Power Consumption in Wireless Sensor Networks Sidra Aslam
Farrah Farooq
Shahzad Sarwar
Punjab University College of Information Technology (PUCIT) University of the Punjab Allama Iqbal (Old) Campus, Anarkali, Lahore, Pakistan +92-(0)42-111-923-923
Punjab University College of Information Technology (PUCIT) University of the Punjab Allama Iqbal (Old) Campus, Anarkali, Lahore, Pakistan +92-(0)42-111-923-923
Punjab University College of Information Technology (PUCIT) University of the Punjab Allama Iqbal (Old) Campus, Anarkali, Lahore, Pakistan +92-(0)42-111-923-923-414
[email protected]
[email protected]
[email protected]
ABSTRACT In wireless sensor networks (WSNs), long lifetime requirement of different applications and limited energy storage capability of sensor nodes has led us to find out new horizons for reducing power consumption upon nodes. To increase sensor node’s lifetime, circuit and protocols have to be energy efficient so that they can make a priori reactions by estimating and predicting energy consumption. The goal of this study is to present and discuss several strategies such as power-aware protocols, cross-layer optimization, and harvesting technologies used to alleviate power consumption constraint in WSNs.
Categories and Subject Descriptors A.1 [General Literature]: Introductory and Survey
General Terms Algorithms, Management, Performance
Keywords Wireless sensor network, power consumption, power-aware protocols, energy harvesting, energy management
1. INTRODUCTION Latest advances in computing and networking have enabled WSNs to realize ambient intelligence, which is a vision through which environment becomes smart, friendly, context-aware, and responsive to human needs. A WSN is a network composed of sensor nodes in a sensor field to cooperatively monitor physical or environmental conditions such as temperature, humidity, vibration, or pressure. Design of a WSN is shown in Figure 1.
a sensor node [2]. Energy storage components of a sensor node store energy to power consumer components. The most commonly used energy storage component is a battery, which can be rechargeable or non-rechargeable. A sensor node is energy constrained due to the limited power of a battery. Each hardware component is controlled by a micro-operating system (µOS). Based upon event statistics, a µOS enables power management by deciding which component has to turn off and on. Power-aware protocols make use of µOS for efficient energy utilization. A significant amount of energy can be saved by predicting a node’s load and the optimal allocation of resources. At the core of µOS, there is a task scheduler, which is responsible for task scheduling, considering resource and time constraints. The task scheduler can play an important role in prolonging network lifetime by taking into account energy-aware schemes [3, 4]. Factors serve as a guideline to design a protocol or an algorithm. Some important factors pertaining to WSNs are network topology, operating environment, hardware constraints, transmission media, power management, longevity, scalability, production cost, and fault tolerance. Longevity deals with co-ordination of sensor activities and optimization of communication protocols. WSNs are constrained by limited resources of memory, computation power, and energy [5]. Energy can be treated as a cost function or as a hard constraint [6]. This paper spotlights power management and thus, investigates techniques for reducing the sensor node’s failure that, in turn, minimizes probability of the WSN’s failure.
In a WSN, the sink node collects data from sensor nodes within the sensor field. A sink node may also send queries, program updates, or control packets to sensor nodes. Sensor nodes detect events in the sensor field, perform local data processing and then transmit data to the base-station. A sensor node is composed of many hardware components. Transceiver is a major energy consumer component in a sensor node because communication is one of the most energy expensive tasks; as compared to data processing [1]. Energy cost of transmitting a single bit is approximately the same as processing thousands of instructions in Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. FIT’09, December 16–18, 2009, CIIT, Abbottabad, Pakistan. Copyright 2009 ACM 978-1-60558-642-7/09/12....$10.
Figure 1. WSN design and organization of components of a sensor node
The remainder of this paper is organized as follows. Section 2 discusses power-aware protocol stack, employing sleep modes, data aggregation and data compression, topology control for low power network design, transmission range optimization, or route optimization. Section 3 discusses cross-layer optimization approaches used to achieve the goal of efficient power usage. Section 4 overviews several energy harvesting technologies that can be used in a WSN for energy scalability. Finally, Section 5 concludes this paper.
end up dead. The battery can recover to a certain extent from high-discharge rate effects, through a phenomenon known as battery relaxation, in which the amount of current drawn from the battery is reduced or cut off [12]. Batteries have approximately twice as long a lifetime, if they are discharged in short bursts with significant off time, rather than in continuous operation [13]. Therefore, battery lifetime is extended when the sensor node operates by frequently oscillating between an active and inactive state.
2. POWER-AWARE PROTOCOL STACK
In the power supply unit, a dc-dc converter maintains a constant voltage supply to the sensor node. When the voltage level across the battery terminals constantly decreases as it is discharged, the converter draws an increasing amount of current from the battery to maintain a constant voltage to the sensor node. In this way, current drawn from the battery is more than actually gets supplied to the node, reducing battery life time. The efficiency factor associated with a dc-dc converter determines the battery lifetime [8]. A low efficiency factor results in significant energy loss in the converter, reducing the amount of energy available to other sensor node components.
The protocol stack used by sensor nodes is shown in Figure 2. Much research has been done to design schemes for power conservation and power management in sensor nodes upon all layers of protocol stack, as studied in subsequent sections. Performance of such schemes can be evaluated through simulation tools for instance PAWiS [7]. PAWiS simulation framework supports capturing of features that are critical for WSN like power consumption.
