhaust fan Light sensor ZigBee sensor Home User

ZigBee coordinator Air condition

Television Management station

Camera

Figure 2. 2. Smart Smart home home control control system. system. Figure

2.2. Case Case Study: Study: Smart Smart Car Car 2.2. Smart home control system. Looking beyond beyond prevalence prevalence as asFigure in the the2.evolution evolution of mobile mobile phones into into smartphones, smartphones, dumb dumb TVs TVs Looking in of phones to smart smart2.2. TVs, the IoTSmart has evolved evolved the the traditional traditional car car into into smart smart cars, cars, while while including including the the properties properties Casethe Study: Car to TVs, IoT has of ubiquitous computing, human computer interaction and application platforms for smart cars. of ubiquitous computing, human computer interaction andphones application platformsdumb for smart cars. Looking beyond prevalence as in the evolution of mobile into smartphones, TVs Therefore, a novel smart car demonstration platform has been presented, in which a transparent Therefore, a novel demonstration platform been in which a transparent to smart TVs, smart the IoT car has evolved the traditional car intohas smart cars,presented, while including the properties windshield displaycomputing, and a number of sensorsinteraction have been for receiving andcars. displaying of ubiquitous human computer and equipped application platforms fordisplaying smart windshield display and a number of sensors have been equipped for receiving and virtual virtual Therefore, information to the driver [14]. Additionally while considering urban street driving scenarios, a novel demonstration platform has been presented, in which a transparent information to the driversmart [14].car Additionally while considering urban street driving scenarios, three windshieldapplications display and anamely, number of have been equipped for receiving and displaying three potential a sensors visibility restoration application, a driving environment potential applications namely, a visibility restoration application, a driving environment understanding virtual information to the driver [14]. Additionally while considering urban street driving understanding application and a nighttime contrast enhancement application havescenarios, been discussed application a nighttime contrast application have abeen discussed to provide three and potential applications namely,enhancement a visibility restoration application, driving environment to provide operational safety to smart cars. operational safety to application smart cars.and a nighttime contrast enhancement application have been discussed understanding Figure 3 illustrates the system architecture of the smart car demonstration platform, focusing on to provide operational to smart cars. Figure 3 illustrates the safety system architecture of the smart car demonstration platform, focusing on its three major units namely, the interaction unit, computing and communication unit, and Figure 3 illustrates architecture the smart car demonstration platform, focusing on its three major units namely, the thesystem interaction unit, of computing and communication unit, and application application unit. Through the application unit, an open platform is provided to the smart its three major units namely, the interaction unit, computing and communication unit, and car for unit. Through the application unit, an open platform is provided to the smart car for users to download Through the application unit, an open platform is provided or to automobile the smart car companies for users toapplication downloadunit. applications from government/private utility providers applications from government/private utility providers or automobile companies in order to customize users download applications from government/private utilityand providers or automobile companies in order to to customize the vehicle’s performance, features capabilities. In order to meet the the vehicle’s performance, and performance, capabilities. features In orderand to meet the requirements of the in order to customizefeatures the vehicle’s capabilities. In order to meet theusers in requirements of the users in areas such as entertainment, communication and social, shopping, areas such as entertainment, communication social, shopping, travel utilities, smartshopping, car-appropriate requirements of the users in areas such and as entertainment, communication and social, travel utilities, smartsmart car-appropriate applications can canbebedeveloped. developed. a smart car system, because travelcan utilities, car-appropriate aIn smart system, because applications be developed. In a smartapplications car system, because of theIn usage of car various battery powered of the usage of various battery powered devices such as sensors, display units, computing and theas usage of various battery devices as sensors, display units, and devices of such sensors, display units,powered computing andsuch communication devices, thecomputing energy consumption communication devices, the the energy consumption interms termsofof fuel, electricity, etc.becan be increased. communication devices, energy consumption in fuel, electricity, etc. can increased. in terms of fuel, electricity, etc. can be increased. Therefore minimizing the energy consumption in Therefore minimizing energyconsumption consumption in IoT IoT applications will be will a Therefore minimizing the the energy in smart smartcar-enabled car-enabled applications be a smart car-enabled IoTinapplications will be a significant issue in the future. significant issue the future. significant issue in the future. Application Unit ApplicationGovernment Unit Automobile Companies Utility Provider Automobile Companies Government Insurance Companies Private UtilityUtility ProviderProvider

Insurance Companies Private Utility Computing and Communication Unit Provider Communication mgmt Device Management Computing and Communication Unit Bluetooth, Wi-Fi, NFC, Communication mgmt Operating System 3G/3.5G/4G/LTE Device Management

Bluetooth, Wi-Fi, NFC, Interaction Unit Operating System 3G/3.5G/4G/LTE Eye Sensors Interaction UnitImage Sensors Transparent Windshield Display EyeSensors Sensors Voice Transparent Windshield Display

ImageSensors Sensors Gesture Voice Sensors

Figure 3. System architecture of a Smart Car demonstration platform.

Gesture Sensors platform. Figure 3. System architecture of a Smart Car demonstration Figure 3. System architecture of a Smart Car demonstration platform.

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2.3. Case Study: Smart Logistics Traditional transport logistics systems are not efficient because of a lack of coordination and 2.3. Case Study: Smart Logistics sharing among the entities transported by logistic vendors, for e.g., trucks returning empty and Traditional transport logisticswastage systemsofare not efficient because of a lack of partially full trucks. This results goods, resources, etc. Therefore, a coordination novel conceptand of sharing among the entities transported by logistic vendors, for e.g., trucks returning and Internet of Perishable Logistics (IoPL) has been introduced to make transportation moreempty intelligent partially full in trucks. results wastage of goods, resources, etc. Therefore, novel concept and efficient, whichThis transportation of package or packages containers between aa source (for e.g.,of a Internet of Perishable Logistics (IoPL) has been introduced to make transportation more intelligent and factory, a farm, etc.) and destination (for e.g., a retailer) is similar to the flow of packets in the efficient,[15]. in which transportation of packagefor or packages between a source (for e.g., a factory, Internet Hence a layered architecture IoPL has containers been proposed, while borrowing from cyber a farm, etc.) destination (for e.g., ainretailer) is similar to the flow packets in the Internet Internet withand various issues identified the logistics system. The keyofissues identified in IoPL [15]. are Hence a layered architecture for IoPL has been proposed, while borrowing from cyber Internet with standardization (loading, unloading and storing machines and their internet enablement using IoT various issues identified in the logisticssharing. system.The Theenablement key issues identified in IoPLand are communication standardization devices), resource and infrastructure of information (loading, unloading and storing machines and their internet enablement using IoT devices), resource technology (ICT)-based capabilities (RFID-based labeling/addressing, barcoding, GPS-based and infrastructure sharing. The enablement of information and communication technology (ICT)-based localization, IoT-based sensing and actuation, etc.) will make a smart logistic system more proactive capabilities and efficient.(RFID-based labeling/addressing, barcoding, GPS-based localization, IoT-based sensing and actuation, will sensing make a smart logistic system of more proactive efficient. which is one of In Figure 4etc.) quality and communication product boxesand is illustrated, In Figure 4 quality sensing and communication of product boxes is illustrated, is one of the the aspects of minimizing wastage of food because of spoilage and contamination. which Tiny sensors such aspects minimizing food because of spoilage contamination. Tinytime sensors such as as Food of Scan, C2Sense,wastage etc. areofinserted into containers orand shipping boxes at the of delivery Food Scan, C2Sense, etc. are inserted into containers or shipping boxes at the time of delivery inside inside a warehouse or in a truck. Then these sensed data along with a box ID for food contaminationa warehouse in a truck. to Then sensed data along with box ID for food contamination tracking tracking areor transmitted the these central controller through anaappropriate sensor network. During the are transmitted to the central controller through an appropriate sensor network. During the flow of flow of data in the Internet, energy consumption is focus issue for localization, scheduling, data in the Internet, energy consumption is focus issue for localization, scheduling, communication, communication, etc. In a similar way, during the flow of package containers from source to etc. In a similar way,of during flow of package containers frombefore sourcethe to researchers destination for thesensing, issue of destination the issue energythe consumption is a major challenge energy consumption is a major challenge before the researchers for sensing, standardization, resource standardization, resource sharing, transportation, etc. to provide an efficient smart logistics system sharing, in IoPL. transportation, etc. to provide an efficient smart logistics system in IoPL. Central Controller Connection via cellular

Shipping Box

MI-enabled sensor device

MI and Wi-Fi enabled sink node

Wi-Fi MI

Smartphone as hotspot

Driver Cabin

Truck container

Figure Figure 4. 4. Quality Quality sensing sensing and and communication communication for for product product boxes. boxes.

2.4. 2.4. Case Case Study: Study: Smart Smart Agriculture Agriculture Water input parameters in agricultural systems. In Water and and electricity electricityboth bothare arethe themost mostprominent prominent input parameters in agricultural systems. aInsituation of inadequate rainfall, crop production is restricted due to inefficient usage of water a situation of inadequate rainfall, crop production is restricted due to inefficient usage of water resources resources and and irrigation irrigation scheduling. scheduling. From From this this perspective, perspective, the the Cloud Cloud of of Things Things (CoT)-based (CoT)-based smart smart irrigation system has been introduced to utilize water and energy in an effective and efficient irrigation system has been introduced to utilize water and energy in an effective and efficientway, way, while irrigation system becomes more efficient and less while enhancing enhancingagricultural agriculturalproduction production[16]. [16].The The irrigation system becomes more efficient and costly by smart water usage (supervising and monitoring water less costly by smart water usage (supervising and monitoring watercapacity, capacity,timing, timing,location location and and duration of flow on the basis of data analytics). The combination of data analytics collection from duration of flow on the basis of data analytics). The combination of data analytics collection from the the CoT CoT (weather (weather situation, situation, type type of of soil, soil, land land condition, condition, crop crop features, features, etc.) etc.) and and data data capture capture by by sensors sensors measuring moisture,heat, heat, water stress, chemicals, etc., provide practical information. This measuring moisture, water stress, chemicals, etc., provide practical information. This information information helps farmers utilize fertilizers, water and pesticides in appropriate locations and helps farmers utilize fertilizers, water and pesticides in appropriate locations and quantities. quantities. Figure 5 illustrates a real time CoT-based smart irrigation system, by enabling task automation based on the data received by the sensors and the devices. In addition, performance statistics and

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Sensors

Fog

Farm

Local Processing

Sensors

Fog

Gateway Internet

Data Center

Internet

Figure 5 illustrates a real time CoT-based smart irrigation system, by enabling task automation based on the data received by the sensors and the devices. In addition, performance statistics and reports are generated, while providing real time information to the farmer based on the business business activity, which helps the farmer to make timely and well-informed decisions. Then the farmer can activity, which helps the farmer to make timely and well-informed decisions. benefit in terms terms of of data data access, access, storage, storage, synchronization synchronization and cost, when farming applications are moved to the cloud daily. daily. In the applications related to smart agriculture, energy usage is also one of the most crucial parameters in terms of lighting, boosters, pumps and other purposes. In the future, energy-efficient smart agricultural system should be proposed, while considering considering other other parameters parameters such as water based on IoT capabilities. IoT capabilities.

Cloud IoT Big data Procesing

Wi-Fi GSM LTE

Figure 5. Real time CoT-based automated irrigation system. Figure 5. Real time CoT-based automated irrigation system.

2.5. Case Study: Smart Fabric 2.5. Case Study: Smart Fabric During the 1990–2000 period the smart clothes concept merging ICT capabilities (sensing, During the 1990–2000 period the smart clothes concept merging ICT capabilities (sensing, actuating, actuating, computing and communication) with clothes and textiles to augment their appeal and computing and communication) with clothes and textiles to augment their appeal and develop protective develop protective textiles was explored by several companies (Philips, Infineon, DuPont, etc.). textiles was explored by several companies (Philips, Infineon, DuPont, etc.). Multimodal sensor, Multimodal sensor, human-computer interface, continuous Internet access, long distance human-computer interface, continuous Internet access, long distance communication and unobtrusive communication and unobtrusive packaging functionalities are available in smart phone apps for packaging functionalities are available in smart phone apps for smart textile systems. In applications smart textile systems. In applications such as sports, healthcare, protection and maintenance, a such as sports, healthcare, protection and maintenance, a larger area and closer contact with skin than larger area and closer contact with skin than possible with sensors or smartphones is required. possible with sensors or smartphones is required. Textile electrodes provide better, continuous and Textile electrodes provide better, continuous and direct skin contact for recording skin conductance direct skin contact for recording skin conductance for stress evaluation, measuring ECG or estimating for stress evaluation, measuring ECG or estimating local pressure to prevent ulcers [17]. Several local pressure to prevent ulcers [17]. Several smart textile technologies to scale up weaving of integrated smart textile technologies to scale up weaving of integrated circuits into textiles have been proposed. circuits into textiles have been proposed. Strain, humidity, and temperature sensing, light transport Strain, humidity, and temperature sensing, light transport and fluid monitoring functionalities have and fluid monitoring functionalities have been enabled in threads. Because of the smaller surface and been enabled in threads. Because of the smaller surface and limited functional complexity achievable limited functional complexity achievable in a textile thread, several wearable threads equipped with in a textile thread, several wearable threads equipped with sensors and transceiver-integrated sensors and transceiver-integrated CMOS circuits provide higher functional density, but smart cloth is CMOS circuits provide higher functional density, but smart cloth is still a very new research area in still a very new research area in IoT even before researchers consider various issues such as efficient IoT even before researchers consider various issues such as efficient energy usage, reliability, less energy usage, reliability, less cost, etc. Because of the smart devices such as sensors, smartphone, cost, etc. Because of the smart devices such as sensors, smartphone, CMOS circuits used in smart CMOS circuits used in smart clothes systems, energy consumption has become the crucial issue for clothes systems, energy consumption has become the crucial issue for the future. the future. 2.6. Simulation Simulation Oriented Oriented Characterization Characterization 2.6. In this this section, section, recent recent technical technical developments developments in in energy-oriented architectures and and protocols protocols for for In energy-oriented architectures IoT environments are explored experimentally in a realistic simulation environment. The IoT environments are explored experimentally in a realistic simulation environment. The comparative comparative investigation focuses on the performance of state-of-the-art techniques investigation focuses on the performance monitoring ofmonitoring state-of-the-art techniques considering considering energy-oriented network evaluation metrics. The simulations have been performed on a energy-oriented network evaluation metrics. The simulations have been performed on a NS2 network NS2 network simulator considering a 100 m ×experimental 100 m squarearea experimental area for with 500 nodes for a simulator considering a 100 m × 100 m square with 500 nodes a total simulation total simulation duration of 60 min to represent an IoT-based monitoring scenario. For robust duration of 60 min to represent an IoT-based monitoring scenario. For robust experimental verification verification a standard simulator has been used considering usage in a wide range aexperimental standard simulator has been used considering its usage in a wide range ofitsexperiments. The major of experiments. The major simulation in Table251 rounds are usedoftorepeated conduct 25 rounds of simulation parameters listed in Tableparameters 1 are usedlisted to conduct experiments repeated experiments considering different arbitrary seed numbers to acquire a 95% considering different arbitrary seed numbers to acquire a 95% certainty interval. In thiscertainty section, interval. In this section, various routing protocols, namely AODV, ER-RPL, REL and FFSC, are various routing protocols, namely AODV, ER-RPL, REL and FFSC, are simulated and analyzed simulated and analyzed usingparameters. the mentioned simulation parameters. The evaluation focus of isthis using the mentioned simulation The focus of this simulation-based to simulation-based evaluation is to realize a resource-constrained scenario in an IoT environment. To realize a resource-constrained scenario in an IoT environment. To this end, lower transmission this end, lower transmission range limit, lower power consumption in signal transmission range limit, lower power consumption rate in signal transmission andrate reception, a restricted areaand for

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Energies 2017, 10, 7 of 41 monitoring are2073 the major considered parameter settings. As initial results, two metrics are considered for evaluating some data dissemination techniques under an IoT centric parameter settings in the reception, aenvironment, restricted area for monitoring are the major parameter As initial simulation namely packet delivery ratio andconsidered network lifetime. Thesettings. precise definition results, two metrics are considered for evaluating some data dissemination techniques under an IoT of network lifetime in sensor-enabled networks depends on the application scenario. For example, centric parametermonitoring settings in the simulation environment, namely packetofdelivery ratio network security-oriented applications require continuous operation each node in and the network, lifetime. The precise definition of network lifetime in sensor-enabled networks depends on the i.e., energy exhaustion of even 1% of the nodes is considered the network lifetime limit, whereas in application scenario. For example, security-oriented monitoring applications require continuous normal environmental monitoring applications, energy exhaustion of even 10% of the nodes is not operation ofthe each node in the network, i.e., energy even 1% of the nodes is considered considered network lifetime limit assuming thatexhaustion 90% of theofnetwork nodes can still perform their the network lifetime limit, whereas in normal environmental monitoring applications, tasks smoothly. In our simulation-based network evaluation case, the energy exhaustion of 2%energy of the exhaustion of even 10% of the nodes is not considered network lifetime limit assuming that 90% nodes is considered the network lifetime considering thethe connectivity-oriented IoT environment target of the network nodes can still perform their tasks smoothly. In our simulation-based network application scenario. evaluation case, the energy exhaustion of 2% of the nodes is considered the network lifetime considering the connectivity-oriented IoT1.environment target application scenario. Table Simulation parameters.

