sensors

sensors Article

Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development Zeba Idrees, Zhuo Zou *

ID

and Lirong Zheng *

School of Information Science and Engineering, Fudan University, Shanghai 200433, China; [email protected] * Correspondence: [email protected] (Z.Z.); [email protected] (L.Z.) Received: 5 July 2018; Accepted: 23 August 2018; Published: 10 September 2018







Abstract: With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system’s effectiveness. Keywords: air pollution monitoring; IoT; edge computing; pollution sensors; electrochemical gas sensors

1. Introduction Air quality is one of the key measures to be closely observed in real-time for today’s urban environments, because it has a paramount impact on human health, safety and comfort. Countries have established their own structures, policies and standards to monitor air pollution and generate alerts for inhabitants [1]. Although this information is restricted to outdoor environments, most of the measurements are static and only obtain average values; however in real time, air quality is variable and may be influenced by diverse circumstances [2], for example wind speed, population density, pollutant distribution, and whether the location is indoors or outdoors. Conventionally, air pollution monitoring stations are large in sizes and expensive for installation and maintenance [3]. However, the air quality data generated by these stations is very accurate. Sensors 2018, 18, 3021; doi:10.3390/s18093021

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Efforts have been made for alternative & cost efficient solutions. Internet-of Things (IoT) is a novel technology which attracts attention from both academia and industry. To overcome the flaws of current monitoring systems and their recognition methods and reduce the overall cost, this paper offers a novel approach that combines the IoT technology with environment monitoring [4–6]. This approach provides a low cost, accurate, easy to deploy, scalable and user friendly system. This paper presents a comprehensive review of pollution monitoring needs, existing monitoring systems, their limitations, and current challenges faced by these monitoring systems. We examine in depth the issues, infrastructure, data processing, and encounters of designing and deploying an integrated sensing node for observing indoor/outdoor air pollution. This project designed an air monitoring system model utilizing the edge computing & IoT architecture, assuring measurement accuracy and power efficiency with minimum cost. To evaluate the system’s viability, a prototype was developed with the Arduino platform and experimental evaluation has been conducted in different sets. The rest of the paper is organized as follows: The background and previous research are discussed in Section 2. Section 3 describes the planned edge computing based IoT architecture. Section 4 explains the system implementation. Experimental evaluation and results are discussed in Section 5. Section 6 presents the conclusion. 2. Background and Related Work With the rapid growth of the industrial sectors and urbanization, the environment becomes highly polluted to the level of disturbing the daily life of the people. Environmental pollution in the broad sense includes the pollution of air, water and the land. Of all these kinds of pollution, air pollution occupies the most prominent place in damaging the health of the people [7,8]. To reduce the impact of air pollution on individuals, the global environment and the global economy, great efforts have been made on air monitoring.

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Pollution Monitoring Systems and Related Requirements: Air quality monitoring systems (AQMS) can be categorized as the indoor and outdoor pollution monitoring reliant on the place where the event occurs. Outdoor air pollution refers to the open and industrial environment. In contrast, the indoor case is the pollution of the air in small confined spaces within homes, work places, offices and closed areas like underground shopping centers and subways [9,10]. Because of their different environment and pollutant types, monitoring systems for indoor and outdoor air have different related requirements as described in Table 1. Table 1. Pollution monitoring systems and related requirements.

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System Type

Deployment & Maintains

Cost

Accuracy

Power Consumption

Response Time

Indoor Outdoor Industrial

Easy Average Average

Little Average Average

Average High Very High

Low Little Average

Average Average Fast

Air Quality Index: As a standard of measurement of air quality, AQI is a quantitative depiction of the air pollution level. The major pollutants involved in the analysis (as described by the Environmental protection agency US [11]) include fine particulate matter (PM2.5 ), inhalable particles (PM10 ), SO2 , NO2 , O3 , CO. Here PM2.5 and PM10 are measured in micrograms per cubic meter (µg/m3 ), CO in parts per million (ppm), SO2 , NO2 , and O3 in parts per billion (ppb). AQI is divided into six levels in total, with green indicating the best and maroon the worst case [12,13], as shown in Figure 1 below.

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AQI

Health Apprehension Health Apprehension 0–50 Good 0–50 Good 51–100 Moderate 51–100 Moderate 101–150 Unhealthy for sensitive 101–150 Unhealthy for sensitive group group 151–200 Unhealthy 151–200 Unhealthy 201–300 Extremely unhealthy 201–300 Extremely unhealthy 301–500 Hazardous 301–500 Hazardous

AQI

Figure Agency. Figure 1. 1. AQI AQI levels levels according according to to China China and and the the US US Environmental Environmental Protection Protection Agency. Figure 1. AQI levels according to China and the US Environmental Protection Agency.

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Design and Deployment Strategies: To obtain reliable and accurate data, conventional Design andsystems Deployment Strategies: To obtain reliablereliable and accurate data, conventional • Design and Deployment Strategies: Toalgorithms obtain and accurate data, conventional monitoring use complex measurement and various supplementary tools. monitoring systems use complex measurement algorithms and various supplementary monitoring systems use complex measurement algorithms and various supplementary As a result, these apparatuses are usually very high in cost and power consumption, and tools. large tools. As a result, these apparatuses are usually very high in cost and power consumption, and large As a result, these apparatuses are usually very high in cost and power consumption, and large in size & weight. Technical advancements resolve these issues to some extent, in that low cost in size in & sensors weight. Technical advancements resolve these issues to some in that in low cost size & weight. resolve these issues toextent, some extent, that low cost ambient with aTechnical small sizeadvancements and quick response are easily available. However, they ambient sensors with a small size and quick response are easily available. However, they cannot ambient sensors with a small size and quick response are easily available. However, they cannot achieve similar data precision levels as conventional monitoring devices. achievecannot similarachieve data precision levels as conventional monitoring devices. similar data precision levels as conventional monitoring devices. Research shows that air monitoring systems are trending towards a new approach that Research shows thattheairWireless monitoring are trending towards a that newcombines approach Research showssensors that airand monitoring systems aresystems trending towards ainto new approach combines low-cost Sensor Network (WSN) one system[3]. Low cost that low-cost sensors and the Wireless Sensor Network (WSN) system[3]. Low cost low-cost sensorsmodels and thehelp Wireless Sensor Network (WSN) one system Low cost sensor based sensor combines based investigators recognize theinto dispersal of [3]. theinto air one pollutants more sensor based models help investigators recognize the dispersal of the air pollutants models help investigators recognize dispersal ofusers the air pollutants more competently precisely. competently and precisely. Even the community can evaluate their personal and exposure to more competently and precisely. Even community users can evaluate theirof personal exposure to Even community users evaluate their personal exposure to pollutants bynumerous means wearable sensor pollutants by means ofcan wearable sensor nodes [14–16]. There have been methodologies pollutants by means of wearable sensor nodes [14–16]. There have been numerous methodologies nodes have been numerous air quality monitoring, as into reported for air [14–16]. quality There monitoring, as reported in methodologies recent research.forThey are mainly classified two for research. air monitoring, as classified reported in recent research. arethe mainly classified into two in recent They areand mainly into two categories, i.e., stationary and mobile air categories, i.e.,quality stationary mobile air monitoring. Figure 2 They depicts categories of the categories, stationary and mobile monitoring. Figure depicts the categories monitoring. Figurei.e., 2based depicts categories of theair monitoring system based2 on deployment strategies of the monitoring system on the deployment strategies in a hierarchical manner. monitoring system based on deployment strategies in a hierarchical manner. in a hierarchical manner. Air Monitoring SystemsAir Monitoring Systems

Stationary

Mobile Stationary

Mobile

Conventional Low Cost Automobile Static Sensor Civic Sensor Monitoring Monitoring Low Cost Sensor Automobile Conventional Network Network Civic Sensor Static Sensor Stations Monitoring Devices Monitoring Network Sensor Network Network Stations Devices Network

Figure 2. Air monitoring system’s categories presented in the recent literature. Figure 2. Air monitoring system’s categories presented in theliterature. recent literature. Figure 2. Air monitoring system’s categories presented in the recent