Power-aware mode control Application layer QoS-power trade off Transport layer Power-aware routing, reduced overhead Network layer Transmission range control, sleep states Data link layer Low power design,powerful battery Physical layer
Figure 2. Cross-layer optimization on sensor networks protocol stack
2.1 Power-Aware Physical Layer Using low power components and trading-off unnecessary performance can save significant amounts of power. To identify power consumption upon each node, it is necessary to identify factors influencing power consumed by different hardware components. For instance, factors that affect transceiver power consumption includes: type of modulation scheme used, data rate, transmit power determined by transmission distance, operational duty cycle, packet payload size, header size, symbol rate for an M-ary modulation scheme, amplifier power, reception power, startup power, and startup time [8]. Many physical layer modulation schemes reduce the radio-transceiver power consumption by reducing transmission time [9]. But transmission energy can also be reduced by lowering transmission power and increasing transmission duration [10]. Transmission energy does not monotonically decrease as transmission time increases. Transmission energy may increase when transmission time exceeds some threshold value. Higher modulation levels may be unrealistic in WSNs but for peak-throughput, higher modulation levels are required. More energy can be conserved by dynamically adopting the modulation level according to instantaneous traffic load, known as modulation scaling. Multiple frequency-shift keying (M-FSK) is more energy efficient than other M-ary or binary modulation schemes for short range, low duty communication systems, like WSNs [11]. A factor that affects battery lifetime is the discharge rate of current. Every battery has a specific discharge rate value specified by the manufacturer. Drawing higher current than specified results in reduced battery life because the active ingredients diffusion rate through the electrolyte falls behind their consumption rate at electrodes. If a high discharge rate is maintained, the battery can
Energy consumed in sensing depends upon the type of sensing-unit used in a sensor node. Factors affecting the power consumed by the sensing unit include signal sampling, conversion of physical signals to electrical, signal conditioning, and analog-to-digital converter (ADC). Choice of micro-controller unit (MCU) is dictated by required performance level and it also impacts consumed power. Several techniques have been proposed to estimate power consumed by different processors [14, 15]. Communication protocols implemented inside the MCU have a drawback of packet traversal from the radio and the computing components (MCU), even if a packet is not destined to it. Energy-aware packet traversal can be employed by enabling the transceiver to make packet-forwarding decisions without turning on the MCU [16].
2.2 Power-Aware Data Link Layer The data link layer includes Medium Access Control (MAC) and error control protocols. MAC protocol in a self organizing WSN creates network infrastructure by defining appropriate communication channels, and shares available communication media among nodes. Since transmission is the most energy- consuming task in a sensor node, MAC protocols should be properly designed to offer energy saving opportunities by cutting down energy inefficient access to minimum. A sensor node wastes a large amount of energy due to idle-listening of channel, packet collision, overhead of control packets, and overhearing. In collision and idle-listening, the node continuously consumes energy in retransmission and sensing channels respectively. Therefore, energy efficient MAC protocol must avoid collision, overhearing, overhead of control packets, and idle-listening. Some of the data link layer protocols are enlisted in Table 1. Sensor nodes can attain energy scalability by operating their components in a power-aware fashion through the use of dynamic power management (DPM). DPM uses event driven power management where a sensor node shuts down when in an idle-state, but state transitioning has an overhead of maintaining the processor’s state; turning off power, and scheduling wakeups [17]. Factors such as power consumed during various modes, transition cost, and time spent by the processor in each mode have significant impact upon a node’s lifetime [18]. Therefore, a sensor
node consumes approximately the same amount of power in sleep and listening modes of the radio receiver [18]. Furthermore, using dynamic voltage scaling (DVS), CPU energy consumption can be reduced by matching the processor’s operating voltage and frequency according to instantaneous workload requirement. Trading off unutilized performance for energy saving through use of DVS results in higher energy efficiency when compared with shutdown based DPM.
In a non-deterministic environment, the stochastic process offers a technique to deal with random events. MAC protocols may be centralized or decentralized. In centralized MAC protocols, nodes contend channels through scheduling. Decentralized MAC protocols contend channels upon demand, without any schedule or synchronization. Such protocols adopt a low power listening (LPL) approach for energy efficient communication. Furthermore, LPL schemes may be classified as fixed or dynamic. Fixed LPL schemes use static value of preamble regardless of traffic conditions. Such schemes have drawbacks of extended waiting time even if the receiver is already in wake up mode. It cannot tell the receiver anything until the end of the preamble. Therefore, they may either waste power or increase latency. Dynamic LPL schemes adjust preamble conforming to various traffic conditions. Such schemes sample a channel more frequently when it is likely to have traffic and less frequently when it is not. Adaptively tuning thresholds according to traffic conditions can reduce energy consumption [19].
