Distributed and Stable Energy-Efficient SchedulingAlgorithm For Coverage In Wireless Sensor

Networks.

Manel Chenait, Bahia Zebbane, Hamza Belbezza, Hakim Balli, Nadjib BadacheLSI Laboratory, University of Sciences and Technology Houari Boumediene (USTHB)

BP32, El-Alia, Bab-Ezzouar, Algiers, 16111, Algeria.

Email: mchenait@usthb.dz, bzebbane@usthb.dz, hbel024@yahoo.fr, ballihakim@gmail.com, badache@mail.cerist.dz

Abstract—Minimizing the energy consumption of battery-powered sensors is an essential consideration in sensor networkapplications like coverage, and sleep/wake scheduling mechanismhas been proved to an efficient approach to handling thisissue. Nevertheless, the frequent switching between states, duringscheduling, leads also to significant energy consumption. In thisarticle, a coverage-guaranteed distributed sleep/wake schedulingscheme is presented with the purpose of prolonging networklifetime while guaranteeing network coverage. Our scheme mit-igates scheduling process to be more stable by avoiding uselesstransitions between states without affecting the coverage levelrequired by the application. The simulation results illustrate thatout scheme outperforms some other existed algorithms in termsof coverage guarantee, energy conservation and stability.

Keywords—Coverage; Stability; Eligibility; Energy Efficiency.

I. INTRODUCTION

Coverage, which reflects how well a sensor field is mon-itored, is one of the most important performance metrics tomeasure sensor networks. Sensor coverage model, like manyother sensor characteristics, such as transfer function, sensi-tivity, dynamic range, accuracy, etc., can be used to measurethe sensing capability and quality of a single sensor. Simplysaid, sensor coverage models are abstraction models, trying toquantify how well sensors can sense physical phenomena atsome location; or in other words, how well sensors can coversuch locations. The coverage approach can be centralized ordistributed [6]. With the centralized approach, the algorithmruns at a special station (usually a base station) where theenergy, communication and computation constraints can beignored. Centralized approaches [12] [13] [4] always requirethe global information of the whole network, run slowlyand have low adaptability to the changes of the network.Conversely, with distributed ones [7] [11] [8], the decisiveprocess is locally and simultaneously carried out at each sensornode which needs only local information, thus being moreadaptable to the dynamic and scalable nature of the network.That is the reason why the distributed algorithms are preferredto centralized ones in wireless sensor networks [9].

Reducing energy consumption, during coverage process, isan important task. Scheduling (or Duty cycling) is an efficientway to reduce energy in the distributed algorithms. It consistsin putting some sensor nodes into sleep mode then allowingalternation between sleep and active modes.

Most distributed solutions aim to ensure coverage usingscheduling as an energy conservation tool (see section 2). How-ever, no work has referred energy waste during the schedulingitself. In fact, among the reasons why sensors may lose energy,two reasons are retained [3] [14]:

• Frequent switching between modes, essentiallyswitching from sleep mode to an active mode, leadsto significant energy consumption.

• Moreover running in Listen mode (or idle mode)is also costly and results in significant high energyconsumption, because the radio electronics have beenturned on and continually decode radio signals todetect the presence of incoming packets.

In this paper, we propose (SEEC), a new coverage protocolwith a stable scheduling i.e. a scheduling that avoids uselesstransitions between states and, at the same time, allows nocoverage level loss. As for knowledge, our protocol is the firstto explore stability issue conjointly with coverage preservingprocess.

The rest of the paper is organized as follows. In Sec-tion 2, we review the related work. The Motivation and theproblem definitions are provided in Section 3. We presentour Scheduling in Section 4. In Section 5, we evaluate theperformance of the proposed algorithm through simulations.Section 6 concludes the paper.