2.1. Related Work in the Literature 2.1. Related in the Literature 2.1. Related Work inWork the Literature Reference [17] proposes an Internet of Things (IoT) system that monitors air pollution in Reference [17]use proposes anofgas Internet of Things (IoT) system monitors pollution in Reference [17] proposes anmultiple Internet Things (IoT) system that monitors air pollution real-time. real-time. The authors sensors, but do not followthat the specificin air standard real-time. The authors use is multiple sensors, but do notand follow theexperimental specific standard The authors use multiple gas sensors, but notgas follow the specific measurement procedure. measurement procedure. Also there nodoproper architecture for standard system it lacks measurement procedure. Also there is no proper architecture for system and it lacks experimental Also there is no proper architecture forasystem and it system lacks experimental evaluations. Reference [18] evaluations. Reference [18] designed monitoring based on the IoT concept where they evaluations. Reference [18] designed monitoring system based onseries, thethe IoT concept designed a monitoring system based onusing theaIoT where they monitor environmental monitor the environmental parameters lowconcept cost sensors from MQ there is no where data they monitor environmental parameters using low MQ series, there is no data parameters using low costcalibration sensors from MQ series, there is nocost datasensors validation thecost sensors calibration validation or thethe sensors procedure that is compulsory in casefrom ofor low sensors. validation or the sensors calibration procedure that is compulsory in case of low cost procedure that is presented compulsory case of low sensors. The system inin Reference [19]cost operates on an existing Wi-Fi network via thesensors. MQTT The system presented in Reference [19] operates on an existing Wi-Fi network via the MQTT The system presented in Reference [19] operates on an existing Wi-Fi network via the MQTT protocol. Their intention was to monitor the indoor air quality, and the focus was on a single protocol. Their intention was to monitor the indoor air and quality, and was on a single protocol. Their was Reference to monitor the indoor air quality, and the focus was on afocus single pollutant, pollutant, beingintention PM control. [8] aims to detect PM only notify thethe user via email using pollutant, being PM control. Reference [8] aims to detect PM only and notify the user via a low cost dust sensor and Raspberry Pi for data transmission. Reference [20] put their effortsemail into using a lowacost dusttosensor andthe Raspberry for The dataresearchers transmission. Reference [20] put their developing system measure 3D AQI Pi map. prototype a quad copter forefforts 3D into developing a system to measure the 3D AQI map. The researchers prototype a quad copter for 3D

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being PM control. Reference [8] aims to detect PM only and notify the user via email using a low cost dust sensor and Raspberry Pi for data transmission. Reference [20] put their efforts into developing a system to measure the 3D AQI map. The researchers prototype a quad copter for 3D data collection and compare the results with official data and claim to achieve reasonable accuracy. This design is not cost efficient and the system implementation is quite complex. Reference [21] uses LTE network communication and IoT concept for air pollution monitoring, they does not consider the limitations of the low cost sensors and compare their results with official data provided by National Ambient Air Quality Monitoring Information System. Reference [22] presented a mobile air quality monitoring system to attain a low-cost solution. They used a public bus rooftop as a sensor carrier. In case of mobile sensing, many considerations need to be addressed for the sensors to work properly, as the sensors work well in the static form [3]. While the cost of the system is low, due to lack of system design parameters, the data is not very reliable. References [23,24] conducted a comprehensive study for sensor network establishment in pollution monitoring systems. They analyzed sensor node placement strategies and issues. This work was evaluated via simulations. Most of the existing systems discussed above place their emphasis only on one or two main pollutants; if the systems measured more pollutants, then the data processing capabilities were unclear. While they used low cost sensors, they lack a calibration mechanism and do not deal with sensor drift compensation issues other than in the work presented in Reference [25]. Furthermore, there were no power management strategies for these systems. Table 2 presents the summary of recent efforts in the field of air pollution monitoring by comparing their design strategies, protocols, hardware/software tools and costs. Table 2. Summary of the related work. Communication Protocols

Sensing Node

Application Environment

Number of Sensing Nodes

Cost Estimation ($)

XBee Module, WIFI Ethernet, WIFI WIFI NM NM WIFI 2.4 GHz ISM Band LTE WIFI Bluetooth, Ethernet Wi-Fi Wi-Fi Zigbee Module NA Public Hotspot

Waspmote Board Arduino Arduino Raspberry Pi NM Raspberry Pi STC12C5A60S2 NM ARM Mbed Arduino, Raspberry Pi Arduino Arduino, Lab View Arduino, Raspberry Pi Mosaic, GPS Linux Embedded System

Outdoor Outdoor Indoor Outdoor Outdoor Outdoor Indoor Outdoor Outdoor Indoor Indoor Indoor Outdoor Outdoor Outdoor

Multiple 1 1 1 1 1 1 Multiple Multiple 1 1 1 1 8 Multiple

1250 500 600 1000 1500 1000 1500 X 1000 1300 500 1200 1400 800 1200

System

Carrier

[17] 2017 [26] 2017 [27] 2017 [8] 2017 [20] 2017 [28] 2017 [29] 2017 [30] 2017 [16] 2017 [31] 2017 [6] 2017 [32] 2017 [7] 2016 [9] 2016 [14] 2016

NM

NM

Outdoor

1

500

[34] 2016 [35] 2016 [10] 2016 [22] 2016

NM Lighting Pole NA NM Quad Copter NM NA Roof Top Public NA Lighting Pole NM Mobile Sensors Bus Top Lighting Pole Bus Top (Mobile Sensors) Public NA NA NM

GPRS Wi-Fi Wi-Fi, Bluetooth, RF MQTT

Outdoor Indoor Indoor Outdoor

Multiple 1 1 1

1000 1200 1200 700

[36] 2016

Lighting Pole

IEEE 802.15.4k

Outdoor

Multiple

1000+

[24] 2017 [37] 2017 [38] 2017

Simulations Simulations Simulations

Simulations Simulations Simulations

NM Arduino TIMSP430 AVR Atmega128 STM32F103RC Microcontroller Unit IBM ILOG Data Set NA Time-Varying Data Sets

Outdoor Outdoor Outdoor

Multiple Multiple Multiple

NA NA NA

[33] 2016

NA = Not Applicable. NM = Not Mentioned

2.2. AQMS Challenges In recent years, many advanced air quality monitoring systems have been proposed, as discussed in Section 2.1. There are still various subjects that are worth discussing, a few of them are considered below.

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Cost& Maintains: The cost of the instruments used in conventional monitoring systems is approx. 90,000 USD [3]. A typical air quality measurement station requires about 200,000 USD for construction and 30,000 USD per year for maintenance [9]. The inclusive cost of the sensor

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network is also highly reliant on the sensors category and number of deployed nodes. As such, there is a vital need to make the system cost-efficient. Accuracy: While the expensive monitoring stations are hard to maintain, the data quality and precision is very high. The systems with low cost tools have poor accuracy, so obtaining precision in these systems is a major challenge. 3D Data Attainment: The majority of the systems published in literature are merely capable of monitoring the air quality of the urban surface or roadside, whereas the inevitabilities and significance of the 3-Dimensional air pollution statistics are portrayed in Reference [3]. Satellites-based 3-Dimensional monitoring systems deal with similar issues to conventional monitoring systems. Reference [20] applied efforts to acquire the 3-Dimensional data in real time. However, the system cost and power consumption is very high. Absence of Active Monitoring: The sensing modules in SSN, VSN and ASN systems update the data periodically and are all passive monitoring systems. Active monitoring could offer greater flexibility and quality for the service. Flexibility/Scalability: Literature studies realize that the majority of the existing systems have no ability to add-on hardware & software reconfigurations that are required when the sensing node classes are revised. In practical scenarios with large-scale applications, there are huge numbers of sensor nodes in the system with an add-on ability that are essential in this case. Power Consumption: Power consumption is not a major issue for indoor air monitoring systems other than in the case of cost concerns. But for the outdoor systems and especially for the sensor network-based approaches, power consumption is an important design consideration [25]. The energy sources could be restocked via solar or other methods. As the size of the network grows, this task becomes more difficult and remains an open research challenge.

Hence all the discussed challenges and the issues can be resolved to some extent by using advanced technologies. These issues should be considered carefully while designing air monitoring systems. 3. Proposed Edge Computing Based IoT Architecture for the Air Pollution Monitoring We have designed an edge-computing based IoT architecture for the air pollution monitoring system. As demonstrated in Figure 3, a layered IoT architecture is employed in the monitoring system. Three layers were defined as the sensing layer, network layer and the application layer, respectively [39]. All the layers are communicating via Zigbee and Wi-Fi, although any other similar technology can be used for this purpose. The total work load is balanced and distributed over these three layers according to the edge-computing mechanism [40,41].