Network events can be considered deterministic when information is required from a WSN at fixed points in time. Non-deterministic events are considered to be random in nature and without any delay constraint. A scheduling strategy involves communication synchronization between two or more nodes. In a non-deterministic environment, the synchronization process has to perform continuously. Based upon the nature of events, scheduling technique may be deterministic or non-deterministic. Therefore, non-deterministic scheduling techniques should be employed for applications where delay overheads are important. Table 1. Data link layer schemes Scheme Hybrid TDMA/ FDMA Sensor MAC (S-MAC) Timeout MAC (T-MAC) PAMAS Sinha’ 2001 [24] Arisha’ 2002 [25] SIFT[26] PAPSM SPAN [27] DMAC [28] Pattern MAC (P-MAC) PCS-MAC [29] Udenz’ 2007 [30] ESR-MAC [31]
B-MAC wise MAC Z-MAC [32] Pianegiani’ 2008 Boost-MAC X-MAC Hohlt’ 2004 [34] Clustering [35] MSC [36] DTPC [21]
Description Centralized MAC schemes Based protocols calculate optimum number of channels and are more energy efficient than TDMA or FDMA. Avoids idle listening, overhearing, and collision. Consumes extra power during fixed active period when there is no traffic load. For variable payloads, it uses less energy than S-MAC because of adaptive duty cycles. It suffers from packet collision. Power saving medium access protocol (PAMAS) reduces collisions, avoid overhearing and use transmit control messages for energy saving. Renders desired energy-quality scalability at cost of latency and sensing accuracy. Techniques do not scale as network size increases. Provides energy-latency trade-offs. It suffers from idle-listening and overhearing. Phase announcement power save mechanism (PAPSM) uses short communication window that limits data transmission or increases transmission time. Uses adaptive scheduling strategy based upon local information and is therefore, scalable. Solves data forwarding interruption, channel contention, and collisions problem. It adaptively adjusts node duty cycles according to traffic load. It provides reduced latency while ensuring high data reliability. Adopts sleep/wakeup schedule based upon its own and its neighbour traffic. It consumes extra energy to maintain traffic pattern information. Power controlled sensor MAC (PCS-MAC) avoids collision and overhearing. It maintains schedule and minimum power level required to communicate with each neighbour node. Optimizes sleep schedule by scheduling network events. Efficient slot reservation MAC protocol preserves collision free transmission. It provides efficient energy saving than S-MAC and T-MAC because of efficient packet transmission. It is more effective in data gathering WSN. Fixed LPL schemes Suffers from idle-listening. Trade-off exists between energy and transmission latency. Combines strength of both CSMA and TDMA. At higher transmission rate, it performs better than B-MAC due to more efficient contention resolution. Uses signals classification to reduce amount of transmitted data based upon computational latency overhead [33]. Dynamic LPL schemes Adjusts preamble immediately after single idle or busy channel observation. Uses short preamble that reduces latency and energy consumption. Proposed distributed techniques introduce significant overhead. Transmission power control (TPC) algorithms Optimizes transmit power level in non-homogeneously distributed network. Integrates transmitted signal power control with received information quality. Improves throughput and power consumption of multi-hop WSN by adjusting radio transmission power until it hears desired number of neighbours. It is useful in combination with LPL that implements low duty cycling.
Transmission Power Control (TPC) mechanism can be used to control traffic transmission performance upon a network and thus, conserves energy. Transmission power determines transmission range and interference caused at other nodes. Properties of power adjusted transmissions have been investigated [20]. Transmission power control protocols can employ fixed TPC (FTPC) or dynamic TPC (DTPC). FTPC improves power consumption up to 16-percent for convergence traffic, when compared with DTPC, but no significant performance improvements for aggregate traffic [21]. Error control schemes minimize packet retransmissions, thus reducing power consumed at the transceiver. Different applications have different quality-of-service (QoS) demands. Adaptive error control schemes were proposed in [22] to reduce energy consumption. Error Control Codes (ECCs) are used to achieve bit error rate (BER) QoS measure. An approach to explore energy optimal ECCs is presented in [23]. To find out energy consumption of ECCs, it is necessary to consider trade-offs between energy consumption in computing encoding-decoding, transmitting redundant bits, and energy saved by coding gain.
2.3 Power-Aware Network Layer Network layer is responsible of topology control, layer 3 addressing, and making routing decisions. Depending upon node versatility, network can adopt directed, multi-hop, single-hop clustering, or multi-hop clustering transmission schemes, as shown in Figure 3. Type of transmission scheme supported by protocol depends upon its functionality. Some of the network layer protocols are enlisted in Table 3.
Figure 3: Transmission schemes in WSN
2.3.1
Routing Protocols
Routing protocols deals with data transmission from one sensor node to another. Routing in WSNs is very challenging due to inherent characteristics that distinguish such networks from other wireless networks as illustrated in Table 2. Routing protocols may try to find out energy efficient routes instead of minimizing delay through power available (PA) in nodes; or energy required for transmission in links along the route. Protocols can make efficient control of power dissipation across all nodes by making local greedy decisions but this case may not be true always. Optimizing network behavior is trivial if each node has global network state but it involves excessive communication overhead as the network size grows. Classification of routing protocols can be made based upon flat, hierarchal, multi-path, query, negotiation, data centric, location, and quality-of-service (QoS) measures [37]. Furthermore, these protocols may be classified as reactive or proactive routing.
2.3.1.1 Reactive Routing Protocols In reactive routing, routes to destination are computed on demand. These protocols take more time for routing first packet as routes are unknown in advance. For instance ad-hoc on demand distance vector (AODV) [38] and dynamic source routing (DSR) [39].
Table 2. Comparison of routing characteristics in WSN and wireless ad-hoc networks WSN Utilize position information for routing. Data moves toward sink node. Data is filtered or aggregated due to redundancy. Rapid topological changes due to node mobility and failure.
Wireless ad-hoc networks Uses global addressing scheme, IP-based routing. Source destination pair changes constantly. No need of aggregation. Topological changes are not frequent.
2.3.1.2 Proactive Routing Protocols Proactive routing protocols maintain table to keep route information from each node to every other node in network [40]. Proactive protocols result in unnecessary bandwidth utilization when topology changes frequently. In the case of the sheer number of sensor nodes, size of the routing table grows exponentially because of maintenance of unused links in table. For instance destination-sequenced distance vector (DSDV) is a proactive routing protocol that keeps the shortest route information of each node from every other node in the routing table [41].