II. RELATED WORK

We will investigate, in this section, some centralized anddistributed coverage-preserving scheduling algorithms. Withthe centralized approach, the algorithm runs at a special station(usually a base station or cluster head) where the energy,communication and computation constraints can be ignored.A cluster-based hierarchical network was considered in [12],in this structure, sleep/wake scheduling problem was illustratedbased on multi-hop communication, this article considered theeffect of synchronization error in their sleep/wake schedulingalgorithm. Most of computation tasks are performed in a basestation which uses the sub-gradient method and computesthe capture probability thresholds, then tells the sensor nodesand the nodes decide the wake-up schedule themselves. Acentralized sleep scheduling algorithm based on integer linearprogramming was presented in [13], which calculates thelifetime using the global information of the whole network

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based on the assumption that the global knowledge of sensorlocations and energies is known. According to their proposedscheme, sensors allowed to sleep can be intermittently inac-tive to reduce energy consumption and thus extend networklifetime. In [4], the authors assumed that all sensors weresupplied with approximately the same amount of initial energyand studied the coefficient of variation of energy consumptionof three different sleep scheduling schemes: the randomizedscheduling (RS) scheme, the distance-based scheduling (DS)scheme, and the Balanced energy Scheduling (BS) scheme.The proposed algorithm is also performed by a cluster head.More accurate scheduling results will be achieved by elimi-nating the centralized approaches, which also lead to a largeamount of data transmission and computation.

In distributed scheduling, the decisive process is locallyand needs only local information. The Sense-Sleep Tree (SS-Tree) [7] uses flow models and mathematical programming tothe network in accordance with the classification tree structureto solve sleep scheduling. It uses the tree structure of thenetwork scheduling and graph theory was applied to form SS-Tree, the method has a high computing complexity and cannotwork in the complex situation. The authors in [10] investi-gated the cross-layer sleep/wake scheduling design in service-oriented WSNs, the purpose of this study is to minimizethe energy consumption and guarantee that enough sensorsare active to provide all required network performances. Thesleep scheduling is considered to be NP-hard, and a heuristiclinear programming based solution is also presented. However,they assume that each service has a known requirement onthe number of active sensors based on the historical servicecomposition requests in the system, which may not be the casein practice. In [8], each node in the network autonomously andperiodically makes decisions on whether to turn on or turn offitself only depending on its local neighbour information. Topreserve sensing coverage, a node will turn it off when otheractive neighbours can help it to cover its whole working area.Optimal Coverage- Preserving Scheme (OCoPS) [2] extendsthe center angles calculation method described by [8], based onthe proposal of a wake-up strategy, a new decision algorithmis illustrated to decide the node status by exchanging localinformation. Aiming at dynamic point coverage, a schedulingalgorithm based on learning automata is proposed in [5],the advantage is that less auxiliary messages are needed tobe delivered between nodes, and each node in the networkis equipped with a set of learning automata which determinewhen and which node should be in active or asleep stateaccording to environmental information. In [11], Wang et alintroduced (CCP), a distributed algorithm, that can providek-coverage for the network with k being arbitrary. The k-coverage eligible algorithm is suggested for each sensor to testthe eligibility to withdraw or to be active, then to launch a verydynamic scheduling. In CCP, Each Sleeping node periodicallyturns its radio on and enters the Listen state to receive Hellomessages and re-evaluate its eligibility. When a network isdeployed, all nodes are initially in the Active state. If an areaexceeds the required degree of coverage due to high density,redundant nodes will find themselves ineligible and switch tothe Sleep state until no more nodes can be turned off withoutcausing an insufficient degree of coverage. Over time, an activenode may run out of energy which may cause the degree ofcoverage to decrease below the desired level. In this case,

some nodes originally in the Sleep state will find themselvesbecoming eligible and enter the Active state so that the networkregains the desired degree of coverage.