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•

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Sensing Layer: This is the basis of the whole monitoring system. The main responsibility of this layer is to sense the air quality. The sensing nodes are the main entities of this layer and can deployed over the wide area. Hardware & software details about these nodes is provided in the next section. Edge Computing Layer: This layer is composed of edge-computing devices (IoT gateways). Its duty is to communicate with the other two layers. ECD gathers the data from the entire sensing layer and after necessary processing, passes the data to the application layer. Application Layer: The application layer is responsible for providing collaborative services to the consumers and the data storage. It can be distributed into two chunks: The IoT cloud (IBM cloud), and user applications. Once it receives the data reported from the edge computing device, it stores the data in the database of the cloud and provides data visualization in numerous ways, as detailed in Section 3.2.

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Application Application Layer Layer

Edge Edge Computing Computing Layer Layer

Sensing SensingLayer Layer

ECD ECD

CLOUD CLOUD SERVICES SERVICES

ECD ECD

Figure System Figure3. Systemarchitecture. architecture. Figure 3.3.System architecture.

3.1. 3.1.System SystemImplementation Implementation 3.1. System Implementation System Systemimplementation implementationcomprises comprisesofofthe thehardware hardwareand andsoftware softwareparts, parts,with withthe thedetailed detailed System implementation comprises of the hardware and software parts, with the detailed description given below. description given below. description given below. 3.1.1. 3.1.1.Hardware HardwarePrototype Prototype 3.1.1. Hardware Prototype The hardware ofofthe system primarily includes the module (SM) and ananedge Thehardware hardware the system primarily includes thesensing sensing module (SM) and edge The of the system primarily includes the sensing module (SM) and an edge computing computing device (ECD), as shown in Figure 4a,b respectively. The sensing node collects the computing (ECD), as shown in Figure The 4a,bsensing respectively. The sensing nodeaircollects the device (ECD),device as shown in Figure 4a,b respectively. node collects the real-time pollution real-time air pollution information and passes this data to the ECD through a wireless channel. A real-time air pollution information and passes this data to the ECD through a wireless channel. information and passes this data to the ECD through a wireless channel. A detailed description ofA detailed description ofofthese modules isispresented as follows. detailed thesetwo two these two description modules is presented as modules follows. presented as follows.

(a) (a)

(b) (b)

Figure Figure4. (a)ECD ECDmodule. module.(b) (b)Sensing Sensingmodule. module. Figure 4.4.(a) (a) ECD module. (b) Sensing module.

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Sensing Figure each equipped SensingModule: Module:As Asshown shownin Figure5, eachSM SMis equippedwith withfour foursub submodules, modules,which whichare are Sensing Module: As shown ininFigure 5,5,each SM isisequipped with four sub modules, which are the sensor block, processing module, communication module and the power module. the sensor block, processing module, communication module and the power module. the sensor block, processing module, communication module and the power module. AA Sensor SensorBlock: Block:To Tomake makethe thesystem systemcost costefficient, efficient,we weuse usethe thelow-cost low-costsensor sensorfor forsmaller smaller A pollutants Sensor Block: To make the system cost efficient, we use the low-cost sensor for smaller and more accurate sensors for the major pollutants present in the air. Deprived pollutants and more accurate sensors for the major pollutants present in the air. Deprived and more accurate sensors forcan the pollutants present in in the Deprived of ofofpollutants the process, these sensors measure the quality seconds. thecomplex complex process, these sensors canmajor measure theair air quality inaair. afew few seconds. the complex these measure thenot air quality in a few seconds. Although Although the precision ofofsensors these sensors may bebe comparable toto conventional Although theprocess, precision these can sensors may not comparable conventional the precision of these sensors may not be comparable to conventional monitoring monitoring stations, it is sufficiently effective to demonstrate the trend of the air quality monitoring stations, it is sufficiently effective to demonstrate the trend of the airstations, quality it is sufficiently effective to demonstrate the trend of the air quality level. This module level. This module is designed to possess six different sensors, GP2Y1014AU0F, DSM501, level. This module is designed to possess six different sensors, GP2Y1014AU0F, DSM501, is designed toSO2-AF, possess different sensors, DSM501, GSNT11, MQ-7, GSNT11, and MiCS2610-11 for the 1010 , PM 2.5MQ-7, , CO, 2, 2SO 2 2 MQ-7, GSNT11, SO2-AF,six and MiCS2610-11 forGP2Y1014AU0F, thedetection detectionofofPM PM , PM 2.5 , CO,NO NO , SO SO2-AF, and MiCS2610-11 for the detection of PM , PM , CO, NO , SO and O and O 3 . Additionally, a DHT11 humidity & temperature sensor was installed to resolve 10 2.5 was installed 2 2to resolve 3. and O3. Additionally, a DHT11 humidity & temperature sensor Additionally, a DHT11 humidity & temperature sensor was installed to resolve temperature temperature temperatureand andthe thehumidity humiditydependency. dependency. and the humidity dependency.

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Figure 5. Sensing Figure 5. Sensing Module. Module.

B B

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Processing Module: Sensors pass their raw data to processing modules such Processing Module: Sensors pass their raw data to processing modules such as asATmega328P.This module performs the necessary processing on the data that is ATmega328P.This module performs the necessary processing on the data that is presented presented in Section 4 as the calibration the power management algorithms. in Section 4 as the calibration the power management algorithms. C Communication Module: The Zigbee/802.15.4 protocol was used to communicate/exchange C Communication Module: The Zigbee/802.15.4 protocol was used to communicate/exchange the data between the sensing module and ECD. XBee S2C 802.15.4 RF Modules are used for the data between the sensing module and ECD. XBee S2C 802.15.4 RF Modules are used this purpose, this module provides quick, robust communication in point-to-point, for this purpose, this module provides quick, robust communication in point-to-point, peer-to-peer, and multipoint/star configurations. The module has the following features: peer-to-peer, and multipoint/star configurations. The module has the following features: 2.4 GHz for worldwide deployment, sleep current of sub 1 μA, with a Data Rate RF of 250 2.4 GHz for worldwide deployment, sleep current of sub 1 µA, with a Data Rate RF of Kbps, a serial of up to 1 Mbps, an indoor/urban range of 200-ft. (60 m) 300-ft. (90 m), and an 250 Kbps, a serial of up to 1 Mbps, an indoor/urban range of 200-ft. (60 m) 300-ft. (90 m), outdoor/RF line-of-sight range of 4000-ft. (1200 m) 2 miles (3200 m). and an outdoor/RF line-of-sight range of 4000-ft. (1200 m) 2 miles (3200 m). D Power Module: The power module consisting of a rechargeable lithium battery and the D control Powermodule. Module:The Thebattery powerismodule consisting of a rechargeable battery the connected to the control module thatlithium converts 9 v to and 5 v and control module. The battery is connected to the control module that converts 9 v to 5v 3 v. This can provide a safe and stable power supply for other sub modules. and 3 v. This can provide a safe and stable power supply for other sub modules. Edge Computing Device: As shown in Figure 3, the ECD in on the second layer that is the Edge Computing As using shownthe inArduino Figure 3,platform. the ECDFor in the on purpose the second layer thatand is the network layer and Device: is obtained of simplicity to network layer and is obtained using the Arduino platform. For the purpose of simplicity and to keep the costs low, this platform is meeting the current prototype requirements, although any keep the costs low, this platform is meeting the current prototype requirements, although any other advance and more powerful board can be used for device development. Block diagram of other advance andin more powerful board can be are used for device development. Block diagram of the ECD is shown Figure 6 and sub modules described below. the ECD is shown in Figure 6 and sub modules are described below. A Processing module: This module is responsible for calculating the AQI, data analysis and management. To prototype, the local data-based SD card was integrated with a A power Processing module: This module is responsible for calculating the AQI, data analysis and device that stores hourly data, with only the daily AQI or AQI sliding window being power management. To prototype, the local data-based SD card was integrated with a posted the stores cloud,hourly as it saves andthe communication bandwidth. system is devicetothat data, power with only daily AQI or AQI sliding This window being scalable and can be configured to multiple modes, including daily, hourly, and sliding posted to the cloud, as it saves power and communication bandwidth. This system is windows A be sliding window is where the AQI will bedaily, posted to theand cloud only scalablemodes. and can configured tocase multiple modes, including hourly, sliding when it varies enough to change the case range is depicted intoFigure 1. Mode windows modes. A sliding window is window where thewhich AQI will be posted the cloud only selection is dependent upon the application and user demand. when it varies enough to change the range window which is depicted in Figure 1. Mode B Sensor Module: This module contains low cost and electrochemical sensors from the MQ series selection is dependent upon the application user demand. (MQ-135, MQ-6, MQ-7, MQ-9) contains for measuring indoor pollutants sensors and hazardous B Sensor Module: This module low cost electrochemical from thegases MQ [10,35]. The GP2Y1014AU0F, was installed to measure the dust & particle matters. series (MQ-135, MQ-6, MQ-7, MQ-9) for measuring indoor pollutants and hazardous Additionally, DHT11 humidity and temperature sensor the was installed to matters. resolve gases [10,35].a The GP2Y1014AU0F, was installed to measure dust & particle temperature and humidity dependency. Additionally, a DHT11 humidity and temperature sensor was installed to resolve C Communication Modules: ECDdependency. device include two communication modules, one is an temperature and humidity XBee S2C module to communicate with SM and the other is a Wi-Fi ESP8266 used to C Communication Modules: ECD device include two communication modules, one is an XBee interface with the cloud platform. S2C module to communicate with SM and the other is a Wi-Fi ESP8266 used to interface with the cloud platform.