2.3.1.3 Data Centric Routing Protocols In WSN, data gathering is an imperative task of communication. It is crucial to design such energy efficient communication strategies as to fully cater to the needs of data gathering operations. Since start-up costs are significant in most radio architectures [42], it is beneficial to operate with as large packet size as possible because it amortizes fixed overhead against more bits. Short packet size produces large overheads while long packet size may experience a higher number of errors and increase overall latency to exchange information. Energy optimization can be attained by optimal packet sizes [43]. Therefore, to reduce energy consumed by a transceiver, data transmission should be limited as much as possible by incorporating data compression and aggregation techniques, known as data-centric approach. Consequently, there are fewer numbers of transmissions, which results in energy saving. The data aggregation process enables the use of data-combining techniques to reduce the amount of data to be communicated, at the expense of extra computation at individual nodes to perform data aggregation. In a reverse-multicast way, data aggregation may lead to high data collision rates, high energy consumption, and low throughput near the sink node. Data aggregation scheme may be adopted depending upon requirement constraints. For instance a source, near to the sink, acts as a data aggregation point. But it will increase the power consumption up of this source node. Multiple data aggregation points can be used for its remedy. Another scheme may be in which each source sends its information to the sink along the shortest path. Data is aggregated where the paths overlap. This will result in an increase of load upon the shortest path. A source that has a single path for transmission and which also acts as the shortest path for the other nodes may suffer from congestion. Latency is associated with aggregation. Data from nearer sources have to wait for aggregating data coming from the farther sources. A significant amount of energy can be saved using data aggregation when the number of sources is large, and they are located relatively close to each other and far from the sink [44].
Energy latency trade-offs are explored in [45]. Data centric architectures should support the type of service, so the desired trade-offs between latency and energy can be attained according to application requirements.
2.3.1.4 Hierarchal Routing Protocols Hierarchical protocols use a clustering mechanism for data aggregation to minimize energy consumption. In WSN, data is regionally co-related and therefore a clustering scheme can be used for transmitting data. Cluster heads collect information from nodes, process, aggregate, and relay information to the base-station.
2.3.1.5 Location Based Routing Protocols Location based protocols utilize geographical information to send data to a desired location instead of exploring the whole network. Location based routing algorithms may use a greedy approach or directional flooding. Some protocols making use of greedy decisions are: most forward within radius (MFR), nearest with forward progress (NFP), compass routing, randomized compass routing, geographic distance routing (GEDIR), and greedy perimeter stateless routing (GPSR). Protocols such as distance routing effect algorithm for mobility (DREAM) and location aided routing (LAR) fall into the directional flooding category.
Table 3. Network layer protocols Protocol Flooding Gossiping Directed diffusion SPIN [48] Rumour routing EPAS, HEPAS [49]
Low energy adaptive clustering hierarchy (Leach) Static clustering Pegasis BCDCP [51] Gradient based routing (GBR) TEEN, APTEEN Ad-hoc on demand distance vector (AODV), CTR EADV [52] Abusaimeh’2009 [53], Madani’2008 [54] Direct Minimum transmission (MTE) [55] e3D [56, 57]
energy
TPR [58] GAF SPAN algorithm Sparse topology and energy management (STEM) ASCENT [60] Slijepcevic’ 2001 [61] MP-SCTC [62]
Description Data centric routing protocols Suffers from implosion, resource blindness, and overlapping. Eliminates the problem of implosion but introduces propagation delay and more resource utilization. It utilizes less power compared with flooding. Not suitable for applications where information is required upon a periodic basis. Sensor protocol for information via negotiation overcomes problem of implosion, overlap, and resource blindness. It is not suitable for applications where information is required upon a periodic basis. Instead of flooding queries, events are flood when the number of events is less than the number of queries. Energy efficient protocol for aggregator selection (EPAS) and HEPAS calculates number of aggregators needed to minimize amount of total energy consumed in network. Hierarchical routing protocols Some overhead is produced in periodically choosing heads but balances the communication load upon each head. Since it is two-hop routing protocol, it cannot be deployed for a huge geographical area. Power usage per node for Leach is less than the power required in flooding, gossiping, and GBR [50]. Energy can be saved by processing data locally instead of sending it back to base-station. Power efficient gathering in sensor information systems (Pegasis) is a chain based clustering protocol. Balances load on each cluster and provide better energy savings compared with Leach and Pegasis. Utilizes neighbour’s hop-count information to make a packet forwarding decision. It works better compared with gossiping [50]. Used in time critical applications. Cluster heads send only significantly changed data to the nodes. Formation of cluster heads is an overhead. In APTEEN, data is also sent periodically. Find shortest path and consume least power but they may limit network lifetime because of unawareness of node’s energy level during communication. In cluster tree routing (CTR), cluster heads are fixed during network lifetime and thus, die quickly. Makes use of computational and implementation simplicity as compared to AODV and DSDV. Role of cluster heads is distributed among nodes based upon the remaining energy of the node. Location based routing protocols/ algorithms Quicker power supply depletion at the nodes far away from the base-station compared with nodes closer to the base-station. Node does not select another neighbouring node for relaying its messages until its relaying node’s power is depleted. Second, nodes that are closer to the base-station will die soon. Third, nodes furthest away from base-station will live longer because they do not have to relay the messages of other nodes. In energy efficient distributed dynamic diffusion (e3D) routing algorithm, each node updates neighbours’ information through distance derived from radio signal strength, battery power and load. No need of routing table and neighbourhood information. Topology control protocols/ algorithms Rotates node functionality periodically to ensure fair energy consumption among nodes. Trade-off exists between topology maintenance and energy conservation Increases network lifetime by maintaining network connectivity at a cost of increased latency [59]. This approach is for event based monitoring systems. STEM improves network lifetime, when compared with GAF and SPAN. Adaptive self-configuring sensor network topologies (ASCENT) conserves energy by allowing only those nodes to remain active that are required to maintain path towards destination. Achieves energy saving by using only a subset of stochastically placed sensor nodes at each moment while fully preserving coverage. Makes network strongly connected and minimizes the total amount of power used.