ParametersTable 1. Simulation parameters. Values Simulation area Parameters Simulation Simulation time area Simulation time Number of Nodes Number of Nodes Topology Topology InitialInitial Energy Energy PowerPower for transmission for transmission PowerPower for reception for reception Base station Location Base station Location Transmission Transmission rangerange Transmission Transmission limitlimit The radius of network The radius of network The traffic rate The traffic rate

100 m × 100 m Values 100 m × 100 70 m 70 m 500 500 random random 18,535 J (2 batteries) AA batteries) 18,535 J (2 AA 05110511 W W 05880588 W W (60, 60) (60, 60) 0.2 m0.2 m 5 5 Source and destination specific Source and destination specific 3 p/s 3 p/s

Considering a 2% dead nodes situation as a monitoring threshold, Figure 6a shows the network Considering a 2% dead nodes situation as a monitoring Figure 6a shows the densities network lifetime of various protocols (AODV, ER-RPL, REL and threshold, FFSC) under various node lifetime of various protocols (AODV, ER-RPL, REL andasFFSC) under various node densities According to Figure 6a, network lifetime is defined the number of rounds until 2% ofAccording the nodes to Figure 6a, network lifetime is defined as the number of rounds until 2% of the nodes die. In all die. In all simulation scenarios with 100, 200 and 500 nodes, FFSC outperforms other protocols, in simulation scenarios 100, and 200 REL and have 500 nodes, FFSC outperforms protocols, in terms of terms of lifetime, thenwith ER-RPL equal lifetimes, and AODVother performs the worst. Figure lifetime, andof REL have equal and AODV performs the Figure 6bmetrics shows 6b showsthen the ER-RPL percentage alive nodes aslifetimes, the simulation time increases. Twoworst. measurement the percentage of alive nodes as the simulation time increases. Two measurement metrics were used to were used to assess the energy efficiency: network lifetime and the saturation time of the IoT assess the energy efficiency: network lifetime and the saturation time of the IoT network. network.

(a)

(b) Figure 6. Cont.

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(c) Figure 6. Simulation based evaluation results: (a) network lifetime in number of communication Figure 6. Simulation based evaluation results: (a) network lifetime in number of communication rounds; (b) percentage of nodes alive during the entire simulation time; (c) packet delivery ratio in rounds; (b) percentage of nodes alive during the entire simulation time; (c) packet delivery ratio in resource constrained environments. resource constrained environments.

According to Figure 6b, the network lifetime is defined as the time spent by device nodes in the to Figure 6b, the network lifetime defined the time spent bysaturation device nodes IoTAccording network until the moment when only 1% ofisthe nodesasremain alive. The timeinisthe like IoT until the moment only 1% of thebegins nodes remain alive. The when saturation like the thenetwork network lifetime, exceptwhen its measurement from the point the time first is node dies. network lifetime, except allow its measurement begins theofpoint whenalgorithm the first node dies. Saturation Saturation time values one to evaluate thefrom ability a routing to effectively adjust to time values allow one routing to effectively adjust to of another another topology dueto toevaluate the deaththe of ability nodes. of In a 100 nodealgorithm density case, the saturation time AODV topology duewhich to the death nodes. a 100 nodetime density case, the timeER-RPL of AODV is 33 min, is 33 min, is lessofthan theInsaturation of FFSC (39saturation min), while and REL lay which is less than the saturation time of FFSC (39 min), while ER-RPL and REL lay between AODV between AODV and FFSC. The network lifetime of FFSC is 69 min, which is 9, 15 and 24 min more and FFSC. network FFSC is 69 min, which isin9,terms 15 and min more than REL, ER-RPL than REL,The ER-RPL andlifetime AODV,ofrespectively. Therefore, of24 network lifetime and saturation and AODV, in terms of networkby lifetime time,AODV FFSC isisthe time, FFSCrespectively. is the mostTherefore, suitable protocol, followed REL, and thensaturation ER-RPL, and themost least suitable protocol, followed by REL, then ER-RPL, and AODV is the least suitable. The reliability of suitable. The reliability of the existing routing protocols AODV, ER-RPL, REL and FFSC has been the existing routing protocols AODV, ER-RPL, and FFSC has beenasmeasured packet deliveryof measured using packet delivery ratio (PDR)REL which is described the ratiousing of the number ratio (PDR) which is described as the ratio of the number of successfully transmitted packets to the successfully transmitted packets to the total number of transmitted packets. Figure 6c illustrates the total of transmitted packets. 6c illustrates the PDR in the simulated withan PDRnumber in the simulated scenario with Figure 100 nodes for all packet transmissions, while scenario establishing 100 nodes for all 75% packet establishing interval 75%delivers to 100%.97% As can be interval from to transmissions, 100%. As canwhile be seen in Figurean6c, FFSC,from which packets seen in Figure 6c, FFSC, which delivers 97% packets successfully outperforms AODV (85%), ER-RPL successfully outperforms AODV (85%), ER-RPL (91%) and REL (95%), respectively and AODV (91%) and the RELleast (95%), respectively and AODV delivers the least packets successfully. delivers packets successfully. 3.3.Related RelatedLiterature LiteratureReviews Reviews Due to the unavailability of extensive surveys on energy efficiency in the IoT, in this section Due of extensive surveys ad-hoc on energy efficiency in the IoT,networks in this section reviews on to thethe IoTunavailability are revisited, focusing on vehicular networks, wireless sensor and reviews on the IoT are revisited, focusing on vehicular ad-hoc networks, wireless sensor various other areas. A survey on different protocols, technologies and applications related tonetworks the IoT and various other areas. A survey on different protocols, technologies andisapplications related toon the has been performed [18]. In [19] a comprehensive overview related to IoT presented, focusing IoT has been performed [18]. In [19] a comprehensive overview related to IoT is presented, focusing system architecture, enabled technologies, privacy and security issues, applications and integration on system architecture, enabled privacy and security issues, applications and between edge/fog computing and the technologies, IoT. From the perspective of context-aware technology, product integration and between edge/fog computingof and the IoT. From theand perspective ofsolutions context-aware development research, an examination a variety of innovative popular IoT was technology, product development and research, an examination of a variety of innovative and done [20]. A survey of prominent research related to the combined area of mobile phone computing popular IoT solutions was done [20]. A survey of prominent research related to the combined area and Internet of Things/Web of Things was provided in [21]. A review on technologies utilized in dataof mobileand phone computing Internet of Things/Web Things was provided in [21]. A review routing a framework ofand structural health monitoringoffor reliable and intelligent monitoring hason technologies utilized in data routing and a framework of structural health monitoring for reliable been presented in [22]. A review on existing middleware solutions for radio frequency identification, and intelligent monitoring has been presented in [22]. A review on existing middleware solutions for machine to machine communication, supervisory control and data acquisition in the IoT has been radio frequency identification, machine to machine communication, supervisory control and data presented in [23]. Various applications related to economic analysis and pricing models, while acquisitionadaptive in the IoT has been in and [23].wireless Variouscommunication applications related economic analysis developing protocols for presented data storage in IoTto environment, have and pricing models, while developing adaptive protocols for data storage and wireless been reviewed [24]. A survey about context awareness in IoT, while addressing various techniques, communication IoT environment, have been reviewed [24]. A survey about context models, methods,infunctionalities, middleware solutions and applications related to it isawareness presentedin IoT, while addressing various techniques, models, methods, functionalities, middleware solutions and applications related to it is presented in [25]. The architectures and frameworks of cognitive radio-based IoT systems are surveyed in [26], while highlighting open issues, research challenges

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in [25]. The architectures and frameworks of cognitive radio-based IoT systems are surveyed in [26], while highlightingrelated open issues, challenges andinapplications relatedenvironments to it. The research and applications to it. research The research efforts IoT-based smart haveefforts been in IoT-based environments been surveyed on the basis of network types, local wireless surveyed onsmart the basis of networkhave types, local area wireless standards, technologies andarea human life standards, technologies human life [27]. Theisframework of Vehicles ismotivation, presented [27]. The framework of and the Internet of Vehicles presented of in the [3], Internet while emphasizing in while architecture emphasizingwith motivation, five layered withmodel, functionalities of and eachfuture layer, five[3], layered functionalities of eacharchitecture layer, network challenges network model, and future aspects inside the on IoT. It presents a network model based aspects inside thechallenges IoT. It presents a network model based three elements: client, connection and on threeAelements: client, connection and cloud. A survey on introducing machine to machine systems, while cloud. survey on machine to machine systems, while their architecture and introducing their architecture and communication networks them basedcontext, on the communication networks and categorizing them based on the and typescategorizing of machine to machine types of machine machine context, task andRecent objective has been [28]. Recentsystems solutions for task and objectivetohas been performed [28]. solutions forperformed machine to machine have machine to surveyed. machine systems have been also surveyed. To support machine to in machine communications been also To support machine to machine communications the IoT environment, in the IoTMedium environment, existing Medium Access (MAC) protocols havewhile been including surveyed, existing Access Control (MAC) layerControl protocols havelayer been surveyed, while including technical challenges,and requirements and existing related them [29]. based A survey technical challenges, requirements existing work related work to them [29].to A survey on based on integration of the cloud with IoT was performed, considering challenges, research issues and integration of the cloud with IoT was performed, considering challenges, research issues applications InIn order to provide integration between smart homes and applications related relatedtototheir theirintegration integration[30]. [30]. order to provide integration between smart homes cloud-centric IoT efficiently, a framework based on various components of the proposed [31]. and cloud-centric IoT efficiently, a framework based on various components ofIoT theisIoT is proposed A smart homehome management model has been identified for this with its challenges. [31]. A smart management model has been identified forframework this framework with its challenges. 4. Energy Efficient Efficient Techniques 4. Energy Techniquesin inthe theIoT IoT In In this this section, section, energy energy efficiency efficiency techniques techniques in in the the IoT IoT are are qualitatively qualitatively reviewed reviewed based based on on the the taxonomy 7. The proposed techniques have been based on five different taxonomy shown shownininFigure Figure 7. The proposed techniques have categorized been categorized based on five layers of layers the IoT These five layers are named SL, LPSL, NCL,NCL, CPS CPS and and AL. different of energy the IoT architecture. energy architecture. These five layers are named SL, LPSL, The existing techniques at the sensing layer, are further classified in three categories: energy efficient AL. The existing techniques at the sensing layer, are further classified in three categories: energy sleep/wakeup, modulation and SoT-based techniques.techniques. Energy harvesting and cognitive efficient sleep/wakeup, modulation and SoT-based Energy harvesting andratio-based cognitive techniques are two more categorizations at the LPSL layer. The existing techniques at the NCL layer are ratio-based techniques are two more categorizations at the LPSL layer. The existing techniques at the also in three categories, as energy efficient scheduling, routing scheduling, and communication NCLcategorized layer are also categorized in named three categories, named as energy efficient routing techniques. The energy-efficient techniques at the CPS layer have two classifications, named as virtual and communication techniques. The energy-efficient techniques at the CPS layer have two machine optimization and Lyapunov optimization. classifications, named as virtual machine optimization and Lyapunov optimization. Energy efficiency in IoT

Sensing

Sleep/wakeup technique TQEE [32], 2014 EESACS[33], 2015

Modulation technique

Local Processing/ Storage SoT based technique

Network/ Communication

Scheduling

EQSA[40], 2016 SOT-EENGNM[35], 2016 EECATS[41], 2017 EAWCM [34], 2016 QEECS[42], 2017 EELTE [60], 2013 EECN [61], 2015

Energy harvesting ALLYS [36], 2017 ESPMS [37], 2017 ABSD [39], 2017

Cognitive radio based RECR-MAC [38], 2017

Routing

Cloud Processing and Storage Communication

Application based HTP[55], 2017 EEDPD[56], 2015 ODAP[57], 2017

EEPR[43], 2014 Virtual Machine Lyapunov REL[44], 2013 Optimization Optimization EECBR[45], 2015 ER-RPL[46], 2016 SVOP[53], 2017 DREAM[52], 2015 ERGID[47], 2016 POEE[54], 2017 EEH-CRAN[62], 2016 FFSC[48], 2016 MCvB[49], 2017 EC-CENTRIC[50], 2017 FBP[51], 2014

Figure efficient techniques techniques in in IoT. IoT. Figure 7. 7. Taxonomy Taxonomy of of energy energy efficient

Table 2 lists the nomenclature used in the equations that appear in the sections that follow. Table 2 lists the nomenclature used in the equations that appear in the sections that follow.

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Table 2. Nomenclature. Notation

Description

cn qm rn ∂m βm r (cn , qm)

Sensing capability of sensor n Desired QoI of task m Sensory radius Variance for sensor n Accuracy requirement of task m Sensor-task relevancy function

E si , m

Maximum residual energy of all possible subset of m activated sensors

vj Eb R Etot (t) ∂ h K (t) i p Eh = k

Normalized residual energy of sensor s j Energy per information bit System data rate Total energy utilization of the network at time t Actual coverage area Active sensors quantity at time t Probability of harvesting k energy units during T time slots of a frame

Eh

Amount of harvested energy of a node during a frame

h i E Eh

Average harvested energy

E Ecir

Energy consumption by sensor node Energy consumed to operate electrical circuit in the sensor node

CSi

Dom Relj E awake Pout pout th Li p Ei Emax Eth f i,s f i,d rij T xy T M−sink Dnt Dr0 r0 EX r0 E  

δ Ci   δ Dj   F ak , β

Relative dominance of candidate service Total energy spent in awake state Outage probability Maximum outage probability threshold Lifetime of a node Forwarding probability of a node Residual energy of node i Maximum residual energy of node i Energy threshold Traffic generation rate at node Traffic arrival rate at node Traffic departure rate from node i to node j Delay between two node x and y Iterative delay Expected delay of node Total delay of entire network Energy consumption of a node X distance away from the sink Total energy consumption in entire network Energy cost of a component Ci Energy consumption of a device Lagrangian function of optimization problem

Re2e L  W lki , nik   U lki , nik

End to end reliability Lifetime of whole network under HTP technique

z

Conflict parameter

Welfare of every user Utility function of user

4.1. Energy Efficient Techniques at Sensing Layer In this section, energy efficient techniques at the sensing layer have been described in three categories, namely energy efficient sleep wakeup, modulation and Self Organized Things (SOT) based techniques. 4.1.1. Energy Efficient Sleep/Wakeup Techniques Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments (TQEE) [32] has been proposed to manage long term energy consumption under appropriate QoI constraints in diverse applications like environmental monitoring, etc. In this technique, first a novel approach of QoI-aware sensor-to-task relevancy is introduced considering the sensing capabilities of sensors. Then, after

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proposing technique of critical coverage set of acoverage given task thetask sensors, dynamic decisions Then,the after proposing the technique of critical setto ofselect a given to select the sensors, for energy consumption optimization are made instantly (Figure 8). dynamic decisions for energy consumption optimization are made instantly (Figure 8). Tasks Desired QoI

Task Model

Information Fusion Algorithm Sensor to tasks Relevancy

EMS

Critical Covering Sets

Energy management Optimization decisions

Sensing Capability Sensors

Status

Figure 8. Flow of an energy management framework.