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Figure 6. Edge computing device. Figure 6. Edge computing device.

Figure 6. Edge computing device. 3.2. Software Implementation 3.2. Software Implementation 3.2. Software Implementation 3.2. Software The large Implementation number of software programs are required for both the IoT cloud and the client so The large number of software programs areare required for the IoT cloud and the client so so that The large number of software programs required forboth both theof IoT cloud in and the client that consumers can enjoyoffull services. There are three primary kinds servers the IoT cloud with The large number software programs are required for both the IoT cloud and the client so consumers can enjoy services. There areare three primary kinds ofservers servers the cloud with thatfunctionalities, consumers canfull enjoy full services. There three primaryServer, kinds of in in the IoTIoT cloud with diverse i.e., Data Processing Server, Storage and HTTP Server. We progress that consumers can enjoy full services. There are three primary kinds of servers in the IoT cloud with diverse functionalities, i.e.,i.e., Data Processing Server, Server.We Weprogress progress the diverse functionalities, Data Processing Server,Storage StorageServer, Server, and and HTTP HTTP Server. the diverse design functionalities, considerations a real-time i.e.,and Dataplan Processing Server,ample Storageair-quality Server, andlevel HTTPindication Server. Wescheme, progress which the design considerations and plan a real-time ample air-quality level indication scheme, design considerations and plan a real-time ample air-quality level indication scheme, whichwhich efficiently the design considerations and plan IBM a real-time ample level indication scheme, which efficiently manages vibrant changes. Bluemix was air-quality used to implement these services. The overall efficiently manages vibrant changes. IBM Bluemix was used to implement these services. The overall manages vibrant changes. IBM Bluemix was used to implement these services. The overall software efficiently manages vibrant changes. IBM Bluemix was used to implement these services. The overall software architecture is described ininFigure software architecture is described Figure7. 7. architecture is described in Figure 7. software architecture is described in Figure 7. • IoT Platform andand Cloudant IoT platform platformcommunicates communicates with and collects • IoT Platform CloudantData DataBase: Base: The The IoT with ECDECD and collects IoT Platform and CloudantData Data Base: Base: The IoT platform communicates withwith ECD ECD and collects The IoT platform communicates and • • IoT Platform and Cloudant the data from it. Itit.uses the built-in dashboardstoto screen data analyze itcollects in real the data from It uses the built-inweb web console console dashboards screen data andand analyze it in real the data from it. It uses the built-in web console dashboards to screen data and analyze it in real the data from it.can It uses the built-in web console dashboards to triggering screen data and analyze itinclude in real time. Users define rules formonitoring monitoring circumstances and actions that that include time. Users can define rules for circumstances and triggering actions time. Users can define rules for monitoring circumstances and triggering actions that include alerts, email notifications, flows, circumstances and other services quickly to dangerous time. Users can define rulesNode-RED for monitoring andreacting triggering actions include alerts, email notifications, flows, andother other services reacting quickly tothat dangerous alerts, email notifications, Node-RED Node-RED flows, and services reacting quickly to dangerous changes. Figure 8 provides a diagram of the architecture. alerts, email notifications, Node-RED flows, and other services reacting quickly to dangerous changes. Figure ofthe thearchitecture. architecture. changes. Figure8 8provides provides aa diagram diagram of changes. Figure 8 provides a diagram of the architecture.

Figure 7. System software architecture. Figure 7. System software architecture. Figure 7. System software architecture.

Figure 7. System software architecture.

Figure 8.IoT IoTarchitecture architecture employing cloud services. Figure employing cloud services. Figure 8. 8. IoT architecture employing cloud services.

Figure 8. IoT architecture employing cloud services.

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Cloudant Data Base: Offers access to a NoSQL JSON data layer. This is compatible with CouchDB Cloudant Data Base: Offers accessinterface to a NoSQL JSON data layer. This is compatible and is manageable through the HTTP for web application models. Every document inwith the CouchDBisand is manageable through interface for web application models. Every database accessible as JSON via a URL,the andHTTP data can be retrieved, stored, or deleted individually document the sets database is accessible as JSON the via air a URL, and data can Analytical be retrieved, stored, or or in bulk. in Data were analyzed to disclose pollution trends. results were deletedinindividually or inbase. bulk. Data sets were analyzed to disclose the air pollution trends. stored the NoSQL data Analytical results were stored in theorNoSQL data base. User Application: Either a website a mobile application can be used to exhibit the air quality User to Application: Either a website a mobile be used exhibit air quality facts the consumer. Watson studioor was used toapplication explore air can quality data to and createthe visualizations facts to the consumer. Watson studio was used to explore air quality data and create for the end user. Watson Studio offers a collection of tools and a cooperative environment for visualizations the end user. Watson specialists. Studio offersUsing a collection of tools and a cooperative data scientists,for developers and domain the presentation applications, the environment data scientists, developers and domain specialists. AQI could befor displayed in real-time, e.g., current AQI indication andUsing trendsthe forpresentation the present applications, the AQI be displayed e.g. current AQI can indication trendsin fora day/week/month. In could addition, the trendsin forreal-time, the individual pollutants also be and displayed the present day/week/month. addition, theinclude trendscurrent for thestatus, individual can also be graphical view. Other availableIn visualizations recentpollutants events, and data logs, displayed in a graphical view. Other available visualizations include current status, recent an example is shown in Figure 9. events, and data logs, an example is shown in Figure 9.

Figure platform for for data data visualization. visualization. Figure 9. 9. IBM IBM Watson Watson IoT IoT platform

4. Data Processing 4. Data Processing 4.1. 4.1. Pre-Calibration Pre-Calibration Characteristics production lot to Characteristics of of the the low-cost low-cost sensor sensor differ differ from from sensor sensor to to sensor sensor and and from from production lot to production lot. Thus, every sensor needs pre-calibration to accurately measure gas capacity. production lot. Thus, every sensor needs pre-calibration to accurately measure gas capacity. Algorithm shows steps for pre-calibration, here coefficient a and b are extrapolated from the 1Algorithm shows the1steps forthe pre-calibration, here coefficient a and b are extrapolated from the curves provided curves provided in sheet, the sensors data sheet, for example Figure 10 shows a characteristic forfrom the in the sensors data for example characteristic curve for the MQ 135 sensor can becure taken MQ 135 sensor taken from Reference [42]. Reference [42].