2.3.1.6 QoS Based Routing Protocols QoS based energy-aware routing protocols attain balance between energy consumption and data quality, for instance sequential assignment routing (SAR) and minimum energy metric (MEM). In SAR, a sensor node selects a tree for data to be routed back to the sink according to energy resources and an additive QoS measure. MEM algorithm minimizes power consumed by each packet without considering its priority. SAR consumes less energy compared with MEM and ensures fault tolerance but has the overhead of maintaining table and node state.
2.3.2
Topology Control
WSN can have star, mesh, and hybrid topology. In star topology, nodes at a large distance from the sink node will consume more energy for transmissions compared with nodes closer to the sink. Due to single hop connectivity with the sink, link failure will disconnect the node from the network. Mesh networks are multi-hop and multi-path, which makes them fault tolerant. Hybrid topology provides the features of low power consumption and fault tolerance by incorporating star and mesh topology. Node degree can have significant impact on its power consumption [46]. Therefore, topology control is a powerful mean for low power network design. Several techniques can be classified as topology control mechanisms [47]. Topology control can be employed by decreasing the number of neighbouring nodes, controlling transmission power through the introduction of hierarchies in network, and forwarding packets over many short-route hops instead of single large-route hop. Topology control protocols stay aware of topological changes. Thus, reduce packet-loss rate and eliminate energy consumption during re-transmission.
2.4 Power-Aware Transport Layer Transport layer is required for end-to-end delivery. It may not be required in WSN because of hop-by-hop communication but may be needed for security reasons. Protocols upon the transport layer of the sensor network protocol stack may be connectionless to avoid costly acknowledgement mechanism because of power constraint.
2.5 Power-Aware Application Layer WSN are extremely application specific and therefore, protocols lying upon application layer vary with application demands. Sensor management protocol (SMP) makes hardware and software of lower layers transparent to management activities. Sensor query and data dissemination protocols provide user applications with interfaces to issue queries, respond to queries and collect incoming replies. In interest dissemination protocols, the user sends their interest to nodes. In data advertisement protocols, the node advertises available data to user nodes.
3. CROSS-LAYER OPTIMIZATION Layered architecture is well suited for wired networks but not for wireless networks [63]. Optimization in one layer may need cooperation of other layers to avoid counteract occurred because of different approaches used for the same optimization target. This can be achieved through merging layers or creating new interfaces between layers. A layered protocol architecture proposed in [64] provides features of traditional layered architecture as well as of cross-layer design. Cross-layer optimization tries to achieve several goals, one of which is to increase network lifetime by reducing the gap between power consumption upon each node and
limited power supply available to it. The lifetime of resource constrained WSN can be increased by exchanging power information across all layers. Low power usage can be achieved in all five layers of WSN protocol stack depending upon application context. For instance, if someone designs a power-saving, shortest-path routing protocol, such routes may take advantage of the fact that for the same transmission distance, taking more smaller-distance hops will save transmission power compared with a single larger-distance hop. However, data transmission with a larger number of hops may have more contention possibilities. If MAC layer is not optimized accordingly, advantages of routing design may be counteracted by increasing power consumption due to increase of contention. Cooperative communication schemes can dramatically reduce transmission power. In such schemes, multiple nodes collaborate in forming a virtual antenna array to achieve spatial diversity. An energy-efficient single-relay selection cooperative communication scheme for WSN was proposed in [65]. The proposed scheme incorporates MAC design and physical-layer power control into the node selection process, in a distributed manner. It minimizes overall energy consumption per packet and maximizes network lifetime. Using cross-layer optimization, energy harvesting technologies can attain energy optimization by integrating energy-aware routing and MAC protocols. For instance, MAC protocol can exploit the information in routing message to determine sleep and wakeup schedule without exchanging any additional control message. When network workload is low, nodes can be scheduled to save power. It is more power efficient for active nodes to use long transmission power ranges since it will require fewer nodes to remain awake. Conversely, short radio ranges may be preferable when the network workload is high as the radio spends more time in the transmission and reception. Minimum power configuration (MPC) protocol [66] minimizes aggregate energy consumption in all power states by integrating optimization upon topology control, power-aware routing, and sleep management. MPC configure network power by considering a set of active nodes and transmission power of nodes. A cross-layer management plane (CLAMP) was proposed in [67] that present a set of parameters to take benefits from the synergy across layers. A cross-layer framework for global optimization of power consumption was proposed in [68] that balances load in WSN through local greedy decisions. This framework employs a flexible cost function at routing layer and adaptive duty cycles at MAC layer in order to adapt the node’s behaviour to its local state. This framework enables each node to use its local and neighbourhood state information to adapt its routing and MAC layer behaviour. The algorithm that characterizes this framework periodically runs three basic steps: gather neighbourhood state information, perform local calculations upon gathered state, and modify local configuration accordingly. Distributed scheduling algorithm proposed in [69] works in cooperation with MAC and routing protocols. It avoids idle listening and collision but provides no load balancing.