Figure 8. Flow of an energy management framework.

For this model sensor-task relevancy function is formulated as:

For this model sensor-task relevancy function is formulated as: 

𝑟(𝑐 𝑛 , 𝑞𝑚 ) = 1{𝑑𝑖𝑠(𝑋 𝑛 , 𝑌 𝑚 ) ≤ 𝑟 𝑛 } ∙ min

r (cn , qm ) = 1{dis( X n , Y m ) ≤ r n } · min

2𝛽𝑚

2βm

 

 𝜕 𝑚m ) ( {𝑄 Q √√∂𝑦y𝑛n }

(1)

(1)

where 𝑐 𝑛 , 𝑞𝑚 , 𝑟 𝑛 , 𝑦 𝑛 , 𝜕 𝑚 and 𝛽𝑚 are the sensing capability of sensor n, desired QoI of task m,

where sensory cn , qm , radius, r n , yn ,variance ∂m andfor βmsensor are the sensing capability of sensor n, m, desired QoI of task m, sensory n and accuracy requirement of task respectively. radius, variance for sensor n and accuracy requirement of task m, respectively. Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Energy for Water Distribution NetworksinBased on Compressive SensingEfficient (EESACS)Sensor [33] hasActivation been suggested to optimize energy consumption IoT applications like water distribution monitoring, intelligenttotransport systems, cities, etc.,inbyIoT activating only a like Sensing (EESACS) [33] has been suggested optimize energysmart consumption applications subset ofmonitoring, sensors to sense and transmit in every timeslot, usingcities, a scheduling based water small distribution intelligent transport systems, smart etc., byapproach activating only a on compressive sensing. Further solutions in based ontimeslot, dynamic using programming are suggested, after small subset of sensors to sense and transmit every a scheduling approach based decomposing the scheduling problem into sub-problems. If sensor 𝑠 𝑖 is chosen as activated m are on compressive sensing. Further solutions based 𝑖+1 on dynamic programming are suggested, after the additional activated sensors out of sensors 𝑠 to 𝑠 𝑁 where 𝑠 𝑖 and m additional sensors are decomposing the scheduling problem into sub-problems. If sensor si is chosen as activated m are the connected, then the maximum residual i+ energy of all possible subsets of m activated sensors is additional activated sensors out of sensors s 1 to s N where si and m additional sensors are connected, formulated as: 𝑗 }activated then the maximum residual energy of allmax possible of𝑣m is formulated as: {𝐸(𝑠 𝑗 ,subsets 𝑚−1)+ 𝑖𝑓 𝐾 ′ 𝑖 sensors ≠ ∅, 

𝐸(𝑠 𝑖 , 𝑚) = {𝑠𝑗 ∈𝐾′_(𝑠𝑖 )  −∞  

max

E

−(𝑠 )

sj,

m−1




+ vj



otherwise 0

(2)

i f K−(si ) 6= ∅,

′ i ) s j ∈K 0 _(senergy where 𝑣 𝑗 is the of sensor 𝑠 𝑗 and 𝐾−(𝑠 E normalized si , m = residual 𝑖 ) is the set of sensors in the DNS (2)

−∞ of the connection between otherwise of 𝑠 𝑖 except sink node 𝑠 𝑟 . Analysis network lifetime and energy consumption has not been done through the proposed technique, and this technique was not tested 0 where in v j various is the normalized residual ofand sensor s j and K−( is the set of sensors in the DNS of si IoT applications such energy as tunnel railway monitoring. si ) 

except sink node sr . Analysis of the connection between network lifetime and energy consumption Energy Techniques has not4.1.2. been done Efficient throughModulation the proposed technique, and this technique was not tested in various IoT applications such as tunnel and railway monitoring. Energy-Autonomous Wireless Communication for Millimeter-Scale Internet-of-Things Sensor Nodes (EAWCM) [34] has been suggested to target order of magnitude enhancements in energy

4.1.2. Energy Efficient Modulation Techniques efficiency while comprehensively analyzing wireless communication systems. This analysis jointly optimizes different parameters: modulation scheme, node dimension, carrier frequency,

Energy-Autonomous Wireless Communication for Millimeter-Scale Internet-of-Things Sensor Rf/digital/analog circuit specifications, synchronization protocol and a miniaturized 3-D antenna for Nodes (EAWCM) [34] has been suggested to target order of magnitude enhancements in energy energy-based ultra-small nodes. In the millimeter-scale node transmission case, if 𝑃𝑐𝑘𝑡 is the efficiency while comprehensively analyzing wireless communication systems. This analysis constant power utilization of the power oscillator circuit, then the energy per information bit can jointly be optimizes different expressed as: parameters: modulation scheme, node dimension, carrier frequency, Rf/digital/analog 𝑐𝑘𝑡 a miniaturized circuit specifications, synchronization protocol 3-D antenna for energy-based 𝑃and 𝐾 × 𝑇 𝑝𝑢𝑙𝑠𝑒 𝐸𝑏 = (3) ⌈𝐶 𝑟 ⌉ case, if Pckt is the constant power utilization ultra-small nodes. In the millimeter-scale node transmission 𝑟 of the and power oscillator then the energy perparameters: information bit can while using circuit, three modulation-coding 𝑇 𝑝𝑢𝑙𝑠𝑒 andbe𝐶expressed , system as: data rate is formulated as:

Eb =

Pckt K × T pulse dCr e

(3)

and while using three modulation-coding parameters: T pulse and Cr , system data rate is formulated as:

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dC𝑟r e R= ⌈𝐶 ⌉ idle charge + (K − 1) T𝑖𝑑𝑙𝑒 +T 𝑅 =MT pulse 𝑝𝑢𝑙𝑠𝑒 𝑐ℎ𝑎𝑟𝑔𝑒

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(4)

(4)

+ (𝐾 − 1)𝑇 +𝑇 𝑟⌉ ⌈𝐶 4.1.3. Energy Efficient Self-Organized (SOT)-Based Techniques 𝑅 = Things 𝑝𝑢𝑙𝑠𝑒 + (𝐾 − 1)𝑇 𝑖𝑑𝑙𝑒 + 𝑇 𝑐ℎ𝑎𝑟𝑔𝑒 𝑀𝑇 4.1.3. Energy Efficient Self-Organized Things (SOT)-Based Techniques 𝑀𝑇

(4)

An energy-efficient next generation network management (SOT-EENGNM) [35] has been An energy-efficient next generation network management (SOT-EENGNM) [35] has been proposed to minimize the total energyThings consumption of things and to stabilize the whole coverage 4.1.3. Energy Efficient Self-Organized (SOT)-Based Techniques proposed to minimize the total energy consumption of things and to stabilize the whole coverage area by using an optimization procedure. In this technique, the next generation self-configuration, area An by using an optimization this technique, the next generation self-configuration, energy-efficient next procedure. generation In network management (SOT-EENGNM) [35] has been self-healing andand self-optimization procedure of network structure of long self-healing self-optimization procedure of self-organizing self-organizing structure of long termterm proposed to minimize the total energy consumption of things andnetwork to stabilize the whole coverage evaluation systems is redefined to overcome human dependency in SOT (Figure 9). evaluation systems is redefinedprocedure. to overcome dependency in SOT (Figure 9). area by using an optimization In human this technique, the next generation self-configuration,

TBD6 TBD6

self-healing and self-optimization procedure of self-organizing network structure of long term Proposedhuman energy efficient SOT framework evaluation systems is redefined to overcome dependency in SOT (Figure 9). Locations Self-configuration Proposed energy efficient SOT framework Update Old states states (Active,passive, Locations Self-healing Self-optimization Self-configuration Update sleep) Old states states Controller (Active,passive, Self-healing Self-optimization sleep) PSD2 Controller

TBD 5

PSD2

PSD1 TBD4 PSD1

TBD3

TBD2 TBD1

TBD3 TBD2 TBD1 TBD TBD4 Figure9.9.Network Network topology topology using 5 Figure usingSOT. SOT.

The spatial distribution of devices determine active things’ Figure 9.to Network topology using SOT.distribution in 2D space, and the

The spatial of distribution of devices determine things’ in 2D space, and the intersection device’s coverage areasto using a conflictactive parameter are distribution also derived analytically in the 𝑡𝑜𝑡 (𝑡) a conflict parameter are also derived analytically in the intersection of device’s coverage areas using self-management process of SOT. If 𝐸 is the total energy utilization of the network at time t, The spatial distribution of devices to determine active things’ distribution in 2D space, and the tot 𝐾(𝑡) is the active sensors quantity at time t, 𝑟 is coverage radius and 𝑧 is conflict parameter, then self-management process of SOT. If E t is the total energy utilization of the network at time t, ( ) intersection of device’s coverage areas using a conflict parameter are also derived analytically in the K (t) 𝑡𝑜𝑡 (𝑡) actual 𝜕 is formulated process ofatSOT. is the total energy utilization of theparameter, network at then time t, is theself-management activecoverage sensorsarea quantity timeIf t,𝐸as: r is coverage radius and z is conflict actual 2 𝐾(𝑡) area is the∂ active sensors quantity at time t,𝑧 𝑟× is coverage radius and 𝑧 is conflict parameter, then coverage is formulated as: 𝐾(𝑡) × 𝜋 × 𝑟 (5) actual coverage area 𝜕 is formulated as:𝜕 =z × K (𝐸t𝑡𝑜𝑡 ) ×(𝑡)π × r2 (5) ∂= Eattotparticular (t𝜋 ) × 𝑟 2 time t w.r.t. actual coverage area (𝜕), 𝑧 × 𝐾(𝑡) × Figure 10a illustrates the energy utilization 𝜕=

(5)

𝐸 (𝑡) while keeping variousthe coverage (𝑟) according to Equation while Figure 10b shows howwhile Figure 10a illustrates energyradius utilization at particular time t (5), w.r.t. actual coverage area (∂), 𝑡𝑜𝑡 (𝑡) when 𝐸 varies the number of active sensor nodes varies. Summaries of related literatures on Figure 10a illustrates the energy utilization at particular time t w.r.t. actual coverage area (𝜕), keeping various coverage radius (r) according to Equation (5), while Figure 10b shows how when Etot (t) energy efficient techniques at the sensing layer are given in Tables 4 and 5, which comprises while keeping various coverage radius (𝑟) according to Equation (5), while Figure 10b shows how varies the number of active sensor nodes varies. Summaries of related literatures on energy efficient characteristics/protocol, issues, contributions, simulation tools used, literatures metrics and (𝑡) sensing when 𝐸at𝑡𝑜𝑡the varies the number of active sensortechniques, nodes Summaries of characteristics/protocol, related on techniques layer are given in Tables 4 and varies. 5, which comprises researchefficient limitations of each paper energy techniques at thereviewed. sensing layer are given in Tables 4 and 5, which comprises issues, contributions, techniques, simulation tools used, metrics and research limitations of each characteristics/protocol, issues, contributions, techniques, simulation tools used, metrics and paper reviewed. 𝑡𝑜𝑡

research limitations of each paper reviewed.

(a)

(b)

Figure 10. Representation of Equation (5): (a) total energy utilization via actual coverage area; (b) (a)via number of active sensor nodes. (b) total energy utilization Figure 10. Representation of Equation (5): (a) total energy utilization via actual coverage area; (b)

Figure 10. Representation of Equation (5): (a) total energy utilization via actual coverage area; (b) total total energy utilization via number of active sensor nodes. energy utilization via number of active sensor nodes.

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Table 3 presents the major abbreviations used in particular in our comparison tables. Table 3. Abbreviations used in comparison tables. Abbreviation

Description

Abbreviation

Description

CL CA DL EC EE DB CS NL MAC CPL DC ND ACA DPR QoS CP QoI NDIB EUS NoM CU GL SOT

Cross-layer Context aware Delay Energy consumption Energy efficiency Durability Compressive sensing Network lifetime Medium access control Composite lifetime Duty cycle Network distortion Actual coverage area Droped packet ratio Quality of service Conflict parameter Quality of information Number of delivered information bits Energy used per server Number of migrations CPU utilization Grid loading Self-Organized Things

PCV PDR CLT ETX BU LQ EH OP RD OPM ST THP L-Ratio PLR T-Ratio CO HC PD AR PC PAR

Photovoltaic cell voltage Packet delivery ratio Clustering Expected transmission count Bandwidth Usage Link quality Energy harvesting Outage probability Relative dominance Optimality Selection time Throughput Largest energy cost ratio Packet loss rate Total saved energy cost ratio Control overhead Hop count Processing density Acceptance rate Power consumption Peak to average ratio

GV

Grid variance

Tables 4 and 5 which shows analysis of energy efficient techniques at LPSL. In Table 5, the characteristics of each paper reviewed are categorized into three groups, including protocol/author, approach, and metrics. The protocol/author column comprises abbreviation of techniques and reference. The approach column is categorized into five classes, namely QoI, CS, CL, DC and SOT. The last column represents metrics including DL, EE, DR, SNR, NL, CP, ACA, and DB. Considering Table 5, the approaches in column four demonstrate that more contributions have been made by duty cycle-based techniques. Further, the metrics column shows that EE is the most considered metric, followed by DR, NL and then ACA for measuring performance of energy efficient techniques at sensing layer.

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Table 4. Summary of energy efficient techniques at sensing layer. Characteristics

Issues

Protocols

Techniques

Contributions

Simulators

Metrics

Limitations Limited to static environment

TQEE

Energy and data quality management

Energy efficient and QoI based solution

QoI-aware sensor-to-task relevancy using duty cycle

Matlab

-DL -QoI -EE

EESAC

Problem of more energy consumption and less robustness because of pipeline leakages

Energy efficient and Robust solution

Compressive sensing based scheduling approach

EPANET

-NL -EE

Not applicable to applications like monitoring of tunnels and railways

EAWCM

Energy optimization because of unique system constraint by ultra-small system dimension

Energy optimized solution.

Cross layer system level optimization approach

–

-EE -DR -PER -SNR

Error correction has not been considered.

SOT-EENGNM

More human-device interaction and Energy efficiency

Energy optimized, Durable and self-managed solution

Self-Organized Things based technique

–

-ACA -CP -EE -DR

Parameters such as Delay, Link stability have not been considered.