III. MOTIVATION AND PROBLEM STATEMENT

Recent technological breakthroughs in ultra-high integra-tion and low-power electronics have enabled the developmentof tiny battery-operated sensors. Given the interest in usingsuch sensors, in unattended deployment, in usually harshenvironments, the replenishment of sensor batteries might beimpossible and thus, sensors are energy constrained and theirlifetime strongly depends on how long their batteries last. Inaddition to the sensing ability, which is the main task, a sensortypically performs signal processing and data transmission.

Generally, the major sources of energy wastage could beenumerated as follows [14]: The first source is overhearing,meaning the node receives packets that are destined to othernodes. The second, the overhead of sending and receivingmedium access control packets. The third source is collisionsin which multiple packets get transmitted simultaneously,magnifying the signal interference and thus mandating retrans-missions. The fourth is the wireless noise in which packetsget corrupted and need to be retransmitted or to increase thetransmission power to overcome the noise level. The fifth causeof energy wastage is the excessive periods of being in an idlestate. Finally, frequent switching between modes, especiallyswitching from sleep mode to active, leads to significant energyconsumption.

Typically, a radio can operate in four distinct operationmodes: Idle (Listen), Receive, Transmit, and Sleep.While theradio is expected to consume more energy in the Transmitand Receive modes, running in the Listen mode is found alsocostly.

Table I summarizes steady energy and switching energy foran 802.11 node [15].

TABLE I. TYPICAL VALUES OF ENERGY COSTS RELEVANT TO

DIFFERENT MODES IN AN 802.11 NODE [15]

Card Pidle(mW ) Psleep(mW ) Psw.tsw(J)Cabletron 830 130 1.328

Lucent Wavelan 843.7 66.3 0.6Cisco Aironet 350 1150 140 0.6

According to the section of Related work, we notice thatdistributed solutions used scheduling as an energy conservationtool. But the frequent switching between states during schedul-ing leads also to significant energy consumption. Notice thatno work has referred to energy waste during the schedulingitself.

We focus on the fifth and the sixth cases, referred previ-ously, we suggest a new coverage whose scheduling is a stableone, i.e. a scheduling that avoids useless transitions betweenstates and allows no coverage level loss, as required by theapplication.

IV. DISTRIBUTED SLEEP SCHEDULING ALGORITHM

Let’s consider a set of sensors S={ s1, s2, ..., sn }scattered over a given geographical area A, The sensors are

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homogeneous and each has two rays a capture range Rs and acommunication range Rc. The goal is to continuously monitora capture area and preserve -as long as possible- a certain levelof coverage, without wasting sensors’ energy of the set S.

In this section, we describe our proposal of an energyconservation and coverage distributed protocol, named SEEC.SEEC stands for (Stable and Energy Efficient Coverage Proto-col For Wireless Sensor Networks) Each sensor of set S runsSEEC independently.

In SEEC, the sensor switches between four states: Sleep,Listen, Active and Exhausted.

• In Sleep state, a sensor turns the radio off.

• In Active state, a sensor actively senses the environ-ment and communicates with other nodes.

• In Listen state, a sensor evaluates the eligibility

• In Exhausted state, a sensor doesn’t participate toeligibility evaluation.

SEEC uses only two types of messages; Join and Hello (theformer informs the neighbours about the future active state andthe latter prompts neighbours to compute their eligibility).The Ks-coverage eligibility algorithm proposed in [11] is alsoused in SEEC to determine whether it is necessary for asensor to become active or not. Given a requested coveragedegree Ks, a node v is ineligible if every location within itscoverage range is already Ks-covered by other active nodes inits neighbourhood [11].

A. Ks-coverage eligibility algorithm

The Ks-coverage eligibility algorithm proposed in [11] isexecuted in order to determine whether it is necessary for asensor to become active or not. Given a requested coveragedegree Ks, a node v is ineligible if every location within itscoverage range is already Ks-covered by other active nodes inits neighbourhood [11]. The following notations are defined:

• The sensing region of node v is the region inside itssensing circle, i.e, a point p is in v’s sensing region ifand only if |pv| < Rsi .