Dozer protocol [70] attains low radio duty cycles in both single-hop and multi-hop networks and thus achieves high energy efficiency. Dozer enables the coordination of MAC-layer, topology control, and routing to reduce energy. It reduces idle listening and overhearing. Positioned based routing protocols, which uses cross layer information are integrated in the architecture proposed in [71] to achieve improved energy efficiency.
4. ENERGY HARVESTING TECHNOLOGIES Energy scalability is of much importance in energy constrained situations and can be achieved through energy-harvesting technologies. Energy-harvesting technologies are required for autonomous sensor networks, where battery recharging is impossible and thus environmental resources are used for energy scalability. Energy harvesting technologies are used to scavenge energy from ambient sources. Since the lifetime of a WSN is determined by the energy of the node, which is the scarcest resource of a WSN and, in most cases, cannot be replenished for reasons like, cost or geographic location. In some cases, sensor nodes can potentially operate indefinitely by using environmental energy. In order to increase the lifetime of a sensor node, energy harvesting devices scavenge energy from light, vibration, or thermal gradients and feed the energy-storage component of the sensor node. Solar cells are made up of silicon with some impurities and exploit photovoltaic effect to convert sunlight into electricity. Vibration based harvesting devices can employ electromagnetic conversion, or piezoelectric effect. Thermoelectric devices generate electric energy when a temperature gradient exists across the device. For instance pico-cube [72] is a wireless sensing device powered by harvested energy. Energy delivered by a harvesting device is not directly usable by the node’s component due to different voltage requirements and is therefore, buffered. Depending upon requirements, capacitors can be used to buffer energy and can deliver power later. An optimal energy management policy for a solar powered sensor node was proposed in [73]. It considers the channel and queue awareness sleep/wakeup mechanism. The sensor node turns off the radio transmitter when the queue becomes exhausted. However, incoming packets from other nodes can be buffered in the queue. Sensor node switches to the sleep mode if channel quality is bad. However, since such devices can produce only a limited amount of energy due to environmental conditions, energy saving mechanisms are also required.
5. CONCLUSIONS Wireless sensor networks (WSNs) are application specific networks and depending upon application requirement may need an operational lifetime of months to years. Therefore, it is mandatory to give the utmost priority to WSNs’ design. This paper surveys the main approaches to energy conservation in WSNs. The sensor’s wireless communication ability facilitates distributed sensing and thus, makes WSNs distributed in nature. The distributed nature of WSNs and increased allowable latency per computation task, enable easy salvage of energy optimization through parallel computation of algorithm’s among multiple
sensor nodes. However, distributed computing algorithms demand more inter-node communication. Node level power management techniques such as DVS at computation component and modulation scaling at communication component reduce energy consumption at cost of increased latency. Energy latency trade-off techniques, both DVS and modulation scaling, can coordinate and exploit total acceptable latency budget to obtain energy savings. Power-aware routing protocols and topology management ensure that the burden of forwarding traffic is distributed between nodes in an energy-efficient way. Data gathering techniques illustrate the effectiveness of exploiting network-wide computation-communication trade-offs. Further energy optimization is possible by reducing packet size. To increase sensor node’s lifetime, integration of harvesting technologies, low power sensor network design, and energy-aware protocols is mandatory. Therefore, generic cross-layer optimization techniques are required to fulfill applications demands.