Table 5. Comparative assessment of energy efficient techniques at the sensing layer. Approaches

Protocol/Author TQEE [32] EESAC [33] EAWCM [34] SOT-EENGNM [35]

QoI √

CS

Metrics

CL

DC √

√

√

SoT

√ √

DL √

EE √ √ √ √

DR

SNR

√ √

√

NL

CP

√

ACA

PER

DB

√ √

√

√

√

√

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4.2.Energy EnergyEfficient EfficientTechniques TechniquesatatLocal LocalProcessing Processingand andStorage StorageLayer Layer 4.2. Inthis thissection, section, energy energyefficient efficienttechniques techniques at atthe thelocal localprocessing processingand andstorage storagelayer layerhave havebeen been In describedin intwo twocategories, categories,namely namelyenergy energyharvesting harvestingand andcognitive cognitiveradio-based radio-basedtechniques. techniques. described 4.2.1.Energy EnergyHarvesting HarvestingTechniques Techniques 4.2.1. AllYou Youcan canSend Sendfor forEnergy Energy Harvesting Networks (ALLYS) been suggested to adjust All Harvesting Networks (ALLYS) [36][36] hashas been suggested to adjust the the transmission probability of nodes the residual energy of individual nodes and the transmission probability of nodes based based on the on residual energy of individual nodes and the network networkin capacity in distributed energy harvesting WSNs-enabled IoT. Ina ALLYS, frame of with fixed capacity distributed energy harvesting WSNs-enabled IoT. In ALLYS, frame ofafixed size size with adjustable slot transmission probability used tothe control the channel access sink while nodes adjustable slot transmission probability is used toiscontrol channel access by sinkby nodes while broadcasting both frame size and transmission reduce the broadcasting both frame size and transmission probability probability to reduce thetocollisions. Thecollisions. probabilityThe of probabilityk of harvesting k energy during time slots of aformulated frame can as: be formulated as: harvesting energy units during T units time slots of aTframe can be ! 𝑒ℎ, 𝑠𝑢𝑐 𝑘 h 𝑝[𝐸 ℎ i= 𝑘] = T(𝑇)   T −k (6) (𝑝 eh, suc)(1 k  − 𝑝 𝑒ℎ, 𝑠𝑢𝑐 )𝑇−𝑘 h 𝑘 p E =k = p 1 − peh, suc (6) k ℎ where, 𝐸 is the amount of harvested energy of a node during a frame, then average harvested energy is given as: where, Eh is the amount of harvested energy of a node during a frame, then average harvested energy 𝑇 is given as: h iℎ ] = ∑ h ℎ = 𝑘] i = 𝑇𝑝𝑒ℎ,𝑠𝑢𝑐 T 𝑘𝑃[𝐸 (7) 𝐸[𝐸 h eh,suc E Eh = ∑ kP E = k = T p (7) 𝑘=0 k =0

Efficient Solar Power Management System for Self-Powered IoT Node (ESPMS) [37] is an Efficient Solar Power Management System for Self-Powered IoT Node (ESPMS) [37] is an architecture which achieves higher end-to-end energy efficiency while avoiding linear regulator and architecture which achieves higher end-to-end energy efficiency while avoiding linear regulator presenting a power convertor based on on-chip switched capacitor in IoT system. In the proposed and presenting a power convertor based on on-chip switched capacitor in IoT system. In the proposed architecture (Figure 11), there are two stages: DC-DC1 and DC-DC2 for DC-DC converters between architecture (Figure 11), there are two stages: DC-DC1 and DC-DC2 for DC-DC converters between harvesters and loads. harvesters and loads.

Cold start Block

S

Therml/Solar Harvester

Radio LDO Reg.4 AFE LDO Reg.3 µ-pro LDO Reg.2 LDO Reg.1 Sensors (DC-DC2)

D

Switching Regulator (DC-DC1)

Power Good Indicator

A

Rectifier

Battery Management(O V,UV,OT)

LO

MPPT Block

Li-ion

Vibrational/ Targeted RF

Figure 11. 11. Typical Typicalpower powermanagement managementarchitecture architecturein inIoT. IoT. Figure

Energy harvested roadside IEEE 802.15.4 wireless sensor networks for IoT applications [38] has Energy harvested roadside IEEE 802.15.4 wireless sensor networks for IoT applications [38] has been suggested while considering different traffic conditions based on the vehicles’ movement in been suggested while considering different traffic conditions based on the vehicles’ movement in WSNs-enabled IoT applications using energy harvesting techniques. To this perspective, an WSNs-enabled IoT applications using energy harvesting techniques. To this perspective, an adaptive adaptive energy efficient technique (ABSD) has been introduced that adapts MAC parameters energy efficient technique (ABSD) has been introduced that adapts MAC parameters related to related to IEEE 802.15.4 sensor nodes to provide queue occupancy level and to offer traffic levels. IEEE 802.15.4 sensor nodes to provide queue occupancy level and to offer traffic levels. EH-ABSD EH-ABSD (enhanced form of ABSD) is also introduced by integrating a new MAC parameter (enhanced form of ABSD) is also introduced by integrating a new MAC parameter (energy backoff) and (energy backoff) and energy harvesting technique, which improves battery lifetime (energy energy harvesting technique, which improves battery lifetime (energy efficiency), network throughput efficiency), network throughput and QoS values. Energy consumption by sensor node is formulated and QoS values. Energy consumption by sensor node is formulated as: as: ( ) h i X𝑋packet   𝑝𝑎𝑐𝑘𝑒𝑡 tr lstn BO SO sleep SO lstn 𝑡𝑟 rc 𝑟𝑐 𝑙𝑠𝑡𝑛 𝐵𝑂 𝑆𝑂 𝑠𝑙𝑒𝑒𝑝 𝑆𝑂 𝑙𝑠𝑡𝑛 ] (2 N {[(1 𝐼 (− + )𝑁)𝐼 𝐸 𝑐𝑖𝑟 (8) + Ecir (8) E = 𝐸 (= 1+ N+ + I+ − 2+ I ++ (22 −−22 )𝐼 I + 2+ 2𝐼 I} 𝑈 + U ) I 𝑁)𝐼 R𝑅 where 𝐸 𝑐𝑖𝑟 is energy consumed to operate electrical circuit in the sensor node, 𝐼𝑡𝑟 , 𝐼𝑟𝑐 , 𝐼𝑙𝑠𝑡𝑛 and cir where energy consumed operate circuit in the sensorlistening node, I tr , and I rc , I lstn and I sleep are 𝐼 𝑠𝑙𝑒𝑒𝑝 Eare iselectrical current toused in electrical transmission, receiving, sleeping mode, electrical current used in transmission, receiving, listening and sleeping mode, respectively. Figure 12 respectively. Figure 12 illustrates EHWSN architecture for moving vehicles. illustrates EHWSN architecture for moving vehicles.

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Data PZT PZT

WSN’s coverage

SINK

Sensor

Road

Energy storage

Lower power Transceiver 802.15.4

Power mgmt unit

Lower power micro controller

Figure Moving vehicles-based EHWSN architecture. Figure 12.12 Moving vehicles-based EHWSN architecture.

4.2.2. Energy Efficient Cognitive Radio Based Techniques 4.2.2. Energy Efficient Cognitive Radio Based Techniques The energy efficient wireless communication technique based on Cognitive Radio for Internet The energy efficient wireless communication technique based on Cognitive Radio for Internet of of Things [38] is a reliable, intelligent, and energy efficient with high throughput and smart cognitive Things [38] is a reliable, intelligent, and energy efficient with high throughput and smart cognitive radio-based technique by optimization of control frames and reduction of handshakes quantity radio-based technique by optimization of control frames and reduction of handshakes quantity across across the control and data channels. Reliable and Energy efficient Cognitive Radio Multi-channel the control and data channels. Reliable and Energy efficient Cognitive Radio Multi-channel Medium Medium Access Control protocol (RECR-MAC) is utilized in various wireless applications that are Access Control protocol (RECR-MAC) is utilized in various wireless applications that are not delaynot delay- sensitive such as playing card and chess gaming applications, social media applications sensitive such as playing card and chess gaming applications, social media applications like Twitter, like Twitter, and Facebook. Voice over Internet Protocol (VoIP) and mobile users utilize this and Facebook. Voice over Internet Protocol (VoIP) and mobile users utilize this technique for making technique for making free calls. free calls. Summaries of related literatures on energy efficient techniques at the local processing and Summaries of related literatures on energy efficient techniques at the local processing and storage layer is given in Tables 6 and 7, which comprise characteristics/protocol, issues, storage layer is given in Tables 6 and 7, which comprise characteristics/protocol, issues, contributions, contributions, techniques, simulation tools used, metrics and research limitations of each paper techniques, simulation tools used, metrics and research limitations of each paper reviewed. reviewed. In Table 7, the characteristics of each paper reviewed are categorized into three groups, including In Table 7, the characteristics of each paper reviewed are categorized into three groups, protocol/author, approach, and metrics. The protocol/author column comprises abbreviation including protocol/author, approach, and metrics. The protocol/author column comprises of techniques and references. The approach column is categorized into five namely, MAC, EH, abbreviation of techniques and references. The approach column is categorized into five namely, CR, DC and QoS. The last column represents metrics, including DL, EE, QoI, BU, NL, PCV, DR, MAC, EH, CR, DC and QoS. The last column represents metrics, including DL, EE, QoI, BU, NL, and THP. Considering Table 7, the approaches in columns three and four demonstrate that more PCV, DR, and THP. Considering Table 7, the approaches in columns three and four demonstrate that contributions have been made by MAC and energy harvesting based techniques, followed by duty more contributions have been made by MAC and energy harvesting based techniques, followed by cycle based technique. Meanwhile, very few contributions have been made towards CR and QoS duty cycle based technique. Meanwhile, very few contributions have been made towards CR and techniques. Further, the metrics column shows that EE is the most considered metric, followed by QoS techniques. Further, the metrics column shows that EE is the most considered metric, followed NL and THP, then DL for measuring performance of energy efficient techniques at local computing by NL and THP, then DL for measuring performance of energy efficient techniques at local layer. Tables 8 and 9 present analyses of energy efficient scheduling techniques.4.3. Energy Efficient computing layer. Tables 8 and 9 present analyses of energy efficient scheduling techniques.4.3. Techniques at Network/Communication Layer. Energy Efficient Techniques at Network/Communication Layer In this section, energy efficient techniques at the network/communication layer have been described In this section, energy efficient techniques at the network/communication layer have been in three categories, namely energy efficient scheduling, routing and communication techniques. described in three categories, namely energy efficient scheduling, routing and communication techniques.

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Table 6. Summary of energy efficient techniques at local processing and storage layer. Characteristics

Contributions

Techniques

Simulators

Metrics

Limitations

ALLYS

Wastage of energy in idle, listening, overhearing, collision and retransmissions

Limited battery solution

Energy harvesting and frame slotted aloha technique

Qualnet and VanetMobiSim

-DL -EE -THP

–

ESPMS

Energy limitation

Utilization of stored energy in regulation and insufficient energy to maintain load

Solar energy harvesting technique

0.18-µm CMOS, MOSFET

-EE -PCV

Parameters such as delay have not been considered

RECR-MAC

Energy consumption and scarcity of bandwidth

Reliable solution with high throughput.

Cognitive Radio Multi-channel MAC approach

NS-2 and SUMO EvalVid

-BU -THP -EE -DR

Not applicable in the applications those are delay sensitive.

ABSD

Excess energy consumption in receiving, transmitting, idle listening and sleeping

Energy efficient and QoS aware solution

MAC aware Energy harvesting approach

OPNET and MatLab

-EE -THP -QoS -DPR

Vital parameters such as PSNR has not been considered.

Issues

Protocols

Table 7. Comparative assessment of energy efficient techniques at local processing and storage layer. Approaches

Protocol/Author ALLYS [36] ESPMS [37] RECR-MAC [38] ABSD [39]

MAC √

√ √

EH √ √

CR

Metrics DC √

QoS

DL √

√ √

√

√

√

EE √ √ √ √

QoI

BU

NL √

√

√ √

PCV

DR

DPR

THP √

√ √ √

√ √

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Table 8. Summary of energy efficient scheduling techniques. Characteristics

Issues

Protocols

Techniques

Contributions

Energy and QoS aware solution

lexicographic optimization and QoS constraints relaxation approach

EQSA

Energy and QoS aware service selection

EECATS

Dynamic service quality requirement with variable quality information in multi hop communication

Energy and context aware solution

Energy efficient and context aware traffic scheduling

QEECS

Maintain tradeoff between energy consumption and QoS.

Energy efficient Qos aware solution for industrial applications

QPSO, TDMA scheduling, NSGA-2

Simulators

Metrics

Limitations

JVM, JRE 1.6

-EC -QoS -CPL -OPM -RD -ST

Limited to static environment

NS2 and Contiki OS

-EE -DL -DR -THP -CA

Overload and link quality have not been considered

MATLAB

-EE -NL -OP -QoS

Delay is not considered. Analysis of the clustering performance is not performed.

Table 9. Comparative assessment of energy efficient scheduling techniques. Approaches

Protocol/Author EQSA [40] EECATS [41] QEECS [42]

MAC

CLT

√ √

√

Metrics

CA

DC

√

√ √

QoS √ √ √

DL √ √

EE √ √ √

OPM √

RD √

NL √

OP

√

√

DR

√

CPL √

THP

√

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4.3. Energy Efficient Techniques at Network/Communication Layer 4.3.1. Energy Efficient Scheduling Techniques Energy-Centered and QoS-Aware Services Selection for Internet of Things (EQSA) [40] utilizes lexicographic optimization and QoS constraints relaxation approaches to preselect QoS-aware services required for the user’s level and the technique of relative dominance of services in the Pareto sense to minimize energy usage for a composite service in the IoT environment. The relative dominance of candidate services is dependent on QoS attributes, energy profile (EP) and user’s preferences and calculated as: CSij

Dom Rel = EP − Dom QoS

where ∑ Dom

(CSij , q)

q =1

CSij

QoS

∑ Dom

(CSij , q)

× Pre f q

(9)

q =1

× Pre f q is QoS-based dominance of the service CSij .

Energy Efficient

Context Aware Traffic Scheduling for IoT applications (EECATS) [41] has been proposed to realize energy-efficient resource allocation in Wi-Fi-based devices in heterogeneous traffic demand and distinct weighted quality classes-based multi-hop IoT infrastructures. In this technique, service quality requirements and a context priority-based optimization model by sub-gradient projection method is introduced. In the awake state, the total energy spent is formulated as: E

awake

tx

= P ×T

aw,l

tx

=P ×

n

∑



t,i

Ai,j 2 × n × T + 3 × T

sfs

+T

pf

+T

wait



! (10)

i =1,j=1

QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems (QEECS) [42] has been suggested to provide tradeoff between energy efficiency and QoS provisioning by formulating a multi objective optimization problem, while using network lifetime and outage probability as metric, respectively. This technique considers two phases for data transmission: setup phase and steady state phase. During the setup phase a combination of quantum-inspired particle swarm optimization and exhaustive search for selecting optimum cooperative coalition for cluster head (CH) and cooperative nodes (Coop) in capillary network of industrial IoT systems. In steady state phase, collection and transmission of data by all nodes is divided into three stages using TDMA scheduling, data collection, local broadcasting and long-haul cooperative transmission. The multi objective optimization problem is solved using improved non-dominated sorting genetic algorithm (NSGA-2). Finally, the combination of QPSO and NSGA-2 algorithms obtains the Pareto-optimal solution first. For QoS provisioning, formula for the outage probability is mentioned as: Pout = pout th − P

CH out, Coop

(11)

where pout th is the maximum outage probability threshold. Lifetime of a node is formulated as: Li =

Eres E

(12)

E is the energy consumption of a node, if pdc , plb and plh are power consumption in data collection, local broadcasting and local-haul cooperative transmission phase respectively, then E is described by a formula as: 1 1 1 × plb + × plh (13) E = × pdc + ∝ 2∝ 2∝ Figure 13 illustrates the system model of QEECS technique. A summary of related literatures on energy efficient scheduling techniques is given in Tables 8 and 9, which comprises characteristics/protocol, issues, contributions, techniques, simulation tools used, metrics and research limitations of each paper reviewed.