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Algorithm 1: Pre-Calibration for Gas Sensors Calculation of R0 (sensor resistance in the clean air) Calculation of Rs (sensor resistance presence of certain gas) Analog read sensor pin Take multiple samples and calculate the average (S) R0 = S/clean air factor Extrapolate coefficients a and b  b Rs 7: Calculate ppm, ppm = a × R 0

1: 2: 3: 4: 5: 6:

4.2. Auto Calibration (Temperature & Humidity Dependency) Low cost sensors are often affected by temperature and humidity, as described in their data sheets [42–45]. Therefore, pre-calibrated values require to be adjusted with respect to the temperature and humidity dependency to ensure sensing accuracy [25]. Algorithm 2 details how to apply the auto-calibration. The calibrated value of the gas sensors Cv , is calculated as: Cv =

Rs × DTH R0

(1)

where Rs is the sensor resistance in the presence of a certain gas, R0 is the sensor resistance in clean air, and DTH is the temperature and humidity dependence value for the calibration. The ratio of the sensor resistances, Rs/R0 , is obtained by: Rs Am = ( Rl × ) − Rl R0 Av

(2)

where Rl is the external resistance, temperature and the humidity dependence value for the calibration, DTH , can be obtained by: DTH = γt2 − t + δ (3) where t is the current temperature and δ is the humidity dependency value. Algorithm 2: Auto Calibration for Temperature and the Humidity Dependency Cv (Calibrated data of the gas sensors), t (Temperature), Hr (Humidity Value), Av (Current Analog read value of gas sensor), Rl (The external load resistance), Rs (resistance ratio) Am (The maximum analog read value of a), R 0 1: Read sensors data (Av , t, and Hr ) 2: Convert the measured values to dependency values (Hr , γ, and ψ) 3: Calculate the value of temperature and humidity dependency, DTH with Equation (3) Rs 4: Calculate the R using (2) 0 5: Calculate the calibrated value Cv using (1)

4.3. Data Smoothing Algorithm According to Reference [46], the measurements that significantly deviate from the normal pattern of the measured data are called outliers. They need to be detected and removed to obtain the accurate data. A data smoothing algorithm is employed to filtering out this noise. To smooth the gas sensor data, important data trends were recorded in a repeated statistical manner and standard deviation with ±3σ is used for limitation. Algorithm 3 explains the smoothing algorithm.

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Algorithm 3: Sensor Data Smoothing 1: 2: 3: 4: 5: 6: 7:

Rs (Sensor Resistor) Rl (Load Resistor) Rs /Rl if Rs /Rl ≥ ±4£(Rs /Rl ) then Perform filtration else Keep Rs /Rl end if

4.4. Data Transmission Strategy An Algorithm 4 was implemented to reduce the power consumption at the sensing node and the ECD. The key idea behind this algorithm is that the data will only be transferred if it is useful. The sensing node only transmits the data to ECD if the measured value was significantly different to the previous value and the amount of difference is specified by the ∆. The ECD calculates the AQI and updates it to the cloud only if the AQI changes its window. If the variation of the AQI is within the same window, it will not be posted to the cloud (on an hourly update case), For example if the AQI varies from 51 to 100, it remains in the moderate window and if the value crosses the limit and moves to the next window, it will be updated. Algorithm 5 describes the multiple SM scenarios and the inclusion of their individual AQI to enhance the data accuracy. Algorithm 4: Data Transmition Strategy for ECD and SN For ECD IAQ.t = max{I1 , I2 , I3 . . . . . . . . . .In } IAQ.tIAQ.tIAQ.t (Overall Air quality index at time t) 1: if IAQ.t = IAQ.t−1 2: then Don’t send to cloud/user app 3: else 4: if IAQ.t 6= IAQ.t−1 5: if IAQ.t > αIAQ.t−1 kIAQ.t < βIAQ.t−1 6: then Update the AQI on the cloud/user end For SN − Il In = CBIhh − CBl (Ct − CBl ) + Il In.t (Overall Air quality index at time t) 1: if In.t = In.t−1 2: then Don’t send data to ECD 3: else 4: if In.t 6= In.t−1 5: if In.t > αIn.t−1 kIn.t < βIn.t−1 6: then Update the AQI on the cloud/user end

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Algorithm 5: Enhancing the Efficiency of the Overall AQI IAQI.1 = max{I1 , I2 , I3 . . . . . . . . . .In } IAQI.N (Overall Air quality index from node N at time t) 1: Average calculation of individual AQI of each pollutant ........IN In = Ia +IbN 2: Calculation of overall AQI IAQI.1 3: Average calculation of AQI from each Sensing Node IAQI.N ........IN IAQI.2 = Ia +IbN 4: if IAQI.2 >IAQI.1 5: then: IAQI =: IAQI.2 6: else 7: if IAQI =: IAQI.1

4.5. Power Consumption Analysis & Computational Cost The energy consumption used by the whole system is the addition of the energy consumed by each task. The average power is computed by considering the consumed power and the application specific period. Pbasic is the power consumption in the “idle” state. The execution time of the specific task τtask is estimated by direct measurements or by timing estimation tools. Below is the power consumption analysis for a single sensing node over one day with and without applying a system model. Here PSensors is the power consumed by the pollution sensors and has a fixed value. PProcesing represents the total processing power of the Arduino. PCommunication consists of the power required to communicate with ECD. In our case, it only counts the number of transmissions from sensing the module to ECD. XBee module requires 1 mw of transmission power denoted as ψ. Arduino consumes an average power of 734 mw denoted as Pbasic . In Equation (6) τtask is the execution time of the particular task. TPCS.N = PSensors + PProcesing + PCommunication

(4)

PProcesing < PCommunication < PSensors

(5)

PProcesing = Pbasic × τtask

(6)

n

PSensors =

∑ Si

(7)

i =1 n

PCommunication =

∑ Ti + Ri

(8)

i =1

The measurement interval depends upon the type of sensor, the response time and the sensing algorithm. The response time is different for the different sensors, as summarized in Reference [3]. We took the measurements every second for particular matter, and every minute for the gases. In the daily case, the individual AQI for CO & O3 in ppm (parts per million) is calculated over 8 h, for SO2 & NO2 in ppb (parts per billion) over 1 h and finally the PM concentration in µg/m3 is calculated over 24 h. Therefore the number of transmissions were calculated accordingly, with 86,400 (transmission every second, for 24 h), 480 (transmission every minute for 8 h), and 60 (transmission every minute for 1 h). Case1 (without employing the designed methodologies): Here £PM2.5 is the number of transmissions required for PM2.5 , £PM10 for PM10 and αCO , α NO2 , αSO2 , αO3 for CO NO2 SO2 O3 respectively. A: Hourly. TNTS.N = £PM2.5 + £PM10 + αCO + α NO2 + αSO2 + αO3

(9)

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TNTS.N = (60 × 60 ) + (60 × 60) + (1 × 60) + (1 × 60) + (1 × 60) + (1 × 60) TNTS.N = 3600 + 3600 + 60 + 60 + 60 + 60 TNTS.N = 7440 ( per hour ) Total PowerS.N = 24 × 7440( per day) Total PowerS.N = 178, 560 ψ B: Daily. TNTS.N = £PM2.5 + £PM10 + αCO + α NO2 + αSO2 + αO3

(10)

TNTS.N = (24 × 60 × 60) + (24 × 60 × 60) + (8 × 60) + (8 × 60) + (1 × 60) + (1 × 60) TNTS.N = 86, 400 + 86, 400 + 480 + 480 + 60 + 60 TNTS.N = 173, 880 Total PowerS.N = 173, 880 ψ Case2 (with designed methodologies): here CPM2.5 is the number of transmissions required for PM2.5 concentration, CPM10 for PM10 and ρCO , ρ NO2 , ζ SO2, ζ O3 for CO NO2 SO2 O3 respectively. A: Hourly. TNTS.N = CPM2.5 + CPM10 + ρCO + ρ NO2 + ζ SO2 + ζ O3

(11)

TNTS.N = (1 × 24) + (1 × 24) + (1 × 24) + (1 × 24) + (1 × 24) + (1 × 24) TNTS.N = 144 Total PowerS.N = 144 ψ ( per day) B: Daily. TNTS.N = CPM2.5 + CPM10 + ρCO + ρ NO2 + ζ SO2 + ζ O3

(12)

TNTS.N = 1 + 1 + 1 + 1 + 1 + 1 TNTS.N = 6 Total Consumed PowerS.N = 6 ψ Total power saved ( A) = 178, 416 ψ Total power saved ( B) = 173, 878 ψ The communication consumes around 35% of the total power, with a designed strategy this consumption reduces to 10% (25% reduction) at a cost of a small increase in processing power consumption (2% increase), hence saving 23% of the total power per node. The total computational burden of the system primarily consists of five algorithms presented in Section 4. To balance the load between the sensing node and the ECD algorithm, 1–3 run on sensing node while 4 & 5 incorporated by the ECD that reduce the computational burden of the sensing node up to 70% approx. and saves significant power as mathematically described above. 5. Experimental Evaluation In order to evaluate the effectiveness of the system, the sensing module, edge-computing device and IBM platform were integrated. As a metric to indicate the air quality, AQI “I AQI ” is calculated by measuring six main pollutants as mentioned in Section 3.1.1. The critical points for the six air pollutants

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are given by China’s Ministry of Environmental Protection and other bodies in References [11,47,48]. A discrete score In is allotted to the level of each pollutant as calculated by Equation (13) and the absolute AQI (I AQI ) is the highest of them as described by the Equation (14). In =