6. REFERENCES [1] C. Schurghers, V. Raghunathan, S. Park, and M. Srivastava. Energy-Aware Wireless Microsensor Networks. IEEE Signal Proc Mag. 40–50, 2002 [2] W. J. Kaiser and G. J. Pottie. Wireless Integrated Network Sensors. CACM. 43(5): 51–58, 2000 [3] P. Spanos, V. Raghunathan, and M. Srivastava. Adaptive Power fidelity in Energy Aware Wireless Embedded Systems. IEEE REAL. 2001 [4] A. Demers, F. Yao, and S. Shenker. A Scheduling Model for Reduced CPU Energy. FOCS. 374-382, 1995 [5] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. A Survey on Sensor Networks. IEEE Commun. Mag. 2002 [6] A. Ephremides. Energy concerns in Wireless Sensor Networks. IEEE Wireless Communications. 2002 [7] J. Glaser, D. Weber, S. Madani, and S. Mahlknecht. Power Aware Simulation Framework for Wireless Sensor Network and Nodes. Eurasip journal on Embedded systems. 2008 [8] A. Savvides, S. Park, and M. Srivastava. Battery Capacity Measurement and Analysis using Lithium Coin Cell Battery. ACM ISLPED. 382-387, 2001 [9] E. Shih, S. Cho, N. Ickes, R. Min, A. Sinha, A. Wang, and A. Chandrakasan. Physical Layer Driven Protocol and Algorithm Design for Energy-Efficient Wireless Sensor Networks. ACM SIGMobile. 2001 [10] A. E. Gamal, C. N. B. Prabhakar, E. U. Biyikoglu, and S. Zahedi. Energy-Efficient Scheduling of Packet Transmissions over Wireless Networks. IEEE INFOCOM. 2002 [11] S. H. Cho, A. Wang, C. G. Sodini, and A. P. Chandrakasan. Energy-Efficient Modulation and MAC for Asymmetric RF Microsensor Systems. ACM ISLPED. 106-111, 2001 [12] C. F. Chiasserini and R. R. Rao. Pulsed Battery Discharge in Communication Devices. ACM MobiCom. 88-95, 1999 [13] L. Benini, G. Castelli, A. Macii, and E. Macii. A DiscreteTime Battery Model for High Level Power Estimation. DATE. 35-39, 2000
[14] S. Malik, V. Tiwari, A. Wolfe, and M.T.C. Lee. Instruction Level Power Analysis and Optimization of Software. VLSISP. 13(2): 1-18, 1996
[33] F. Pianegiani, M. Hu, A. Boni, and D. Petri. Energy-Efficient Signal Classification in Ad-hoc Wireless Sensor Networks. IEEE Trans. Instrum. Meas. 57(1), 2008
[15] A. P. Chandrakasan and A. Sinha. Jouletrack: A Web Based Tool for Software Energy Profiling. DAC. 2001
[34] B. Hohlt, L. Doherty, and E. Brewer. Flexible Power Scheduling for Sensor Networks. IPSN. 205-214, 2004
[16] M. Srivastava, V. Tsiatsis, and S. Zimbeck. Architectural Strategies for Energy-Efficient Packet Forwarding in Wireless Sensor Networks. ISLPED. 92-95, 2001
[35] K. B. Chang, Y. B. Kong, and G. T. Park. Clustering Algorithm in Wireless Sensor Networks Using Transmit Power Control and Soft Computing. IEEE ICIC. 171–175, 2006
[17] A. Chandrakasan and A. Sinha. Dynamic Power Management in Wireless Sensor Networks. IEEE DT-M. 2001 [18] J. D. Estrin and H., Y. Xu. Geography-Informed Energy Conservation for Ad-hoc Routing. ACM MobiCom. 70-84, 2001 [19] S. Lee, J. Choi, J. Na, and C. Kim. Analysis of Dynamic Low Power Listening Schemes in Wireless Sensor Networks. IEEE Commun. Lett. 13(1), 2009
[36] A. B. Cunha and D. C. Silva. An Approach for the Reduction of Power Consumption in Sensor Nodes of Wireless Sensor Networks: Case Analysis of Mica2. SAMOS. 132-141, 2006 [37] K. Akkaya and M. Younis. A Survey on Routing Protocols for Wireless Sensor Network. Elsevier Ad-hoc Networks. 3(3): 325–349, 2003 [38] C. Perkins and E. Royer. Ad-hoc on Demand Distance Vector Routing. IEEE WMCSA. 1999
[20] S. Lindsey and C. Raghavendra. Pegasis: Power-Efficient Gathering in Sensor Information Systems. Int Conf Comm. 2001
[39] D. B. Johnson and D. A. Maltz. Dynamic Source Routing in Ad-hoc Wireless Networks. IEEE Trans. Mob. Comput. Kluwer. 1996
[21] J. Jeong, D. Culler, and J. Oh. Empirical Analysis of Transmission Power Control Algorithms for Wireless Sensor Networks. IEEE INSS. 2007
[40] R. C. Shah and J. M. Rabaey. Energy Aware Routing for Low Energy Ad-Hoc Sensor Networks. IEEE WCNC. 2002
[22] P. Lettieri, C. Fragouli, and M. Srivastava. Low Power Error Control for Wireless Links. ACM MobiCom. 139-150
[41] C. E. Perkins and P. Bhagwat. Highly Dynamic Destination Sequenced Distance Vector Routing (DSDV) for Mobile Computers. ACM SIGCOMM. 1994
[23] S. Chouhan, M. Balakrishnan, and R. Bose. A Framework for Energy Consumption Based Design Space Exploration for Wireless Sensor Nodes. ACM ISLPED. 2008
[42] A. Wang, S. H. Cho, C. G. Sodini, and A. P. Chandrakasan. Energy Efficient Modulation and MAC for Asymmetric Microsensor Systems. ISLPED. 106-111, 2001
[24] A. Sinha and A. P. Chandrakasan. Operating System and Algorithmic Techniques for Energy Scalable Wireless Sensor Networks. Springer. 2001
[43] I. Joe. Optimal Packet Length with Energy Efficiency for Wireless Sensor Networks. IEEE ISCAS. 2005
[25] K. A. Arisha, M. A. Youssef, and M. F. Younis. EnergyAware TDMA Based MAC for Sensor Networks. IEEE IMPACCT. 2002 [26] K. Jamieson, H. Balakrishnan, and Y. C. Tay. Sift: A MAC Protocol for Event-Driven Wireless Sensor Networks. 2003 [27] B. Chen, K. Jamieson, and H. Balakrishnan, R. Morris. Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad-hoc Wireless Networks. Wireless Networks. 8(5): 481 – 494, 2002 [28] G. Lu, B. Krishnamachari, and C. S. Raghavendra. An Adaptive Energy-Efficient and Low-Latency MAC for Data Gathering in Sensor Networks. IEEE SPDP. 2004 [29] P. C. Nar and E. Cayirci. PCSMAC: A Power Controlled Sensor-MAC Protocol for Wireless Sensor Networks. EWSN. 2005 [30] A. Udenz and K. M. D. Maier. Renewal Theory Sleep Time Optimisation for Scheduling Events in Wireless Sensor Networks. NASA/ESA AHS. 2007 [31] Y. C. Chang and J. P. Sheu. An Energy Conservation MAC Protocol in Wireless Sensor Networks. Wireless Pers Commun. 261–276, 2009 [32] I. Rhee, A. Warrier, M. Aia, and J. Min. Z-MAC: A Hybrid MAC for Wireless Sensor Networks. IEEE Trans. Netw. Serv. Manage. 2008