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Coop1

CN1

CN2

… … CNj

CH

... Coopi

Capillary Base Gateway Station Data Collection Local broadcasting

IoT Platform Long haul CMISO Cellular access

Figure 13. System model. Figure 13. System model.

In Table 9, the characteristics of each paper reviewed are categorized into three classes, In Table 9, the characteristics of each paper reviewed The are categorized into three classes,comprises including including protocol/author, approach, and metrics. protocol/author column protocol/author, approach,and and reference. metrics. The column comprises abbreviation of abbreviation of techniques The protocol/author approach column is categorized into five namely, techniques and reference. The approach column is categorized into five namely, MAC, CLT, CA, DC MAC, CLT, CA, DC and QoS. Last column represents metrics including DL, EE, OPM, RD, NL, OP, and COL QoS. Last represents metrics DL, EE,inOPM, RD,five NL,demonstrate OP, DR, COLthat andmore THP. DR, and column THP. Considering Table 9,including the approaches column Considering Table the approaches in column five demonstrate thatbymore contributions havebased been contributions have 9, been made by QoS based techniques, followed MAC and duty cycle made by QoS based techniques, followed by MAC and duty cycle based techniques. Meanwhile, very techniques. Meanwhile, very few contributions have been made towards clustering and context few contributions have been made towards clustering and context aware techniques. Further, metrics aware techniques. Further, metrics column shows that EE is the most considered metric, followed by column that EE is most considered metric,offollowed by NL and DL, thentechniques. THP for measuring NL and shows DL, then THP forthe measuring performance energy efficient scheduling performance of energy efficient scheduling techniques. 4.3.2. Energy Efficient Routing Techniques 4.3.2. Energy Efficient Routing Techniques Energy-efficient probabilistic routing (EEPR) [43] has been suggested to control the Energy-efficient probabilistic routing (EEPR) [43] has been suggested to control the transmission transmission of routing packets randomly using flooding technique while enhancing the network of routing packets randomly using flooding technique while enhancing the network lifetime and lifetime and minimizing the packet loss rate. EEPR utilizes two metrics: expected transmission count minimizing the packet loss rate. EEPR utilizes two metrics: expected transmission count and residual and residual energy of every node simultaneously in the context of typical ad-hoc on demand vector energy of every node simultaneously in the context of typical ad-hoc on demand vector protocol. protocol. While using the expected transmission count metric, EEPR composes the routing route While using the expected transmission count metric, EEPR composes the routing route with better with better link quality, while using the residual energy of every node as a routing metric makes it link quality, while using the residual energy of every node as a routing metric makes it possible the possible the utilization of energy more evenly for all the IoT nodes across the network. The utilization of energy more evenly for all the IoT nodes across the network. The transmission count transmission count metric is induced not by using the heuristic method but by using the bit error metric is induced not by using the heuristic method but by using the bit error rate based on the rate based on the path-loss model. The second routing matric used is residual energy which results path-loss model. The second routing matric used is residual energy which results in even use of it in even use of it among all the nodes (Figure 14). Then, the forwarding probability 𝑝 of a node among all the nodes (Figure 14). Then, the forwarding probability p of a node under the proposed under the proposed EEPR algorithm is determined as: EEPR algorithm is determined as: (𝐸𝑇𝑋

−𝐸𝑇𝑋

)

𝑚𝑎𝑥 (𝑖−1,𝑖) , 𝑝 = [𝑃𝑚𝑖𝑛 + 𝐸𝑖 𝐴[1 + ]]1/∝ 𝑚𝑎𝑥 ) ETX(i(1−𝐸𝑇𝑋 − ETX max −1,i ) (14) p = [ Pmin + Ei A[ 1 + 1 − 𝑃𝑚𝑖𝑛 ]]1/∝ , (1 − ETXmax ) 𝐴= (14) 2 × 𝐸𝑚𝑎𝑥 1 − Pmin A= 2 × Emaxresidual energy of node i, and 𝑃𝑚𝑖𝑛, and ∝ where 𝐸𝑖 and 𝐸𝑚𝑎𝑥 are residual energy and maximum are predefined minimum forwarding probability, and the weighted factor for variation of 𝑝 where Ei and Emax are residual energy and maximum residual energy of node i, and Pmin , and ∝ are respectively. The forwarding probability is better, with high residual energy of the node and low predefined minimum forwarding probability, and the weighted factor for variation of p respectively. ETX value of the link, but in case of far lower ETX value of link and less residual energy of the node, The forwarding probability is better, with high residual energy of the node and low ETX value of the the forwarding probability is low. link, but in case of far lower ETX value of link and less residual energy of the node, the forwarding probability is low.

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More energy More energy

× ×

T T ES ES U U Q Q RE RE

ST 1 UE T Q ,1 1 RE UES s X ET s,1 EQ X SR ETETXs,2 S ETX s,2 REQ UEST REQ UEST

2 2 Less energy Less energy

3 3

T ES U Q T RE UES Q RE

× ×

D D

4 4

Figure 14. EEPR algorithm. Figure 14. EEPR algorithm. Figure 14. EEPR algorithm.

EEPR algorithm results in slightly more route setup delay and slightly lesser routing success EEPR results in more route setup and slightly lesser success EEPR algorithm algorithm results in slightly slightlythe more route setup delay delay and been slightly lesser routing routing success probability. EEPR does not consider delay. Equation (14) has illustrated by Figure 15. probability. EEPR does not consider the delay. Equation (14) has been illustrated by Figure 15. probability. EEPRthat does notforwarding consider the delay. Equation has as been by Figure 15. Figure 15a shows how probability of a node(14) varies 𝐸𝑇𝑋illustrated varies in Equation (𝑖−1,𝑖) Figure 15a that how forwarding probability of a node varies as ETX varies in Equation (14), (i −1,i )and Figure 15ashows shows forwarding probability a node varies as 𝐸𝑇𝑋 varies inenergy Equation (14), while Figurethat 15b how shows the relation between of forwarding probability residual of (𝑖−1,𝑖) while Figure 15b shows the relation between forwarding probability and residual energy of node (14), shows the relation between forwarding probability and residual energy ofi. node while i. HereFigure 𝐸𝑚𝑎𝑥 15b has been considered as 100 joule to calculate 𝐴. Here has𝐸beenhas considered as 100 joule to calculate A. node E i.max Here been considered as 100 joule to calculate 𝐴. 𝑚𝑎𝑥

(a) (a)

(b) (b)

Figure 15. Representation of equation 14: (a) forwarding probability of a node via 𝐸𝑇𝑋(𝑖−1,𝑖) ; (b) forwarding probability via residual energy i. Figure 15. Representation of equation 14: of (a)node forwarding probability of a node via 𝐸𝑇𝑋(𝑖−1,𝑖) ; (b) Figure 15. Representation of equation 14: (a) forwarding probability of a node via ETX(i−1,i) ; forwarding probability via residual energy of node i. (b) forwarding probability via residual energy of node i.

Routing protocol based on Energy and Link quality (REL) [44] is based on optimization of the based on Energy and Link quality (REL) [44] is based on optimization of and the routeRouting selectionprotocol mechanism while utilizing link quality estimation, energy evaluation, hop count based on itEnergy and Link quality (REL) [44] is based on optimization ofand the route selectionprotocol mechanism while utilizing link quality estimation, energy evaluation, hop count load Routing balancing mechanisms, so increases the system's reliability and prevents premature death of route selection mechanism while utilizing link quality estimation, energy evaluation, hop count and load balancing mechanisms, so residual it increases the system's reliability prevents premature death to of nodes. REL uses three metrics: energy, link quality based and on weak links, and hop count load balancing mechanisms, sopaths. it increases system's reliability prevents premature of nodes. REL uses three metrics: residual link quality basedand onthere weak and hop death count to minimize long and inefficient Forenergy, thethe route selection process, arelinks, two threshold values: nodes. REL uses metrics: residual energy, link quality based on weak links, and route. hopvalues: count minimize long and inefficient paths. For used the route selection process, there are two threshold first threshold is three 𝐸𝑡ℎ (energy threshold) in load balancing technique and finding The max _𝑎𝑙𝑙𝑜𝑤 to minimize long and inefficient paths. For the route selection process, there are two threshold first threshold is 𝐸𝑡ℎ threshold) used in load balancing technique and finding route. The second threshold is(energy 𝐻𝐶𝑑𝑖𝑓𝑓𝑒𝑟 (maximum-hop-count-difference) which calculates the values: first threshold Eth (energy threshold) usedThe in load technique andcalculates finding second threshold is ofis 𝐻𝐶𝑑𝑖𝑓𝑓𝑒𝑟 (maximum-hop-count-difference) which the maximum difference hops to max the _𝑎𝑙𝑙𝑜𝑤 present route. primebalancing technique of fault tolerance androute. load The second threshold HCditof fthe ermax_allow (maximum-hop-count-difference) which calculates the maximum difference ofisapplications hops present route.ofThe primeroutes technique of fault tolerance and load balancing in WSNs/IoT is the usage multiple to control traffic across various maximum difference of applications hops to thethroughput present route. prime technique of fault tolerance and load balancing WSNs/IoT is the usage ofThe multiple routes to control traffic across various paths. By in using multiple routes, and data reliability can be increased by balancing balancing in WSNs/IoT applications is the usage of multiple routes to control traffic across various paths. usingand multiple routes, throughput and data be increased balancing utilizedBy energy bandwidth aggregation. In REL, routereliability selection iscan processed on the by basis of three paths. By using multiple routes, throughput and data reliability can be increased by balancing utilized utilized and bandwidth aggregation. In REL, route on the basis of three rules. Ifenergy 𝑅𝑎 is current route and 𝑅𝑏 is alternative routeselection and 𝐸𝑡ℎ isisprocessed energy threshold then both energy and bandwidth aggregation. In REL, route selection is processed on the basis of three rules. is currentbased routeonand and 𝐸𝑡ℎ is energy then rules. both routes Ifare𝑅𝑎compared the 𝑅metrics and thereroute is a switch from 𝑅𝑎 to 𝑅threshold 𝑏 is alternative 𝑏 if the comparison If R a isare current route and Rbelow: is the alternative routethere and is Etha is energy threshold bothcomparison routes are bon routes based metrics and switch from 𝑅𝑎 to 𝑅then if the satisfied thecompared rules described 𝑏 compared based the metrics and there is a switch from R a to Rb if the comparison satisfied the rules satisfied the rulesondescribed below: max _𝑎𝑙𝑙𝑜𝑤 1. 𝑅𝑎 energy and 𝑅𝑎 weak links = described below:= 𝑅𝑏 energy, 𝑅𝑎 hopcount = 𝑅𝑏 hopcount + 𝐻𝐶𝑑𝑖𝑓𝑓𝑒𝑟 1. 𝑅𝑎𝑏 weak energylinks = 𝑅𝑏 energy, 𝑅𝑎 hopcount = 𝑅𝑏 hopcount + 𝐻𝐶𝑑𝑖𝑓𝑓𝑒𝑟 max _𝑎𝑙𝑙𝑜𝑤 and 𝑅𝑎 weak links = max _𝑎𝑙𝑙𝑜𝑤 weak 2. 𝑅R𝑏𝑎a energy energylinks energy, 𝐻𝐶𝑑𝑖𝑓𝑓𝑒𝑟 𝑅𝑎 weak 1. =weak links ≥ R+b 𝑏energy max _𝑎𝑙𝑙𝑜𝑤 3. 𝑅 energy, 𝑅𝑎 links energy≥ 𝑅≤𝑏 weak 𝑅𝑏 energy 𝐻𝐶𝑑𝑖𝑓𝑓𝑒𝑟 𝑅𝑎 weak links + 𝐸𝑡ℎ , 𝑅𝑎 hopcount > 𝑅𝑏 hopcount + weak links > 𝑅𝑏 and 𝑎 energy 𝐻𝐶𝑑𝑖𝑓𝑓𝑒𝑟 max _𝑎𝑙𝑙𝑜𝑤 and 𝑅𝑎 weak links ≥ 𝑅𝑏 weak links

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R a energy > Rb energy, R a energy ≤ Rb energy + Eth , R a hopcount > Rb hopcount + HCdi f f ermax_allow and R a weak links ≥ Rb weak links

REL selects a route with good link quality and efficient energy consumption, but has the disadvantage of less routing success probability and more route set-up delay. This protocol is applicable only for static environments. For energy saving the radio transmission technique can be used. Energy-Efficient Content-Based Routing (EECBR) [45] is a routing technique for publishing/ subscribing approaches that comprises routing based on context and content. The EECBR protocol tackles the routing problem while balancing the consumption of energy in efficient manner across an IoT network. First of all, a virtual topology is created, which can be a tree or the set of clusters depending upon the network’s topology. In this algorithm, the sensor which covers the maximum sets of sensors of interest that are located in the communication range, is selected as a leader from a set of sensors of interest for a particular event. Compared with many other protocols it schedules and decides sleep/awake sensors by monitoring a certain area by using some leaders. The virtual topology helps in organizing sensors and reducing their communication cost across the IoT network. While making decisions on the selection of the leaders, the contribution of every sensor is evaluated, which depends on the quantity of uncovered sensors that are interested in a particular event/uncovered leaders of (i − 1)th level located within the range of the sensors, and the residual energy of sensors. The sensor with the largest contribution is selected as a leader. Intended receivers are broadcasted by enabling it. An Energy-Efficient Region-Based RPL Routing Protocol for Low-Power and Lossy Networks (ER-RPL) [46] has been suggested to achieve energy-efficient point to point data delivery while considering reliability in machine to machine communication for static, low-power and lossy networks. ER-RPL utilizes only a subset of nodes from some regions, in place of all nodes, for finding optimal route in terms of efficient energy consumption and reliability. ER-RPL has been described in two stages: network initialization stage and route discovery stage. In the network initialization stage, the first coordinates of each reference node are computed, then based on reference node’s coordinate values and hop counts, every node estimates its distance to every reference node. By using a distributed self-regioning technique, the region of a node in the network is computed. In the route discovery stage, based on the region of the source node and destination node, route discovery is processed among the subset of nodes using region-based route discovery. Then, region-based route discovery is enhanced, by designing region to region routing without route discovery. Besides, the region to region route is further shortened by implementing a route adjustment on-the-fly algorithm and the dead zone problem is solved by an adaptive region selection algorithm. If f i,s is traffic generation rate at node i, f i,d is traffic arrival rate at destination node i, rij is traffic departure rate from node i to node j. Then, in ER-RPL, formula for point to point traffic model of node is mentioned below: f i,s + ∑ ∂ ji r ji − ∑ ∂ij rij pij = f i,d (15) j∈ NN (i )

j∈ NN (i )

where 0 ≤ r ji ≤ C ji , ∀i,j∈n , and i 6= j. If network running time is T, traffic generation rate at time t is f i,s,t , traffic arrival rate at destination node i f i,d,t and duration of traffic flow is dt, then traffic load at node i is formulated as: Z T 0

Z T 0

f i,s,t dti = Li0 + Li1 + . . . + LiN =

∑

Lij

(16)

∑

L ji

(17)

0≤ j ≤ N

f i,d,t dti = Li0 + Li1 + . . . + LiN =

0≤ j ≤ N

ER-RPL supports generic traffic patterns, network scalability and robustness in different wireless channel condition. It discovers energy efficient and reliable routing paths, but is not suitable in a

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dynamic environment. An Efficient Routing Protocol for Emergency Response Internet of Things (ERGID) [47] has been suggested on the basis of global information decision to enhance reliability, to minimize delay and to provide load balancing in the network. In ERGID, a delay iterative method is used for optimizing delay estimation and residual energy probability choice approach is utilized for balancing the network load. The delay iterative method is based on a Dijkstra algorithm for finding the shortest path. The delay between two nodes x and y is calculated as: T xy =

( T x − TS) 2

(18)

where T x and TS are the current time of node x and time stamp, respectively, and the iterative delay is calculated as: n o T M−sink = min T Mx + T x−sink (19) x ∈ RM

According to the residual energy probability choice approach, if E x is the residual energy of node x and CN is the set of nodes having a delay to the sink smaller than the residual time, then the forwarding probability for every candidate node is formulated as: Px =

Ex ∑ j∈CN E j , x ∈ CN

(20)

ERGID is limited to small-scale emergency response IoT due to its energy consumption, and not focused on large-scale applications. A summary of related literatures on energy efficient routing techniques is given in Tables 10 and 11, which comprises characteristics/protocol, issues, contributions, techniques, simulation tools used, metrics and research limitations of each paper reviewed. In Table 11, the characteristics of each paper reviewed are categorized into three classes including protocol/author, approach, and metrics. The protocol/author column comprises abbreviations of techniques and reference. The approach column is categorized into five groups namely, SR, PA, PSS, CA and QoS. The last column represents metrics including DL, EE, LQ, ETX, NL, HC, PLR, PDR and CO. Considering Table 11, the approaches in columns two and five demonstrate that more contributions have been made by PA and QoS based techniques, followed by SR, PSS and CA techniques. Further, the metrics column shows that EE is the most considered metric, followed by DL, then LQ, NL, HC and PDR for measuring performance of energy efficient routing techniques.