Ih − Il (Ct − CBl ) + Il CBh − CBl

I AQI = max{ I1 , I2 , I3 . . . . . . . . . .In }

(13) (14)

In : Air-Quality Index of the Nth pollutant Ct : Truncated concentration of the Nth pollutant CBl : The concentration breakpoint that is ≤Ct CBh : The concentration breakpoint that is ≥Ct ll : AQI w.r.t CBl lh : AQI w.r.t CBh According to the above equations I AQI will be the individual AQI i.e., In of that particular pollutant which acquires the highest value. According to the historical statistics of official AQI data, the major and dominant pollutant is PM2.5 [13,49]. IPM (Discrete AQI of PM2.5 and IGs (discrete AQIs of other pollutants) are related as IPM  IGS . Keeping these statistics in mind, this prototype selected the three dominant pollutants PM2.5, PM10 & CO to calculate the outdoor AQI. However the SM is scalable and can measure all six pollutants according to the requirement of the sensitive scenario. This selection reduces the cost of the sensing node without significantly affecting the data accuracy, as including more pollutants enhances the cost of the individual node and overall system [24]. The overall functionality of the system was demonstrated by conducting the experiments in different settings—a living room (small), office (medium), and an open environment. In the small-size living room (5 m × 3.5 m) it was estimated that one sensing node was adequate, which was placed in the middle side of the room at a height of 1.7 m. For the open environment, the effective height of the node was kept at 9 m. Results and Discussions To examine the viability of the system and the employed algorithms, measurements were taken with and without flattening the calibration and accumulation algorithms. MQ-135 could measure various gases as described in [42], for testing purpose it was calibrate to measure CO2 only. CO2 was chosen to demonstrate the effectiveness of the calibration algorithm due to the ease of the experimental setup and its plentiful availability in the target area. The data was collected over six hours in an office setting (13–17 employees), and Figure 10 depicts the results. Initially there were only four persons, with the passage of the time, the number of persons in the office start to increase, and the ascending level of CO2 indicate the sensor’s ability to detect that change. The CO2 ppm level reached its maximum value when around 15 persons were present in the office, as shown in the graph below. During the break time, CO2 ppm remained in its lower range, and data trends with time and scenario verified the calibration’s effectiveness. Outliers frequently occurred in real-time measurements and they needed to be detected and eliminated. Another test was performed to validate the smoothing algorithm in a small room (single cubical) setting over one hour. Minor variations were observed in Vout as numbers of persons were quite small as compare to office setting (1 vs. 15). Figure 11 shows the data measurements, graph contain raw data from the sensor with outliers. After applying the smoothing algorithm we get the clean data and outliers have been effectively removed.

measurements and they needed to be detected and eliminated. Another test was performed to validate the smoothing algorithm in a small room (single cubical) setting over one hour. Minor variations were observed in Vout as numbers of persons were quite small as compare to office setting (1 vs. 15). Figure 12 shows the data measurements, graph contain raw data from the sensor with Sensors 2018,After 18, 3021 of 23 outliers. applying the smoothing algorithm we get the clean data and outliers have15been effectively removed. 450 400 350

CO CO22 (ppm) (ppm)

300 250 200 Maximum employes present in the office.

150 100

Break time when few of them were present in the office.

50 0

09:00 AM

After break time, number of people start increasing.

12:00 PM

03:00 PM Time

Figure measurements over over six six hours hours in in the the office office setting. setting. Figure 10. 11. CO CO222 measurements

represents the the value value of of V Vout out remaining the same before and after the smoothing In the graph, x represents algorithm. Data Data isisplotted plottedininthe the same graph purely comparison, as smoothing the smoothing algorithm same graph purely for for comparison, as the algorithm does doesaffect not affect the readings the abnormal onesare that are indicated the arrowheads. not the readings exceptexcept the abnormal ones that indicated by theby arrowheads.

1000

Outlie

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Without Smothing With Smoothing 18:12:13 18:12:13 18:12:45 18:12:45 18:13:17 18:13:17 18:13:49 18:13:49 18:14:21 18:14:21 18:14:53 18:14:53 18:15:24 18:15:24 18:15:56 18:15:56 18:16:28 18:16:28 18:17:00 18:17:00 18:17:32 18:17:32 18:18:04 18:18:04 18:18:36 18:18:36 18:19:08 18:19:08 18:19:40 18:19:40 18:20:12 18:20:12 18:20:44 18:20:44 18:21:16 18:21:16 18:21:48 18:21:48 18:22:20 18:22:20 18:22:52 18:22:52 18:23:24 18:23:24 18:23:56 18:23:56 18:24:28 18:24:28 18:25:00 18:25:00 18:25:32 18:25:32 18:26:04 18:26:04 18:26:36 18:26:36 18:27:08 18:27:08 18:27:40 18:27:40 18:28:12 18:28:12 18:28:44 18:28:44 18:29:16 18:29:16 18:29:48 18:29:48 18:30:20 18:30:20 18:30:52 18:30:52 18:31:24 18:31:24 18:31:55 18:31:55 18:32:27 18:32:27 18:32:59 18:32:59 18:33:31 18:33:31 18:34:03 18:34:03 18:34:35 18:34:35 18:35:07 18:35:07 18:35:39 18:35:39 18:36:11 18:36:11 18:36:43 18:36:43 18:37:15 18:37:15 18:37:47 18:37:47 18:38:19 18:38:19 18:38:51 18:38:51 18:39:23 18:39:23 18:39:55 18:39:55 18:40:27 18:40:27 18:40:59 18:40:59 18:41:31 18:41:31 18:42:03 18:42:03 18:42:35 18:42:35 18:43:07 18:43:07 18:43:39 18:43:39 18:44:11 18:44:11 18:44:43 18:44:43 18:45:15 18:45:15 18:45:47 18:45:47 18:46:19 18:46:19 18:46:51 18:46:51 18:47:23 18:47:23 18:47:55 18:47:55 18:48:26 18:48:26 18:48:58 18:48:58 18:49:30 18:49:30 18:50:02 18:50:02 18:50:34 18:50:34 18:51:06 18:51:06 18:51:38 18:51:38 18:52:10 18:52:10 18:52:42 18:52:42 18:53:14 18:53:14 18:53:46 18:53:46 18:54:18 18:54:18 18:54:50 18:54:50 18:55:22 18:55:22 18:55:54 18:55:54 18:56:26 18:56:26 18:56:58 18:56:58 18:57:30 18:57:30 18:58:02 18:58:02 18:58:34 18:58:34 18:59:06 18:59:06 18:59:38 18:59:38 19:00:10 19:00:10 19:00:42 19:00:42 19:01:14 19:01:14 19:01:46 19:01:46 19:02:18 19:02:18 19:02:50 19:02:50 19:03:22 19:03:22 19:03:54 19:03:54 19:04:26 19:04:26 19:04:58 19:04:58 19:05:29 19:05:29 19:06:01 19:06:01 19:06:33 19:06:33 19:07:05 19:07:05 19:07:37 19:07:37 19:08:09 19:08:09 19:08:41 19:08:41 19:09:13 19:09:13 19:09:45 19:09:45 19:10:17 19:10:17 19:10:49 19:10:49

200

Figure 12. 11. Sensors raw sata with and without smoothing algorithm.

For the theairair pollution monitoring evaluation, measurements have been conducted in the For pollution monitoring evaluation, measurements have been conducted in the dormitory dormitoryofbuilding of the FUDAN from University from April2018. to 5 We Mayselected 2018. We selected 15 building the FUDAN University 21 April to 21 5 May 15 consecutive consecutive days and 24 readings each day (every hour) to determine the consistency of the days and 24 readings each day (every hour) to determine the consistency of the measurement. measurement. Every monitoring instance considered different and weather Every monitoring instance considered different environmental and environmental weather parameters, such as parameters, such as temperature, relative humidity, and wind speed. temperature, relative humidity, and wind speed. reliability of of Air monitoring monitoring data data counting counting AQI AQI for for PM PM2.5 isis shown Air shown in in Figure Figure 13. 12. To To validate validate the the reliability 2.5 the system, system, additional data were PM2.5 databases 2.5 the additional data sets weresets acquired fromacquired recognizedfrom PM2.5 recognized databases http://www.younghttp://www.young-0.com/airquality/ [49]. These datasets are compared in Figures 13 and 14 for 0.com/airquality/ [49]. These datasets are compared in Figures 12 and 13 for outdoor and indoor outdoor and indoor environments respectively. environments respectively.