[44] B. Krishnamachari, D. Estrin, and S. Wicker. Impact of Data Aggregation in Wireless Sensor Networks. IEEE ICDCS. 2002 [45] Y. Yu, B. Krishnamachari, and V. K. Prasanna. EnergyLatency Tradeoffs for Data Gathering in Wireless Sensor Networks. IEEE. 2004 [46] A. Salhieh, J. Weinmann, M. Kochhal, and L. Schwiebert. Power Efficient Topologies for Wireless Sensor Networks. IEEE ICPP. 156-163, 2001 [47] B. Gholamzadeh and H. Nabovati. Concepts for Designing Low Power Wireless Sensor Network. WASET. 35, 2008 [48] H. Balakrishnan, W. R. Heinzelman, and J. Kulik. Adaptive Protocols for Information Dissemination in Wireless Sensor Networks. ACM MobiCom. 1999 [49] Y. P. Chen, A. L. Liestman, and J. Liu. A Hierarchical Energy-Efficient Framework for Data Aggregation in Wireless Sensor Networks. IEEE Trans. Veh. Technol. 55(3), 2006 [50] L. A. Varghese, G. Santhosh, J. Mathew, and K. P. Jacob Evaluation of the Power Consumption of Routing Protocols for Wireless Sensor Networks. IEEE. 2006 [51] S. D. Muruganathan, D. C. F. Ma, R. I. Bhasin, and A. O. Fapojuwo. A Centralized Energy Efficient Routing Protocol for Wireless Sensor Network. IEEE Radio Communications. 2005
[52] S. Mahlknecht, S. Madani, and M. Rötzer. Energy Aware Distance Vector Routing Scheme for Data Centric Low Power Wireless Sensor Networks. IEEE INDIN. 2006 [53] H. Abusaimeh and S. Yang. Dynamic Cluster Head for Lifetime Efficiency in WSN. Int. J. Autom. Comput. 2009 [54] S. Madani, D. Weber, and S. Mahlknecht. A Hierarchical Position Based Routing Protocol for Data Centric Wireless Sensor Networks. IEEE INDIN. 2008 [55] H. Balakrishnan, W. Heinzelman, and A. Chandrakasan. Energy-Efficient Communication Protocol for Wireless Micro-sensor Networks. HICSS. 2000 [56] I. Raicu. Efficient Even Distribution of Power Consumption in Wireless Senor Networks. ISCA. 2003 [57] I. Raicu, L. Schwiebert, S. Fowler, and S. K. S. Gupta. e3D: An Energy-Efficient Routing Algorithm for Wireless Sensor Networks. IEEE ISSNIP. 2004 [58] S. Madani, D. Weber, and S. Mahlknecht. TPR: Dead End Aware Table Less Position Based Routing Protocol for Low Power Wireless Sensor Networks. IEEE SIES. 2008 [59] C. Schurgers, V. Tsiatsis, and M. B. Srivastava. STEM: Topology Management for Energy Efficient Sensor Networks. IEEE. 2001 [60] A. Cerpa and D. Estrin. ASCENT: Adaptive Self-configuring Sensor Networks Topologies. IEEE Trans. Mob. Comput. 2004 [61] S. Slijepcevic and M. Potkonjak. Power Efficient Organization of Wireless Sensor Networks. IEEE. 2001 [62] D. Li, H. Du, and W. Chen. Power Conservation for Strongly Connected Topology Control in Wireless Sensor Network. ACM FOWANC. 2008 [63] V. Srivastava and M. Motani. Cross-Layer Design: A Survey and the Road Ahead. IEEE Commun. Mag. 2005 [64] S. Madani, S. Mahlknecht, and J. Glaser. A Step towards Standardization of Wireless Sensor Networks: A Layered Protocol Architecture Perspective. IARIA SENSORMM. 2007 [65] Z. Zhou, S. Zhou, J. Cui, and S. Cui. Energy-Efficient Cooperative Communication Based on Power Control and Selective Single-Relay in Wireless Sensor Networks. IEEE Trans. Wireless Commun. 7(8), 2008 [66] G. Xing, C. Lu, Y. Zhang, and Q. Huang. Minimum Power Configuration in Wireless Sensor Networks. ACM MOBIHOC. 2005 [67] S. Madani, S. Mahlknecht, and J. Glaser. Clamp: Cross layer Management Plane for Low Power Wireless Sensor Networks. FIT. 100-112, 2007 [68] R. Jurdak, P. Baldi, and C. V. Lopes. Adaptive Low Power Listening for Wireless Sensor Networks. IEEE Trans. Mob. Comput. 6(8), 2007
[69] M. L. Sichitiu. Cross-Layer Scheduling for Power Efficiency in Wireless Sensor Networks. IEEE INFOCOM SER. 2004 [70] N. Burri, P. V. Rickenbach, and R. Wattenhofer. Dozer: Ultra Low Power Data Gathering in Sensor Networks. ACM IPSN. 2007 [71] S. A. Madani. Cross Layer Design for Low Power Wireless Sensor Networks. PHD Dissertation. 2008 [72] Y. H. Chee, M. Koplow, M. Mark, N. Pletcher, M. Seeman, F. Burghardt, D. Steingart, J. Rabaey, P. Wright, and S. Sanders. PicoCube: A 1cm3 Sensor Node Powered by Harvested Energy. ACM. 2008 [73] K. Bhargava, D. Niyato, E. Hossain, and M. Rashid. Wireless Sensor Networks with Energy Harvesting Technologies: A Game-Theoretic Approach to Optimal Energy Management. IEEE Wirel. Commun. 2007