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Table 10. Summary of energy efficient routing techniques. Characteristics

Issues

Protocols EEPR

Techniques

Contributions

Network partitioning and Energy consumption in distributed network

Evenly consumption of energy by each node and enhanced network lifetime

Energy efficient probabilistic routing

Simulators

Metrics

Limitations

NS-2 on Linux fedora 13

-ETX -EE -LQ -NL

Delay is not considered. Routing success probability is less and route setup delay is high

OMNET++ with Castalia framework

-EE -LQ -HC -NL -PDR -DL

Not managed radio/transmissions to improve energy saving

OMNET++

-EE -NL

QoS parameter such as delay is not considered. Cross layer approach is not utilized

Not working in dynamic environment.

Limited to small-scale emergency response IoT for energy consumption

REL

Restriction of sensor nodes in terms of energy, processing and memory.

Evenly distribution of network resources, enhanced lifetime and QoS

Routing protocol based on energy and link quality.

EECBR

Energy consumption challenge in asynchronous communication

Enhanced lifetime and less energy consumption

Publish/subscribe scheme based on content based routing

ER-RPL

Energy efficiency, reliability, dead zone problem

Energy efficient, reliable and robust solution.

Distributed self-regioning technique

NS-3

-DL -PDR -EC -HC -CO

ERGID

Assurance of real time emergency response ability

Reliable data transmission and energy efficient emergency response in IoT

Emergency response IoT based on global information decision. REPC and DIM

NS-2

-DL -EE -PLR

Table 11. Comparative assessment of energy efficient routing techniques. Approaches

Protocol/Author SR EEPR [43] REL [44] EECBR [45] ER-RPL [46] ERGID [47]

√

PA √ √

√

Metrics

PSS

CA

√

√

QoS

DL

√

√

√ √

√ √

EE √ √ √ √ √

LQ √ √

ETX √

NL √ √

HC

PLR

√

PDR

CO

√

√

√ √

√

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4.3.3. Energy Efficient Communication Techniques An Energy Efficiency Communications Approach for Delay Minimizing in Internet of Things [48] optimizes end-to-end delay and network lifetime from a theoretical perspective and establishes the relationships between the minimum per-hop forwarding distance r0 , the transmission range r, the node density ρ and the duty cycle that affect the node energy and network delay as well as the network lifetime. Liu et al. suggested a technique called Fast data collection for the node Farthest away from the sink and Slow data collection for the node Closest to the Sink (FFSC) that adopts a direct forwarding routing technique for nodes located at a large distance from the sink after determining the availability of forwarding nodes. In the region near the sink, which has higher energy constraints, data is only forwarded after the selection of the optimal relay node to minimize energy consumption and enhance network lifetime. FFSC saves energy of node and minimizes delay using a duty cycle technique. If the number of forwarding set nodes for a node is n and duty length is t, then the duty cycle of every node is 1t , and the expected delay of nodes is calculated as: Dnt =

        1 in 1 n 1 ( t −1) n i 1 − 1 − 1 − + t − 1 1 − ( ) ∑ t t t i =0

t −2

(21)

Total delay of the entire network can be formulated as: D

r0

= 2π

Z R 0

&

X erX0

'

t −2



1 ∑ i 1− t i =0

inr0 " d



1 1− 1− t

 n r0 # d

r0 !  1 ( t −1) n d + ( t − 1) 1 − Xd X t



(22)

where d and X are the distance from source to sink, and the distance of particular node from the sink, r respectively. eX0 is expected reward of each hop. Energy consumption of a node X distance away from the sink is formulated as: r X X rx EX0 = PLPL t + PRX ∅rx (23) R + PT ∅T X , P X , P X and ∅rx are the power associated with low power listening operation of node, where PLPL R T R average power to transmit a packet, power used to receive a packet and amount of data loaded by node. rx Considering ∅rx T = ∅ R + 1, Therefore total energy consumption in the entire network is expressed as:

Er0 = 2π

Z R 0

r0 EX Xd X

(24)

Bacterial Relay for Energy Efficient Molecular Communications (MCvB) [49] has been proposed to deliver information while considering energy efficiency using bacterial mobile relays for molecular communication in the Internet-of-Nano-Things. This technique first derives the probability of end-to-end delivery using a propagation model of information pickup and information delivery stages after introducing system parameters. This provides an energy model while deriving an energy efficiency expression considering all communication stages. These communication stages are: encoding, encapsulation, propagation, decapsulation and decoding (Figure 16). If Etot is energy consumption for whole communication mechanism including these all stages as:   Etot = N × E p + M × Eb + K × Em × T + J × Ed + Ee

(25)

where N × E p is the total energy consumption in encoding stages, having N plasmids, M × Eb is m energy consumption in encapsulation   stage having M plasmids, K × E × T energy consumption in

propagation stages and J × Ed + Ee is the energy consumption in the decapsulation and decoding stage. This technique has a drawback in the case of delay tolerant and short distance communication. An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design (EC-CENTRIC) [50] has been proposed to provide an optimization framework that analyzes basic functionalities of IoT systems in terms of energy and context. The process of this framework is divided in three technical aspects: providing a balance between communication tasks and processing

where 𝑁 × 𝐸 𝑝 is the total energy consumption in encoding stages, having 𝑁 plasmids, 𝑀 × 𝐸 𝑏 is energy consumption in encapsulation stage having 𝑀 plasmids, 𝐾 × 𝐸 𝑚 × 𝑇 energy consumption propagation stages and 𝐽 × (𝐸 𝑑 + 𝐸 𝑒 ) is the energy consumption in the decapsulation and Energiesin2017, 10, 2073 26 of 41 decoding stage. This technique has a drawback in the case of delay tolerant and short distance communication. Energies 2017, 10, 2073 26 of 40 𝑡𝑜𝑡 𝑝 𝑏 𝑚 𝑑 𝑒

𝐸

= 𝑁 × 𝐸 + 𝑀 × 𝐸 + 𝐾 × 𝐸 × 𝑇 + 𝐽 × (𝐸 + 𝐸 )

(25)

𝑏 DNA Polymerase Waiting for Bacteria where 𝑁 × 𝐸 𝑝 1.isPlasmid the totalChromosome energy consumption in encoding stages, having 𝑁 plasmids, 2. ID Stage 𝑀 × 𝐸 is 4. 𝑚 3. techniques dependent signal-like feature extraction and compression, network lifetime DP DP× 𝑇 energy energy consumption inonencapsulation plasmids, 𝐾 × 𝐸 enhancing consumption DP stage having 𝑀 DP U U U U 𝑑 𝑒 while designing routing and channel access protocols jointly, and adopting appropriate learning in propagation stages and 𝐽 × (𝐸 + 𝐸 ) is the energy consumption in the decapsulation and E.coli:Recipient Relaxasome Transferasome Disposal Plasmid Tx:Donor architecture and reconfigurable to provide in tolerant various operating decoding stage. This techniquetechniques has a drawback in self-adaptability the case of delay and shortconditions distance ID Stage Chemotaxis as presented in Figure 17. communication. Encapsulation Tumble

Bio-compartment

1.

Plasmid DP U

Process

D P U

Run i

Chromosome 2. IP Stage

RA Concentration

DNA Polymerase Gradient Waiting for Bacteria 4. Receiver ID Stage 3. Cell Wall DP U

Concentration Transmitter RADP U Gradient Nanomachine

Tx:Donor E.coli:Recipient

Run i+1

DP Nanomachine U

CapsuleTransferasome Flagellum Relaxasome Disposal Plasmid Bacteria: E.coli

ID Stage Chemotaxis Encapsulation Figure 16. Communication stages Tumble of molecular communication using bacteria relay. Run i+1 Process Run i Bio-compartment RA Concentration D An Energy- andP Context-Centric Perspective on IoT Gradient Systems and Protocol Design U IP Stage (EC-CENTRIC) [50] has been proposed to provide an optimization analyzes basic Receiver Cell Wall framework that functionalities of IoT systems in terms of energy and context. The process of this framework is Nanomachine RA Concentration Transmitter divided in three technical aspects: providing a balance between communication tasks and Gradient Nanomachine Capsule Flagellum processing techniques dependent on signal-like feature extraction and compression, enhancing Bacteria: E.coli network lifetime while designing routing and channel access protocols jointly, and adopting appropriate learning architecture and reconfigurable techniques to provide self-adaptability in Figure Figure16. 16.Communication Communicationstages stagesof ofmolecular molecularcommunication communicationusing usingbacteria bacteriarelay. relay. various operating conditions as presented in Figure 17.

An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design Enegy Centric manager Energy (EC-CENTRIC)Harvesting [50] has been(Hybrid proposed provide an optimization framework that analyzes basic designtoand optimization of PHY/MAC/network protocos) functionalities of IoT systems in terms of energy and context. The process of this framework is divided in three technicalNetwork aspects: balance between MAC/ communication tasks and Node adata dataproviding scheduling(duty processing(feature PHY processing processing techniques dependent on signal-like feature extraction and compression, enhancing cycle, extression, (routing, data network lifetime while designing routing and channel access protocols jointly, and adopting discovery) compression..) fusion….) appropriate learning architecture and reconfigurable techniques to provide self-adaptability in Context learning Sensors data various operating conditions as presented in Figure 17. and self-adaptation Exogenous data

Energy Harvesting

(classification, estimation, adaption)

Enegy Centric manager

Figure 17. Energy and context centric framework.

Figuredesign 17. Energy and context centric framework. (Hybrid and optimization of PHY/MAC/network protocos)

Co-locating services in IoT systems to minimize the communication energy cost [51] minimizes

MAC/energy Co-locating in IoT systems to minimize cost [51] minimizes computation services and communication by enabling a service merging technique utilized in Node datathe communication Network data energy scheduling(duty mapping and co-located multiple services on devices in IoT applications. After formulating the processing(feature computation and communication energy by enabling a service merging technique utilized PHY in mapping processing service co-location problem asdata a quadratic programming problem,cycle, it is reduced to an the integer extression, (routing, and co-located multiple services on devices in IoT applications. After formulating service programming problem using a reduction technique in multi hop networks, and modeled as a discovery) compression..) fusion….)programming co-location problem as a quadratic problem, it is reduced to an integer programming maximum weighted independent set problem in a single hop network. Using integer programming problem using a reduction technique in multi hop networks, and modeled as a maximum weighted Context learning and dataof a component reduction, theSensors energy cost 𝐶 𝑖 is expressed as: self-adaptation independent Exogenous set problem in a single hop network. Using integer reduction, the energy (classification, estimation,programming adaption) data 𝑖) 𝑖𝑥 ) 𝑦𝑖 ) (𝐿 (𝐿 cost of a component Ci is expressed as: 𝛿(𝐶 = ∑ 𝛿𝑇 + ∑ 𝛿𝑅 (26) Figure 17. Energy and context centric framework. 𝑥 𝑦

      Co-locating services in IoT systems to ∑ minimize communication energy cost [51] minimizes δ Ci = δT Lix the +∑ δR Lyi (26) computation and communication energy xby enabling ya service merging technique utilized in mapping and
co-located
multiple services on devices in IoT applications. After formulating the ix and δ Lyi are transmission cost of its out-links Lix and receiving cost of its in-links Lyi where δ L T service co-location Rproblem as a quadratic programming problem,j it is reduced to an integer in FBP respectively. Hereusing pij = 1a ifreduction Ci has chosen to be run on device then the energy consumption programming problem technique in multi hopDnetworks, and modeled as a of a device is formulated as:     maximum weighted independent set problem in a single hop network. Using integer programming i δ D𝐶j 𝑖 = (27) ∑ pij ∗ δ Cas: reduction, the energy cost of a component is expressed i

𝑦𝑖 ) A summary of related literatures techniques is given 𝛿(𝐶 𝑖 ) =on ∑energy 𝛿𝑇 (𝐿𝑖𝑥 ) efficient + ∑ 𝛿𝑅 (𝐿communication (26) in Tables 12 and 13, which comprises characteristics/protocol, issues, contributions, techniques, 𝑥 𝑦 simulation tools used, metrics and research limitations of each paper reviewed.

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Table 12. Summary of energy efficient communication techniques. Characteristics

Issues

Protocols

Contributions

Techniques

Simulators

Metrics

Limitations

OMNET++

-EE -DL -NL

Link quality has not been considered

–

-EE -NDIB -DR

Inferior Energy efficiency in short distance and delay tolerant communication. Functionalities such as Energy harvesting, wakeup radios and protocol adaptation have not been considered Vital parameters such as Delay, link quality have not been considered.

FFSC

Maintain tradeoff between energy efficiency and delay

Energy efficient solution with minimized delay

Fast data collection for node Farthest away from the sink and Slow data collection for node Closest to the Sink technique,

MCvB

Low capacity, more delay and sensitivity to environmental parameters via deffusion

Superior energy efficiency for long distance and delay sensitive communication

Bacteria mobile relay technique

EC-CENTRIC

Ill design network, processing and resource management solution

Energy efficiency in dynamic environment

Energy centric and context aware optimization technique

–

-NL -ND -QoS

FBP

Communication energy cost

Less computation time and energy cost

Maximum weight independent set approach based on clustering

–

-T-Ratio -L-Ratio

Table 13. Comparative assessment of energy efficient communication techniques. Approaches

Protocol/Author FFSC [48] MCvB [49] EC-CENTRIC [50] FBP [51]

MAC √

BMR

Metrics

DC √

CA

√

√

√ √

CLT

DL

√

√

√

EE √ √ √ √

T-Ratio

√

L-Ratio

√

NL √

ND

√

√

NDIB

DR

√

√

QoS

√

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In Table 13, the characteristics of each paper reviewed are categorized into three including protocol/author, approach, and metrics. The protocol/author column comprises abbreviations of techniques and references. The approach column is categorized into five classes, namely, MAC, BMR, DC, CA and CLT. The last column represents metrics including DL, EE, T-ratio, L-ratio, NL, ND, NDIB, DR and QoS. Considering Table 13, the approaches in columns two, three and five demonstrate that more contributions have been made by MAC-, DC- and CLT-based techniques, followed by BMR, and CA techniques. Further, the metrics column shows that EE is the most considered metric for measuring the performance of energy efficient communication techniques, followed by NL, then DL, Energies 2017, 10, 2073 29 of 41 ND and DR.