• A point is called an intersection point between nodesu and v, i.e., p ∈ v∩u, if p is an intersection point ofthe sensing circles of u and v.

• A point p on the boundary of the coverage region Ais called an intersection point between node v and A,i.e. p ∈ v ∩ A if |pv|= Rsi.

An example is showed in Figure 1, s3 is ineligible fork=1, but eligible for k>1.

Authors in [11] proved that a convex region A is Ks-covered by a set of nodes if all intersection points betweenany nodes are at least Ks-covered and all intersections pointsbetween any node and A’s boundary are at least Ks-covered.

Theses observations allow transforming the problem ofdetermining the coverage degree of a region to the simplerproblem of determining the coverage degrees of all the in-tersection points in the same region. A node is ineligible if

s1

s2

s3

s4

s5

s6

Fig. 1. An example of eligibility rule.

Algorithm 1 Ks-coverage eligibility algorithm

Beginfind all intersection points inside C(v);SI = {p|(p ∈ u ∧ v OR p ∈ u ∧ A) AND u,w ∈SN(v)AND |pv| < Rs}find all coinciding sensors;SC = {u| |uv| = 0};if(SI = 0);{if(SC ≥ Ks) return INELIGIBLEelse return ELIGIBLE};for each poin p ∈ SIbeginsd(p) = |{u | u ∈ SN(v) AND |pu| < Rs}|if(sd(p) < Ks) return ELIGIBLEend

return INELIGIBLEEnd

Fig. 2. Ks-coverage eligibility algorithm.

all the intersection points inside its sensing circle are at leastKs-covered.

The resulting Ks-coverage eligibility algorithm is shown inFigure 2.

According to the previous paragraph, we find that theKs-coverage eligibility algorithm is a very effective tool todetermine the suitability for a sensor to become an activesensor or not. We also note that this algorithm relies only onpurely geometric properties, i.e., it is completely independentof the messages received or sent (Figure 2) .

Therefore, we will benefit from its important properties foruse in SEEC. Indeed, the Ks-coverage eligibility algorithm willbe called as a function that returns a Boolean (TRUE to saythat the sensor is eligible to be active or FALSE otherwise);and from this information the SEEC scheduling decides of thenext state of passage.

B. SEEC scheduling

Initially, when the network is first deployed, all sensors arein Listen state, the sensor initiates a Listen timer Tl and runsthe eligibility algorithm. We consider two cases (Figure 3):

• First case:The sensor is EligibleThis means that the sensor is likely to become active.In this case, the sensor initiates a join timer Tj and

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waits for a certain while Tj before activating. If itreceives, in the meantime, a Join message from aneighbour active node, it recalculates its eligibility, ifit is eligible then it sets TJ and waits for a certain whileTj before activating, else (if it is ineligible), activationwill not be necessary because there is another activeneighbour node which can cover the area. Then thesensor cancels Tj and joins sleep state. Conversely, ifTj expires without receiving any message, the nodebroadcasts a join beacon to inform its neighbours ofits future active state, and then returns effectively toactive state.Indeed, we introduce a new state named Exhausted,the transition to this state is only from the active state,and if the activity timer Ta>Threshold, this state rep-resents the sensors that will run out very soon to avoidunnecessary execution of the Ks-coverage eligibilityalgorithm. Thus, the sensors that fall within that statewill never participate in the eligibility calculation, butthese nodes are not disconnected from the network, oncontrary, they devote the rest of their energy for theother activities in the network (routing, connectivity,...) until they effectively die.

• Second caseEither the sensor is ineligibleor Tl expires without receiving any messages.

In both cases the wake of such a node is not important.A sleep timer is calculated then started and the nodereturns to sleep state. This aspect allows coveragecontinuity between neighbours. At the end of Ts, thenode goes to Listen state.