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Figure AQI compared compared with with official official data, data, from from 21 21 April April to to 55 May May 2018. 2018. 2.5 AQI Figure 12. 13. Outdoor Outdoor measured measured PM PM2.5 2.5

Figure14. 2.5 Figure 13. Indoor Indoor measured measured PM2.5 AQIvalue valuecomparison comparison with with official official data, data, for 15 days from 21 April 2.5AQI to 5 May May,2018. 2018.

It is is apparent apparent from from Figure Figure 14 15 that that the the trends trends of of the the AQI AQI lines lines accord accord well well when when measured measured with with It multiple sensing nodes as compared to the single node case. Although the data trends were multiple sensing nodes as compared to the single node case. Although the data trends were comparable comparable within official datanode in the(Nsingle node (N the = 1)gap scenario, the gapisand disparity is the higher than with official data the single = 1) scenario, and disparity higher than in multiple in the multiple node (N = 2) case. It is clear that the sensing efficiency improves with the involvement node (N = 2) case. It is clear that the sensing efficiency improves with the involvement of more sensing of more sensingitnodes because it enhances thedata quality of the data realizing drift and nodes because enhances the quality of the by realizing theby sensor drift the andsensor enhancing the enhancingcapability. the coverage capability. Additionally, the multiple node approach somehow the coverage Additionally, the multiple node approach somehow combats the combats issue of the issue of the non-uniform pollution density. The discrepancy between the measurements and non-uniform pollution density. The discrepancy between the measurements and reference data reference data be due to several such asmeasurement the different measurement locations, respective may be due to may several factors, such asfactors, the different locations, respective environment, environment, data acquisition techniques and the type of the incorporated sensors. data acquisition techniques and the type of the incorporated sensors. Figure 15 16 shows shows the the AQI AQI trends trends measured measured in in the the indoor indoor environment. environment. It It can can be be seen seen that that these these Figure measurements have to the the fact fact that that measurements have less less discrepancy discrepancy as as compared compared to to the the outdoor outdoor areas, areas, this this is is due due to environmental factors have less effects in the closed setting. Consequently, the air quality environmental factors have less effects in the closed setting. Consequently, the air quality information information acquired by thissystem monitoring system was ablethe to determine thereliability accuracy necessities. and reliability acquired by this monitoring was able to determine accuracy and necessities. PM2.5 concentrations are greatly linked to some of the atmospheric properties like wind speed, PM2.5 2.5 concentrations are greatly linked to some of the atmospheric properties like wind speed, temperature and humidity. The effect of these parameters on PM2.5 concentrations was observed, 2.5 concentrations was observed, temperature and humidity. The effect of these parameters on PM2.5 Figure 17 display the relative humidity and temperature for the under-observation days. Figure 18

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plots the among humidity PM2.5 2.5 and and the temperature wind speed. for It can observed that from April 17 to Figure 16 associations display the relative the be under-observation days.28 Figure Sensors 2018, 18, x 17 of 23 2.5 concentrations 2 May PM2.5 increased progressively the wind plots the the associations among PM2.5 and the wind speed. It canwith be observed that speeds from 28 reaching April to approximately 3.5 m/s. Afterward the speeds dropped gradually and held in a lower range, the 2.5 associations amongincreased PM2.5 and progressively the wind speed. It can observed from 28 April to was 2 Mayplots the PM concentrations with the be wind speedsthat reaching approximately owing to the dispersal of the pollutants by strong winds [36]. It is possible to conclude thattoAQI 2 May the PM2.5theconcentrations increased progressively the wind speeds reaching 3.5 m/s. Afterward speeds dropped gradually and held with in a lower range, was owing the approximately 3.5 speed m/s. Afterward speeds dropped gradually and held in a lower range, was 2.5 is a major depends on variation to some extent PM 2.5 participant ofdepends the AQI. dispersal of the the wind pollutants by strong the winds [36]. It isas possible to conclude that AQI on the

the dispersal of the pollutants by strong winds [36]. It is possible to conclude that AQI windowing speedto variation to some extent as PM2.5 is a major participant of the AQI.

N=1

200 150

AQI

AQI

depends on the wind speed variation to some extent as PM2.5 is a major participant of the AQI. Outdoor Real-time Air Quality Index (AQI) (Hourly) AQI 300 Refrence Outdoor Real-time Air Quality Index (AQI) (Hourly) AQI 250 300 Refrence N=1 200 250

100 50

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Figure 15. between the withsingle single andmultiple multiple sensing nodes =1& 14. Comparison between the measurements with and single and sensing multiple sensing nodes Figure 15.Comparison Comparison between themeasurements measurements with nodes (N = (N 1& = 2) 21–30 April 2018. N ==2)1Nfrom April 2018. (N & N from =21–30 2) from 21–30 April 2018.

250250

Indoor Real-timeAir AirQuality Quality Index Indoor Real-time Index(AQI) (AQI)(Hourly) (Hourly)

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50 0 0 April 21 22 23 24 25 26 27 28 29 30 April 21 22 23 24 25 26 27 28 29 30 Figure 16.Comparison between the measured and reference data on overall AQI (Indoor) from 21–30 April 2018.

0 April

0.0

and measurements. 22 Figure 2317.Relative 24 humidity 25 26 temperature 27 28 29 30 Day

Figure 16. 17.Relative Figure Relative humidity humidity and and temperature temperature measurements. measurements.

Temprature ( ooC)

Temprature ( oC)

Humidity %

Humidity %

15. Comparisonbetween betweenthe themeasured measured and and reference reference data data on on overall overall AQI AQI (Indoor) from 21–30 Figure 16.Comparison April 2018. April 2018. 100 35.0 30.0 80 100 25.035.0 60 20.030.0 80 15.025.0 40 60 10.020.0 Humidity % 20 5.0 15.0 Temprature 40 0 0.0 10.0 Humidity % April 22 23 24 25 26 27 28 29 30 20 5.0 Temprature Day

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Figure Effect concentration (microgram (microgram per per cubic cubic meter). meter). 2.5 concentration concentration (microgram per cubic meter). Figure 17. 18. Effect Effect of of the the wind wind speed speed on on PM PM2.5 2.5 Figure 18. of the wind speed on PM

Figure 19 19 describes the the impact impact of of the the number number of of sensing sensing nodes nodes and and their their positioned positioned height height on on 18 describes Figure the overall overall deployment cost. The cost ratio ranges from toto20. 20. The addition of of thethe sensing nodes to overall deployment deployment cost. cost.The Thecost costratio ratioranges rangesfrom from11 1to 20.The The addition sensing nodes the addition of the sensing nodes to enhance the efficiency has a lower deployment cost as compared to the addition of sinks/ECD that to enhance efficiency a lower deployment as compared to the addition of sinks/ECD enhance thethe efficiency hashas a lower deployment costcost as compared to the addition of sinks/ECD that are equipped withwith pollution sensors. Our Our monitoring system has designs designs corresponding to the the that equipped are equipped pollution sensors. monitoring system has designs corresponding to are with pollution sensors. Our monitoring system has corresponding to mono-sink case.case. Graph shows thatthat as the the number of sensing sensing nodes increases, they cause rise in the mono-sink Graph shows as the number of sensing nodes increases, they cause a rise mono-sink case. Graph shows that as number of nodes increases, they cause aa rise in deployment costs. Similarly, the node’s deployment height also plays an important role in cost in deployment costs. Similarly, node’s deploymentheight heightalso alsoplays playsan animportant important role role in in cost deployment costs. Similarly, thethe node’s deployment effectiveness. Trends Trendsshown showninin inFigure Figure1819 19 conclude the directly proportional relation between cost conclude thethe directly proportional relation between cost cost and effectiveness. Trends shown Figure conclude directly proportional relation between and the number number of sensing sensing node plus their placement height. the number of sensing nodenode plus plus theirtheir placement height. and the of placement height. 35 35 30 30 25 25 20 20 15 15 10 10 55 00

Deployment Cost Cost Deployment Height (m) (m) Height

20 20 15 15

Height Height

Cost CostRatio Ratio

25 25

10 10 55 00 11

22

33

44 55 66 77 Number of of Sensing Sensing Nodes Nodes Number

88

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18. Relationship cost. Figure 19. 19. Relationship between between number number of of the the sensing sensing nodes, nodes, node node height height and and the the deployment deployment cost. Figure