4.4. 4.4. Energy Energy Efficient Efficient Cloud Cloud Computing Computing Techniques Techniques In In this this section, section, energy energy efficient efficient techniques techniques at at the the cloud cloud processing processing and and storage storage layer layer have have been been described in two categories, namely energy efficient virtual machine optimization and Lyapunov described in two categories, namely energy efficient virtual machine optimization and Lyapunov optimization optimization techniques. techniques. 4.4.1. 4.4.1. Energy Energy Efficient Efficient Virtual Virtual Machine Optimization Techniques Techniques An An evergreen evergreen cloud: Optimizing Optimizing energy energy efficiency efficiency in in heterogeneous heterogeneous cloud cloud computing computing architectures architectures [52,53] [52,53] has has been been introduced introduced for for solving solving energy energy efficiency efficiency problem problem using using novel novel mathematical optimizationmodel model based on migration VM migration cloud data center under IoT mathematical optimization based on VM in cloudindata center under IoT applications. applications. Moreover robust energy efficiency scheduling procedure, not migration dependentison live Moreover a robust energya efficiency scheduling procedure, not dependent on live offered. migration offered. This wholeSmart technique is termed Smart(SVOP) VM Over Provision This wholeistechnique is termed VM Over Provision (Figure 18). (SVOP) (Figure 18).

Cloud Data Center SaaS VMs Received Requests Cloud Management System Request stream

Server Group1 Server hosting VMs Server Group 2 APP k

APP i

Client side Figure Figure 18. 18. Heterogeneous Heterogeneous IoT IoT client client sending sending various various requests requests to to aa public public cloud. cloud.

Predictive Predictive Optimization-based Optimization-based Energy Energy efficiency efficiency for for cloud cloud computing computing system system (POEE) (POEE) [54] [54] has has been proposed to optimize energy consumption and maintain system performance, firstly by been proposed to optimize energy consumption and maintain system performance, firstly by predicting predicting the resource usage of upcoming period using a Gaussian process regression approach for the resource usage of upcoming period using a Gaussian process regression approach for resource resource orchestration in cloud computing-based IoT. An appropriate numbersservers of physical servers orchestration in cloud computing-based IoT. An appropriate numbers of physical are computed are computed a convex optimization technique for every monitoring window. Finally, the using a convexusing optimization technique for every monitoring window. Finally, the objective of energy objective of energy saving is achieved by issuing a corresponding migration instruction to stack the saving is achieved by issuing a corresponding migration instruction to stack the virtual machine and virtual and turnservers. off theThe idleLagrangian physical servers. Lagrangian function offormulated optimization turn offmachine the idle physical functionThe of optimization problem is as: problem is formulated as: ! ! !     ϑk𝑘 ϑ𝜗k𝑘 idle 𝑖𝑑𝑙𝑒 + β a k𝑘− 𝑘k F𝐹(𝑎 ak ,𝑘β, 𝛽)==γ𝛾k𝑘s𝑠n𝑛 (𝑙 l k𝑘 (𝜗k ) − l + +𝑠s𝑘k𝑎a𝑘k P + ω 0 − a (28) − 𝑙) 𝑃 + 𝛽 (𝑎 − ) + 𝜔(0 − 𝑎 ) (28) 1) ( l ) (−1) a𝑎𝑘 l𝑙k𝑘(− (𝑙) 𝑘 𝑘 𝑖𝑑𝑙𝑒 where theenergy energycost costofofthe therunning runningmachine. machine.This This technique technique is is not not well well suited where s𝑠k a𝑎k P𝑃idle isisthe suited for for real real life scenarios. life scenarios.

4.4.2. Energy Efficient Lyapunov Optimization Techniques Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems (DREAM) [52] has been proposed by invoking Lyapunov optimization to solve jointly three dynamic problems: first, determining the use of local CPU or cloud resources, second, task allocation to transmit across the network and processing in local CPU and third, CPU clock speed and network

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4.4.2. Energy Efficient Lyapunov Optimization Techniques Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems (DREAM) [52] has been proposed by invoking Lyapunov optimization to solve jointly three dynamic problems: first, determining the use of local CPU or cloud resources, second, task allocation to transmit across the network and processing in local CPU and third, CPU clock speed and network interface controls. DREAM minimizes the energy usage of mobile nodes while considering delay constraints under heavy traffic in an IoT cloud system. Lyapunov drift plus penalty function is defined as sum of expected network and CPU energy consumption during time slot s, and formulated as: ∆( L(s)) + VE{ Ec (r c (s)) + En (l (s))| Q(s)}

(29)

where V is energy-delay trade off parameter. Summaries of the related literatures on energy efficient cloud computing techniques are given in Tables 14 and 15, which comprise characteristics/protocol, issues, contributions, techniques, simulation tools used, metrics and research limitations of each paper reviewed. In Table 15, the characteristics of each paper reviewed are categorized into three groups, including protocol/author, approach, and metrics. The protocol/author column comprises abbreviations of techniques and references. The approach column is categorized into five classes, namely, SVM, LO, PO, QoS and CLT. Last column represents metrics including DL, EE, PD, EUS, AR, PC, CU and NoM. Considering Table 15, the approaches in column four demonstrate that more contributions have been made by QoS-based techniques, followed by SVM, LO, PO and CLT techniques. Further, the metrics column shows that EE is the most considered metric, followed by DL and CU then PD, EUS and AR for measuring performance of energy efficient cloud computing techniques.

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Table 14. Summary of energy efficient cloud computing techniques. Characteristics

Issues

Contributions

DREAM

Increasing cpu and energy consumption in mobile device

Energy efficient and fairly scheduled solution with less delay

SVOP

High cloud provider cost and energy consumption

POEE

Challenge to balance system performance and power consumption

Protocols

Techniques

Simulators

Metrics

Limitations

Lyapunov optimization technique

Android phone (Galaxy note2)

-DL -EE -PD

Recorded video streaming has not been evaluated

Consolidation based energy efficient and robust solution

Smart virtual machine over provision technique

VanetMobiSim

-EUS -AR -NoM

Less acceptation rate

Energy efficient and adaptive solution

Predictive and convex optimization technique

NS-2 and SUMO EvalVid

-PC -DL -CU

Limited to homogenous environment and not suitable in real life scenarios

Table 15. Comparative assessment of energy efficient cloud computing techniques. Approaches

Protocol/Author SVM DREAM [52] SVOP [53] POEE [54]

LO √

PO

√ √

Metrics QoS √ √

CLT

DL √

√

√

EE √ √ √

PD √

EUS

AR

√

√

PC

CU

NoM

√ √

√

√

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4.5. Energy Efficient Techniques at the Application Layer Energies 2017, 10, 2073

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A green and reliable communication modeling for industrial internet of things [55], named Hybrid 4.5. EnergyProtocol Efficient Techniques the Applicationof Layer Transmission (HTP) is aatcombination a send-wait automatic repeat request approach in hotspot areas andand a network transmission approach in non-hotspot A green reliable coding-based communicationredundant modeling for industrial internet of things [55], namedareas for industrial IoT applications. hasisbeen proposed of to amaximize save energy Hybrid Transmission ProtocolHTP (HTP) a combination send-wait lifetime, automatictorepeat request and in is initial energy of node, d is data size and k is maximum to minimize delays in the areas network. approach in hotspot and If a E network coding-based redundant transmission approach in non-hotspot areas for industrial IoT applications. hasrequest been proposed to maximize lifetime, time for retransmission under send-wait automaticHTP repeat approach. Then lifetime of to whole 𝑖𝑛 save energy and to minimize delays in the network. If 𝐸 is initial energy of node, 𝑑 is data size network under HTP technique is expressed as: and 𝑘 is maximum time for retransmission under send-wait automatic repeat request approach.  Then lifetime of whole network is expressed Ein  under HTP technique , i f las:< l

L=

𝐿=

0 kdEele +k (d+1) Eele +k (d+1)∈ f s d2 in𝑖𝑛 𝐸 E  , i f l >, 𝑖𝑓l0𝑙 < 𝑙0 𝑒𝑙𝑒 ele k(d+ +k(+ d+ 1)∈+ d4 ∈ 𝑑 2 ) Eele𝑒𝑙𝑒 amp1) 𝑘𝑑𝐸kdE ++𝑘(𝑑 +11)𝐸 𝑘(𝑑 𝑓𝑠

(30)

𝐸 𝑖𝑛 and end to end reliability can be formulated as: 𝑒𝑙𝑒 , 𝑖𝑓 𝑙 > 𝑙0 𝑒𝑙𝑒 {𝑘𝑑𝐸 + 𝑘(𝑑 + 1)𝐸 + 𝑘(𝑑 + 1) ∈𝑎𝑚𝑝 𝑑 4 n+1 can be formulated N as: and end to end reliability k e2e

R

=

∏

1 − (1 − R )

i

𝑛+1

i =1

∏𝑁

q

(30)

! 1−

i = n +2

h +2 Rl i

− (hi + 2) Rlhi +1 (1 −

Rl )

ℎ +2 ℎ +1 𝑅𝑒2𝑒 = ∏ (1 − (1 − 𝑅)𝑘𝑖 ∏ √1 − 𝑅𝑙 𝑖 − (ℎ𝑖 + 2)𝑅𝑙 𝑖 (1 − 𝑅𝑙 ))

(31) (31)

𝑖=𝑛+2 where k i is maximum time 𝑖=1 for retransmission under send-wait automatic repeat request approach where 𝑘 is maximum time for retransmission undernetwork send-wait automatic repeat request approach for node i and𝑖 hi is redundancy level of node i under coding based redundant transmission for node i and ℎ is redundancy level of node i under network coding based 𝑖 approach. Energy-Efficient Dynamic Packet Downloading for Medical IoT Platforms redundant (EEDPD) [56] transmission approach. Energy-Efficient Packet Downloading for Medical IoT utilizes dynamic energy efficient techniqueDynamic to compute amount of power allocated inPlatforms every access (EEDPD) [56] utilizes dynamic energy efficient technique to compute amount of power allocated in point which is based on channel state and buffer backlog size considering buffer stability to enhance every access point which is based on channel state and buffer backlog size considering buffer power/energy management. stability to enhance power/energy management. The medical cloud computing architecture (Figure 19) consists of various key element: medical The medical cloud computing architecture (Figure 19) consists of various key element: medical users users (mobile phone, tablet etc.), access points cloud. (mobile phone, tablet etc.), access pointsand andmedical medical cloud.

Medical clouds Processing unit Internet Diagnosis files Gateway

Medical video database

Access point

Images (Xray. CT,MRI)

Downlink data transmission over wireless channel Portable PC

Mobile phone

Tablet

Medical machine

Figure 19. Medical cloud computing system architecture.

Figure 19. Medical cloud computing system architecture.

The medical cloud containing its own processing unit, can generate relational maps and find

The medicalpatient cloudinformation containingwhile its own processing unit, can generate relational maps and find meaningful performing data mining process. Related medical information is downloaded by medicalwhile usersperforming from central platforms. In this information technique is meaningful patient information datamedical mining cloud process. Related medical quality-ware operations arefrom not central considered in medical imagining cloud downloaded by medical users medical cloud platforms. In thisnetworks. techniqueOptimized quality-ware Day-Ahead Pricing with Renewable Energy Demand-Side Management for Smart Grids (ODAP) operations are not considered in medical imagining cloud networks. Optimized Day-Ahead Pricing [57] has been suggested while considering renewable energy buying- back technique with dynamic with Renewable Energy Demand-Side Management for Smart Grids (ODAP) [57] has been suggested pricing to make energy efficient system for smart grid enabled IoT. In this technique, a day-ahead while considering renewable energy buying-back technique with dynamic pricing to make energy time-dependent pricing scheme has been proposed distributively to enhance user privacy, after efficient system for smart grid enabled IoT. In this technique, a day-ahead time-dependent pricing formulating dynamic pricing problem as a convex optimization dual problem for providing benefit scheme has proposed to enhance user20, privacy, after formulating pricing to both been user and electric distributively companies. According to Figure a power delivery system isdynamic composition problem as a convex optimization dual problem for providing benefit to both user and electric

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companies. Figure a power delivery system is composition of anenergy electricgeneration company of an electricAccording company to and users20, having energy storage devices, and renewable and users having energy storage devices, and renewable energy generation devices (wind turbine). devices (wind turbine). Electric company

Users

Smart meter Info flow Line flow Storage Figure 20. 20. Power overview. Figure Power delivery delivery system system overview.

From the user’s perspective, if 𝑙𝑘 is electricity consumed by each user with charged/discharged From the user’s perspective, if lk is electricity consumed by each user with charged/discharged rate 𝑟𝑘 and 𝑚𝑘 is renewable energy sold by each user in time slot k, then welfare of every user is rate rk and mk is renewable energy sold by each user in time slot k, then welfare of every user is formulated as: formulated as: 𝐾

   𝑖 , 𝑛𝑖 ) − 𝑅𝑠(𝑟 𝑖) − 𝑅𝑟  𝑊(𝑙𝑘𝑖 , 𝑛𝑘𝑖 ) =K∑(𝑈(𝑙 (𝑚𝑘𝑖 )− 𝑃𝑘 × (𝑙𝑘𝑖 + 𝑟𝑘𝑖 )+ 𝐸𝑘 × 𝑚𝑘𝑖 ) 𝑘 𝑘 𝑘 W lki , nik = ∑ 𝑘 U lki , nik − Rs rki − Rr mik − Pk × lki + rki + Ek × mik k

(32) (32)

𝑖 𝑖 where 𝑈(𝑙𝑘𝑖 , 𝑛𝑘𝑖
) is the utility function of user, 𝑅
𝑠 (𝑟𝑘𝑖 ) + 𝑅𝑟 (𝑚 (𝑙𝑘𝑖 +
𝑘 ) + 𝑃𝑘 ×
𝑟𝑘 ) is the total cost of i i i i i i 𝑖 where lk , and nk is𝐸the utility function of user, Rs rk + Rr mk +energy Pk × lkback + rk to is grid. the total cost of every every U user renewable A summary of 𝑘 × 𝑚𝑘 is the profit after selling i is the profit after selling renewable energy back to grid. A summary of related user and E × m related literatures k k on energy efficient techniques at the application layer is given in Tables 16 and 17, literatures on energy efficient techniques atissues, the application layer techniques, is given in Tables 16 andtools 17, which which comprises characteristics/protocol, contributions, simulation used, comprises issues, contributions, metrics andcharacteristics/protocol, research limitations of each paper reviewed. techniques, simulation tools used, metrics and research of each paper In Tablelimitations 17, characteristics of eachreviewed. paper reviewed are categorized into three groups including In Table 17, characteristics each paper are categorized three groups including protocol/author, approach, andofmetrics. The reviewed protocol/author column into comprises abbreviations of protocol/author, approach, and metrics. The protocol/author column comprises abbreviations of techniques and reference. The approach column is categorized into five groups, namely SVM, techniques reference. TheLast approach column is categorized into five groups, NCRT, DA,and DAP and REBB. column represents metrics including DL, EE,namely GL, GV,SVM, LLR,NCRT, PAR, DA, and Considering REBB. Last column represents metricsinincluding DL, EE, GL, GV, equally. LLR, PAR, PDR and PDRDAP and NL. Table 17, the approaches every column contribute Further, the NL. Considering Table 17, the approaches in every column contribute equally. Further, the metrics metrics column shows that EE is the most considered metric, followed by NL then PDR, LLR and column that performance EE is the most metric, followedatby NL then PDR,layer. LLR and PAR for PAR for shows measuring ofconsidered energy efficient techniques the application measuring performance of energy efficient techniques at the application layer.