We notice that SEEC starts by reducing the number ofstates (only four states) which leads to a reduction of thefrequent transition between them for stability and more energyconservation. SEEC decreases also the number of exchangedmessages (only two messages Hello and Join); the Hellomessage is sent only once from the sensor which is in theListen state, prompting neighbours to compute their Eligibility.The Join message is broadcasted by a sensor just before itbecomes active in order to inform the neighbours about thefuture active state; remember that one of the major sourcesof energy wastage is the overhead of sending and receivingpackets (see section 3). Other improvements can be referredto: SEEC decreases also the number of the execution of theKs-coverage eligibility algorithm and detects the nodes closeto exhaustion, these nodes will be isolated and not involved inthe calculation of eligibility, since they will be exhausted aftera short time: In Sleep state and after the eligibility calculation,the sensor remains in this state for a time equivalent to theestimated lifetime of an active neighbour that has the lowestamount of energy. In fact, an active sensor with low-energy willbe replaced by another active sensor before it is exhausted.

Note that the values of Tj and Tl affect the responsivenessof SEEC. Shorter timers lead to quicker response to variationsin coverage, both timers are also related to the density of nodesin the network. For example, for a denser network where anode has more neighbours, both timers should be increased togive a node enough time to collect the Join or Hello messagesfrom its neighbours. For the time being, both the Join and

START

State=

Listen

- BroadcastHello

- Set Tl

Tlexpires?

Eligibility()

Sensoreligible?

Set Tj

Tjexpires?

RCVJoin?

Sensoreligible?

-BroadcastJoin

- Set Ta

State=

Active

Ta>

Tthreshold?

State=

ExhaustedEND

- CalculateTs

- Set Ts

State=

Sleep

Tsexpires?

Cancel Tj

No

Yes

Yes

No

Yes

No

Yes

Yes

Yes

No

Yes

Yes

Fig. 3. SEEC Scheduling.

Listen timers in SEEC are randomized.

V. PERFORMANCE EVALUATION

To evaluate SEEC performance, a set of simulation ex-periments were carried out and compared to the results ofCCP protocol [11] using the Network simulator NS-2 andthe settings defined in Table II. We chose CCP for severalreasons: First, CCP is a reference protocol and it is amongthe firsts coverage protocols based sleep scheduling techniqueand most protocols that have been proposed subsequently usethe same technique (scheduling). In addition, CCP and SEEC

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TABLE II. SEEC PARAMETERS.

Space 1000 m2

Nodes 10-1024 (Uniformly distributed)Propagation model TowRayGround

Sensing range (Rsi) 20mCommunication range (Rci) 200m

Mac 802.11Initial energy 200 JIdle energy 0.75 W

Sleep energy 0.025 WTransmission energy 1.90 W

Reception energy 1.50 W

0 20 40 60 80 100

20

40

60

80

100

T ime(s)

Coverage(%

)

CCP

SEEC

Fig. 4. Coverage vs. Lifetime, (n = 225)

uses the exactly the same mechanism to evaluate the eligibility(the eligibility algorithm), except that SEEC reduce frequentswitching between states. Our objective is to quantify theenergy gain when using a stable scheduling, then to comparethe results with the closest protocol to SEEC, which is CCPin this case.

A. Coverage

We perform some simulations on the coverage (Figure 4).The purpose of calculating coverage is to evaluate the effectof our scheme SEEC on monitoring when sensors lose energy.Initial coverage is used as the base value (100 %) and iscompared with coverage after our scheme is applied. The goalhere is to show that our scheme does not significantly affectthe initial coverage while turning off sensors. When densityreaches 255 nodes in 1000 m2, initial coverage can cover entirearea. Moreover the performance comparison of CCP with ourscheme shows that at the 40th second, the network lifetimewas approximately at 70 % compared to the 55 % under CCP.