The impact impact of of the the sensing sensing node node height height on the the deployment deployment cost and data accuracy was studied. The height on deployment cost cost and and data data accuracy accuracy was was studied. studied. For the data collection, experiments were conducted on the different floors of the international For the thedata datacollection, collection, experiments were conducted ondifferent the different of the international experiments were conducted on the floors floors of the international student student dormitory building FUDAN University University Handan The Campus. The campus has 23afloors floors and aa student dormitory FUDAN Handan Campus. The campus has 23 and dormitory building building FUDAN University Handan Campus. campus has 23 floors and total height total height 90 of approx. approx. 90 m. m. Figure 20the reveals theofeffects effects of the the optimal optimal deployment height i.e.,major near total height of 90 reveals the of deployment i.e., near of approx. m. Figure 19 Figure reveals20 effects the optimal deployment height height i.e., near major pollutants. This study is helpful in node positioning, and to estimate where it will provide the major pollutants. Thisisstudy is helpful node positioning, and to estimate it will the provide the pollutants. This study helpful in nodeinpositioning, and to estimate where itwhere will provide accurate accurate value. From the the trends, wethat observed that pollution concentration concentration tends toground rise near near the accurate value. From we observed that pollution tends rise value. From the trends, we trends, observed pollution concentration tends to rise near theto 1–20the m ground 1–20 m and then start decreasing gradually. At high altitudes, pollution sources are more ground 1–20 m and then start decreasing gradually. At high altitudes, pollution sources are more and then start decreasing gradually. At high altitudes, pollution sources are more dispersed and dispersed and have have low low concentration concentration asthe compared to the ground ground level, especially in case case of particle particle dispersed and as compared the level, especially in of have low concentration as compared to groundto level, especially in case of particle matters [24]. matters [24]. Figure 21 shows the overall deployment cost depending on the measurement height, it matters [24]. Figure 21 shows the overall deployment cost depending on the measurement height, it Figure 20 shows the overall deployment cost depending on the measurement height, it was observed was observed that the deployment cost is nominal when the nodes height is near the effective release was that thecost deployment costwhen is nominal whenheight the nodes height near therelease effective release that observed the deployment is nominal the nodes is near the is effective height of height of of pollution pollution sources, which was found to be be around 9m. This is is explained explained by thethe factpollution that the the height sources, found to around 9m. This the fact that pollution sources, which waswhich foundwas to be around 9 m. This is explained by the factby that pollution concentration becomes high when being near to to the the pollutant active release height, along pollution concentration becomes being near pollutant release along concentration becomes high whenhigh beingwhen near to the pollutant active releaseactive height, alongheight, with this the with this the pollutants have a greater tendency to drop than to rise because of gravitational effects with this the pollutants a greater tendency drop than toofrise because ofeffects gravitational pollutants have a greaterhave tendency to drop than totorise because gravitational [23,24]. effects [23,24]. [23,24].

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PM PM2.5 (µg/m33)) 2.5 (µg/m

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Figure concentration. Figure 20. the node deployment height on PM 2.5 Figure 19. 20. Effect Effect of of the the node node deployment deployment height height on on PM PM2.5 2.5 concentration. concentration.

Deployment DeploymentCost Costratio ratio

The incorporation incorporation ofof the the more pollutants enhances thethe deployment costcost significantly. As The the deployment cost significantly. As incorporation of themore morepollutants pollutantsenhances enhances deployment significantly. demonstrated in Figure 21, more pollutants results in a larger number of related sensors. Further demonstrated in Figure 21, more pollutants resultsresults in a larger of related Further As demonstrated in Figure 20, more pollutants in a number larger number of sensors. related sensors. sensors integration ultimately increases the deployment expenses. The rise in the cost ratio from sensors integration ultimately increases the deployment expenses. The rise in the cost ratio Further sensors integration ultimately increases the deployment expenses. The rise in the cost from ratio 5 pollutants to 6 pollutants is smaller than the growth from 4 sources to 5. This is for the reason 5 pollutants to 6 pollutants is smaller than thethan growth 4 sources 5. This to is for that from 5 pollutants to 6 pollutants is smaller the from growth from 4tosources 5. the Thisreason is for that the increasing results in many between pollution zones which increasing pollution sources results in results many intersections intersections between between pollutionpollution zones [24], [24], which reason that pollution increasingsources pollution sources in many intersections zones [24], directlydirectly affect the the system costs. costs. directly affect system costs. which affect the system 25 25 20 20 15 15 10 10 5 5 0 0 2 2

4 4

5 5

6 6

7 7

8 8

9 9

10 10

11 11

Pollution Pollution Sources Sources

Figure on the the overall overall system system deployment deployment cost. cost. Figure 21. 21. Impact of of the the pollution pollution sources sources on 20. Impact

Below the summary of the features possessed by the proposed monitoring system and its Below is is the summary summary of of the the features features possessed possessed by by the the proposed proposed monitoring monitoring system system and and its its Below is the comparison with with existing existing systems systems (Table (Table 3). 3). comparison comparison with existing systems (Table 3). Table 3. of the proposed and the existing pollution monitoring systems. Table 3. Comparison Comparison of the the proposed proposed and and the the existing existing pollution pollution monitoring monitoring systems. systems. Table 3. Comparison of

Features Features Features Cost Cost Cost Accuracy Accuracy Accuracy PowerPower Consumption Power Consumption Consumption System Deployment System System Maintains Deployment Deployment Scalability & Up Maintains Maintains gradation Scalability & & Up Up Scalability Accessible to gradation gradation Common User Accessible Accessible to to Common User Common User

Official Official

Proposed Proposed Monitoring Monitoring System System Monitoring System

Proposed

Models Presented in Models Presented Models Presented inin the Literature the theLiterature Literature

Low Low Low Good-Average Good-Average Good-Average

High High

Systems Very High Very Good Very Good Very Good

Low

High

Very High

Easy

Complex

Highly Complicated

Easy Easy Easy

Moderate Complex Complex

Yes Easy Easy

Mostly Not Moderate Moderate

Yes Yes Yes

Mostly Not Mostly Yes Not

Yes No

Yes Yes

Yes Yes

No No

Low Low

High Average-Low Average-Low Average-Low High High

Official Monitoring Monitoring Monitoring Systems Systems Very High Very High

Very Very High High

Highly Complicated Complicated Yes Difficult Difficult Highly Difficult

Yes

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6. Conclusions In this paper, we developed an edge-computing based IoT architecture for the air pollution monitoring system. The project integrates open access, low cost technologies. The system’s prototyping was done with a small size, cheap and easy to develop & deploy Arduino platform. Prototype is able to monitor multiples gases and particular matter along with humidity and temperature. Algorithms were employed to avoid temporary sensor errors and to manage the cross sensitivity problems. Automatic calibration was applied to ensure the accuracy of the sensors reporting. Another algorithm was developed to minimize the redundant network traffic and to reduce the power consumption. The designed model is deliberated in such a way to reduce the computational burden over sensing nodes that is battery operated and balanced the total work load with edge computing device. The proposed system has no limits on the installation location. Watson studio was employed to explore air quality data and to create the data visualizations for the end user. The study concluded that it is best to position the sensing node at the optimal height near to the pollutants. The system model is intended for static sensor networks, supported by the fact that the pollution sensors work well in static mode. A sufficiently number of experiments have been conducted in different locations to endorse the reliability of the air quality monitoring system. Various remarkable facts have been discovered when associating the air quality tendency and other similar statistics. From the large volume of data, useful information was generated and published to the users at the local level to create awareness. This is an excellent solution for household, offices and crowded environment monitoring, although for industrial monitoring, current versions need to be upgraded for higher accuracy. Nodes are scalable and allow easy up gradation according to demand. Author Contributions: Conceptualization, Z.I. and Z.Z.; Methodology, Z.I.; Software, Z.I.; Validation, Z.I. and Z.Z.; Formal Analysis, Z.I.; Investigation, Z.I.; Resources, Z.Z. and L.Z.; Data Curation, Z.I.; Writing-Original Draft Preparation, Z.I.; Writing-Review & Editing, Z.I. and Z.Z.; Visualization, Z.I., Z.Z. and L.Z.; Supervision, Z.Z. and L.Z.; Project Administration, Z.Z. and L.Z.; Funding Acquisition, Z.Z. and L.Z. Acknowledgments: This work was supported in part by NSFC under Grant 61571137, in part by the Shanghai Pujiang Program under Grant 17PJ1400800, and in part by the Shanghai Institute of Intelligent Electronics and System. Conflicts of Interest: The authors declare no conflict of interest.

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