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Table 16. Summary of energy efficient techniques at application layer. Characteristics Protocols

Issues

Techniques

Contributions

Simulators

Metrics

Limitations

Bad performance in case of small link loss rate

HTP

Energy consumption, delay and reliabilty

Green and reliable communication

send-wait automatic repeat request and network coding based redundant transmission techniques

OMNET++

-DL -EC -NL -PDR -LLR

EEDPD

Problem of buffer stabilization and energy efficiency

Reliable, robust and energy efficient solution

Energy efficient dynamic allocation technique

NS-2

-EE -NL

Quality awareness has not been considered.

OPAD

Evenly distributed energy consumption

Scalable, energy efficient solution.

Renewable energy buying back and day ahead time dependent pricing techniques

NS-2 and SUMO EvalVid

-EE -GL -GV -PAR

Vital parameters such as delay and Quality awareness have not been considered

Table 17. Comparative assessment of energy efficient techniques at application layer. Approaches

Protocol/Author HTP [55] EEDPD [56] OPAD [57]

SW-ARQ √

NCRT √

DA

Metrics DAP

REBB

√

√

√

DL √

EE √ √ √

GL

GV

√

√

LLR √

PAR

√

PDR √

NL √ √

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5. Open Research Issues in Energy Oriented IoT 5. Open Research Issues in Energy Oriented IoT Many issues related to energy consumption have been solved and isolated by researchers, but Manyare issues related to consumption been solved and isolated but still there challenges to energy solve existing issues.have An insight is provided relatedbytoresearchers, existing issues still there are challenges to solve existing issues. An insight is provided related to existing issues faced faced to attain in energy efficient system. The research issues that require attention are as follows: to attain in energy efficient system. The research issues that require attention are as follows: · Cross layer interoperability • Cross layer interoperability Several algorithms and techniques working separately on a particular layer of IoT architecture Several algorithms and techniques working separatelythem on a in particular layer of IoTsolution architecture are introduced for energy efficiency in the IoT. Combining single operational is an are introduced for energy efficiency in the IoT. Combining in single solution is an existing challenge. The concept of interoperability should bethem stabilized andoperational privileged by the system existing challenge. The concept of interoperability should be stabilized and privileged by the system to avoid oscillation among layers [34]. Moreover, there should be enough flexibility in the system, to avoid oscillation among layers [34]. Moreover, should enough flexibility in theaffecting system, while changing a certain techniques such as energythere efficiency of be a particular layer without while changing a certainFigure techniques such as energy efficiency of a particular layer the whole IoT system. 21 represents the concept of interoperability of IoTwithout layers: affecting sensing, the whole IoT system. Figure 21 represents the concept of interoperability of IoT layers: sensing, local local processing and storage, network, cloud processing and storage, and application, in terms of processing and storage, network, cloudefficiency, processingmobility, and storage, application, terms of various various techniques related to energy and and security and QoSinparameters. This techniques to energy efficiency, and more security and QoS parameters. Thisvarious cross layer cross layer related interoperability makes a mobility, IoT system effective and efficient in IoT interoperability makes a IoT system more effective and efficient in various IoT applications [58–65]. applications [58–65].

Application

Mobility

Security

Local computing and storage

Quality of Service

Network

Energy efficiency

Cloud processing and storage

Sensing Figure Figure 21. 21. Cross Cross layer layer interoperability. interoperability.

· Energy is omnipresent Potential of energy harvesting • Energy is omnipresent Potential of energy harvesting In the form of light, temperature, motion, wind, electric, sound, mechanical etc., but only needs In the form of light, temperature, motion, wind, electric, sound, mechanical etc., but only needs to to be harvested. Cyber-physical systems, which are a new area in IoT, combine networking and be harvested. Cyber-physical systems, which are a new area in IoT, combine networking and computing computing with physical processes [36]. These processes can be human controlled or autonomous. with physical processes [36]. These processes can be human controlled or autonomous. IoT-based smart IoT-based smart sensors gather, control and measure the information and communicate to the base sensors gather, control and measure the information and communicate to the base station [37]. Because station [37]. Because of the battery limitations of the devices, energy harvesting is necessary, using of the battery limitations of the devices, energy harvesting is necessary, using natural energy resources natural energy resources like wind, vibration power, solar and thermal power [39]. Due to like wind, vibration power, solar and thermal power [39]. Due to unreachability of these devices in unreachability of these devices in many cases, energy efficiency to support these devices through many cases, energy efficiency to support these devices through energy harvesting techniques is an energy harvesting techniques is an important research issue. In Figure 22, various natural energy important research issue. In Figure 22, various natural energy resources those can be potentially utilized resources those can be potentially utilized for energy harvesting are illustrated. The usage of energy for energy harvesting are illustrated. The usage of energy resources such as solar power, vibration resources such as solar power, vibration power, thermal power, wireless power and wind power power, thermal power, wireless power and wind power may solve the issue of battery limitation in may solve the issue of battery limitation in terms of energy through various energy harvesting terms of energy through various energy harvesting techniques, in the IoT environment [62]. techniques, in the IoT environment [62].

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Solar power Solar power

Vibration power Vibration power

Energy Energy harvesting harvesting Wind power Wind power

Thermal power Thermal power

Wireless power Wireless power

Figure 22. 22. Energy harvesting in in IoT. IoT. Figure Energy harvesting Figure 22. Energy harvesting in IoT.

· Interactivity limitations limitations ·• Interactivity Interactivity limitations A network can be created for processing and managing data on various remote servers joined A can created for processing and data on remote joined A network network can be be processing and managing managing datacomputing on various variousenvironment remote servers servers joined together rather than bycreated a local for computer or server in the cloud [52]. The together rather than by a local computer or server in the cloud computing environment [52]. together rather than by a local computer or server in the cloud computing environment [52]. The cloud provides sharable resources among multiple users, while making accessible large amounts of The cloud provides sharable resources among multiple users, while making accessible large amounts cloud provides sharable resources among users, while making large amounts of information gathered from IoT devices for multiple analysis and processing [53]. Inaccessible the case of gathering large of information gathered from IoT devices for analysis and processing [53]. In the case of gathering information gathered from IoTitdevices analysis and processing [53]. In theconsumption case of gathering amount of data from devices creates for multiple effects such as more energy [54].large As a large amount offrom data devices from devices it creates multiple effects such as more energy consumption [54]. amount of data it creates multiple effects such as more energy consumption [54]. As a result new approaches and research can be introduced for reducing the device interactivity to As a result new approaches and research can be introduced forreducing reducingthe thedevice device interactivity interactivity to result new approaches and research can be introduced for to minimize energy consumption in IoT applications [63–66]. In Figure 23, location-based interaction minimize energy consumption in [63–66]. In 23, interaction minimize energy in IoT IoT applications applications [63–66]. In Figure Figurebetween 23, location-based location-based based on the cloudconsumption and IoT is illustrated. Location-based interactions IoT devicesinteraction and users based on the cloud and IoT is illustrated. Location-based interactions between IoT devices and based cloud and IoT are is illustrated. interactions between devices and users users based on on the cloud computing one of theLocation-based effective approaches to overcome theIoT issue of interactivity based on cloud computing are one ofofthe effective approaches to to overcome thethe issue of interactivity for based on cloud computing are one the effective approaches overcome issue of interactivity for making energy efficient IoT system [67]. making energy efficient IoT system [67]. for making energy efficient IoT system [67].

... ...

Location L1 Location L2 Location Ln Location User1 L1 Location User1 L2 Location User NLn User1 User1 User N Application1 Application2 Application M Application1 Application2 Application M Cloud Cloud

... ... Physical WSNs Physical WSNs

Figure 23. Location-based IoT and cloud interaction. Figure 23. Location-based interaction. Figure 23. Location-based IoT IoT and and cloud cloud interaction.

· QoS constraints approaches · QoS constraints approaches • QoSDevices constraints approaches have become autonomous and intelligent, because of the usage of new computational Devices have become autonomous intelligent, becauseofofIoT the usage of [40]. new computational concepts like artificial intelligence for theand effective deployment limitations Devices have become autonomous and intelligent, because of thenetworks usage of new The computational concepts like artificial intelligence for the effective deployment of IoT networks [40]. The limitations of energylike consumption should be through the association of artificial neural networks. concepts artificial intelligence foradapted the effective deployment of IoT networks [40]. The limitations of of energy consumption should system be adapted through the association of artificial neural networks. Moreover the QoS constrained may be controlled in an intelligent manner for IoT energy consumption should be adapted through the association of artificial neural networks. every Moreover Moreover the QoS constrained systemenergy may be controlled in an intelligent manner for every IoT application that may have may a distinct [42]. According requirements of every the QoS constrained system be controlled profile in an intelligent manner to forthe every IoT application that application that may have a distinct energy profile and [42]. research According to the of every application in an IoT network, novel techniques based onrequirements QoS constraints can may have a distinct energy profile [42]. According to the requirements of every application in an IoT application in an IoT network, novel techniques and research based on QoS constraints can contribute to providing potential solutions [3]. Figure 24 represents a QoS-constrained IoT system, network, novel techniques and research based on QoS constraints can contribute to providing potential contribute to providing potential solutions Figure 24intelligence, represents a QoS-constrained IoT system, while fulfilling various requirements such [3]. as artificial solve the open requirements challenge of solutions [3]. Figure 24 represents a QoS-constrained IoT system, whiletofulfilling various while fulfilling various requirements such as artificial intelligence, to solve the open challenge of energy consumption in IoT applications. such as artificial intelligence, to solve the open challenge of energy consumption in IoT applications. energy consumption in IoT applications.

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Requirements Requirements

Internet of Things Internet of Things (IoT) (IoT)

Applications and Applications and Users Users

QoS Support QoS Support Figure 24. QoS support for IoT. Figure Figure 24. 24. QoS QoS support support for for IoT. IoT.

· Energy efficiency in real time emergency response · Energy efficiency in real time emergency response • Energy efficiency in real time emergency response Energy is susceptible to security attacks due to encryption incapability. IoT devices have an Energy is susceptible to security attacks due to encryption incapability. IoT devices have an Energy susceptible to security to encryption IoT devices an energy state iswithin a network, that attacks can be due forged, which is incapability. called an energy forgeryhave attack. energy state within a network, that can be forged, which is called an energy forgery attack. energy stateenergy withinstate a network, can be forged, which is called an energy forgery attack. Therefore, Therefore, privacythat preservation issues require attention [58]. Also, some backup can be Therefore, energy state privacy preservation issues require attention [58]. Also, some backup can be energy state preservation require attention Also,Therefore, some backup beisavailable for available forprivacy information, but noissues backup is available for[58]. energy. thiscan area one of the available for information, but no backup is available for energy. Therefore, this area is one of the information, but no backup available for energy. Therefore, one of the research challenges research challenges in IoT. is Real time performance is one ofthis the area mostischallenging issues in various research challenges in IoT. Real time performance is one of the most challenging issues in various in IoT. Real time performance is onemonitoring, of the most challenging issues in various IoT applications such as IoT applications such as medical forest fire monitoring and warning and intelligent IoT applications such as medical monitoring, forest fire monitoring and warning and intelligent medical monitoring, forest fire monitoring and warning and intelligent transportation systems [47]. transportation systems [47]. Improving the real time performance in terms of ensuring emergency transportation systems [47]. Improving the real time performance in terms of ensuring emergency Improvingability, the real energy time performance terms of ensuring ability, energy response efficiency,inreliability of dataemergency gatheringresponse or transmission haveefficiency, become response ability, energy efficiency, reliability of data gathering or transmission have become reliability ofresearch data gathering or transmission become challenging research [64]. Through challenging issues [64]. Through thehave ERGID technique, an attempt hasissues been made to solve challenging research issues [64]. Through the ERGID technique, an attempt has been made to solve ERGID technique, an attempt has made emergency to solve theresponse problemIoT, of energy in the problem of energy consumption in been small-scale but theconsumption issue of energy the problem of energy consumption in small-scale emergency response IoT, but the issue of energy small-scalewith emergency response IoT, but the issue of energy efficiency reliability in large-scale efficiency reliability in large-scale emergency response IoT haswith become an open research efficiency with reliability in large-scale emergency response IoT has become an open research emergencyfacing response IoT has become an open research challenge researchersresponse [68]. Figure 25 challenge researchers [68]. Figure 25 illustrates the real facing time emergency in an challenge facing researchers [68]. Figure 25 illustrates the real time emergency response in an illustrates the real time emergency an the intelligent system, while focusing on the intelligent transport system, whileresponse focusinginon issue oftransport real time performance enhancement intelligent transport system, while focusing on the issue of real time performance enhancement issue of real planning time performance enhancement using route planning and energy management. using route and energy management. using route planning and energy management.

Vehicle location Vehicle location

Starting point Starting point Immediate Immediate emergency response emergency response

Vehicle Vehicle monitoring monitoring Route planning Route planning and safety and safety assurance assurance

Driver behaviour Driver behaviour monitoring monitoring Destination Destination Freight location, Freight location, status and scheduling status and scheduling

Figure intelligent transport transport system. Figure 25. 25. Real Real time time emergency emergency response response in in Figure 25. Real time emergency response in intelligent intelligent transport system. system.

· Backhauling in ultra-dense networks ·• Backhauling Backhauling in ultra-dense networks in ultra-dense networks Powering and backhauling in ultra-dense networks (UDNs) is a critical deployment issue [58]. Powering and backhauling backhauling in ultra-dense ultra-dense networks networks (UDNs) (UDNs) is is aa critical critical deployment deployment issue issue [58]. [58]. The main problem in the ultra-dense deployment is taking care of the power requirements of each The main problem in the ultra-dense deployment is taking care of the power requirements of each small cell base station [66]. Because of concern about this problem, the concept of self-backhauling small cell cell base basestation station[66]. [66].Because Becauseofofconcern concern about this problem, concept of self-backhauling about this problem, thethe concept of self-backhauling has has become an open research field in ultra-dense network-related IoT [69]. has become an open research in ultra-dense network-related IoT [69]. become an open research fieldfield in ultra-dense network-related IoT [69]. 6. 6. Conclusions Conclusions IoT are day. energy consumption is IoT systems are evolving evolvingand andbecoming becomingmore morecommon commonday dayby day.The The energy consumption systems are evolving and becoming more common day bybyday. The energy consumption is the only restriction that constrains the processing of IoT network functionalities. In this study, a is only restriction that constrains processing IoT network functionalities. study, thethe only restriction that constrains thethe processing of of IoT network functionalities. In In thisthis study, a comprehensive classification structure and a qualitatively analysis of related publications on energy acomprehensive comprehensiveclassification classificationstructure structureand andaaqualitatively qualitativelyanalysis analysisof ofrelated related publications publications on on energy efficient techniques in relation to an IoT perspective have been presented. Different issues and efficient techniques in relation to an IoT perspective have been presented. Different issues and techniques employed in energy efficient techniques in IoT have been analyzed. We have also techniques employed in energy efficient techniques in IoT have been analyzed. We have also

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efficient techniques in relation to an IoT perspective have been presented. Different issues and techniques employed in energy efficient techniques in IoT have been analyzed. We have also elaborated future research directions. This review work will improve the understanding of energy efficiency research trends and directions in the IoT, thus hopefully stimulating research work in the energy-related domain in the IoT by making research gaps finding easier, while better energy-efficient techniques will improve the sensing, computation and communication efficiency in various IoT applications. Author Contributions: K.K. and O.K. conceived, designed and performed the experiments. K.K. has carried out review with the guidance of O.K. and S.K. The paper is written and revised by K.K. and O.K. The paper is read, and quality improvement suggested by Y.C., J.L., and N.A. Conflicts of Interest: The authors declare no conflict of interest.

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