B. Network lifetime

• Network lifetime vs Energy

To know the network lifetime, based on energy, we consultedthe trace file that gives, at each moment, the energy level ofeach sensor (Figure 5(a)). We get the time from the first sensorwhose energy is null. At time T= 138.5 seconds for the CCP,the first sensor is exhausted; whereas for SEEC, the first sensoris exhausted at time T = 142.6 seconds. So the network lifetimeis 138.5 seconds for CCP and 142.6 seconds for SEEC.

• Network lifetime vs messages reception

To know the network lifetime, based on message reception,we consulted the file that provides information on message

(a) (b)

200

400

600

800

Lif

etim

e(s)

SEEC

CCP

Fig. 5. Network lifetime

100 200 300 400 500 6000

1

2

3

·104

T ime(s)

Numberoftransitions

SEEC

CCP

Fig. 6. Stability in transition numbers vs. Lifetime (n = 1000)

transmission and reception (Figure 5(b)). Our simulationsproduced the following results: The last message received is attime T = 377.6 seconds for CCP, and time T = 788.8 secondsfor SEEC .These results confirm those found in the lifetimecalculation versus energy. In other words, SEEC extends thenetwork lifetime more CCP.

C. Stability

A stable protocol as a protocol able to maintain, as longas possible, the coverage level with a minimum switchingbetween states.

The number of transitions in the protocol SEEC is signif-icantly lower than that of CCP, during the entire simulationperiod (Figure 6). This is due to the stable scheduling ofSEEC that avoid frequent switching between states. We willsee, in the next simulations, that the stability aspect affectssignificantly the energy consumption.

D. Energy consumption

A sensor node consumes energy when receiving, sending,and even listening to a message on a According to thegraph in Figure 7, we note that at the beginning, the energyconsumption rate is slightly higher for SEEC. But thereafter,both energy consumption rates increase over time. But thisincrease is more important in CCP than in SEEC. Note thatwhen all the energy is exhausted in CCP, some still remainsin SEEC. this is due to the energy conservation in SEECscheduling, caused by the reduction of exchanged messages,the reduction of the number of the eligibility runs, and thereduction of the frequent switching between states. At 240 sfor example, the average energy cost of SEEC reaches 22,62% energy saving when compared to the CCP scheme.

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200 400 600

0

20

40

60

80

100

T ime(s)

Energy(w

att)

CCP

SEEC

Fig. 7. Energy vs. Lifetime, (n = 1000)

0 200 400 600 800 1,000

0

200

400

Sensors

Activesensors

CCP (k=1)

CCP (k=2)

SEEC (k=1)

SEEC (k=2)

Fig. 8. Number of Active nodes

E. Number of Active nodes

The Figure 8 shows the percentage of the active nodesin network for the two schemes It can be seen that SEEChas more active nodes than CCP. This is due to the SEECscheduling which keeps active sensors in their same state(active state) until their exhaustion. Conversely, the numberof active nodes in CCP is lower than in SEEC because CCPallows active nodes to sleep after a certain activity time.This aspect doesn’t influence the coverage or the energyconsumption as we have seen in previous simulations.

VI. CONCLUSION

In this paper, we proposed a new coverage preserving pro-tocol for wireless sensor networks based on a stable schedulingalgorithm that avoid useless transition between states. Theproposed approach is strong in two main aspects: Firstly, itis adaptable and practically feasible for dynamic topologiessince the proposed algorithm is distributed. Secondly, thescheme preserves coverage level while explicitly minimizingenergy consumption. The algorithm’s simplicity eases imple-mentation and deployment. This also reduces computationalcomplexity and it decreases energy consumption depending oncomputational tasks. Our experimental results show that ourexhibits better performance when compared to CCP protocol.We plan to make SEEC adaptable to different coverage degree(Ks=3,4...), and to compare the SEEC complexity with othercoverage preserving algorithm. In addition, we should pointout that ranking the expiration time of the different timersaccording to the ’utility’ of the node may result in a bettercoverage results. For instance, a node that will cover more un-covered area should have a shorter join timer when competingwith other competing nodes. This aspect will be developed infuture work.

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