Real-Time Implementation of a Harmony Search Algorithm-Based Clustering Protocol for Energy-Efficient Wireless Sensor Networks

774 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 1, FEBRUARY 2014

Real-Time Implementation of a HarmonySearch Algorithm-Based Clustering Protocol forEnergy-Efficient Wireless Sensor NetworksDuc Chinh Hoang, Student Member, IEEE, Parikshit Yadav, Student Member, IEEE,Rajesh Kumar, Senior Member, IEEE, and Sanjib Kumar Panda, Senior Member, IEEE

Abstract—In real-life applications of wireless sensor networks(WSNs), optimization of the network operation is required to ex-tend its lifetime. A framework is proposed that enables practicaldevelopment of centralized cluster-based protocols supported byoptimization methods for the WSNs. Based on this framework, aprotocol using harmony search algorithm (HSA), a music-basedmeta-heuristic optimization method, is designed and implementedin real time for the WSNs. It is expected to minimize the intra-cluster distances between the cluster members and their clusterheads (CHs) and optimize the energy distribution of theWSNs. Thestudy of HSA cluster-based protocol is carried out in a real casewhere theWSNs equippedwith the proposed protocol are deployedin an indoor environment to monitor the ambient temperature forfire detection. A comparison is made with the well-known cluster-based protocols developed for WSNs such as low-energy adaptiveclustering hierarchy-centralized (LEACH-C) and a cluster-basedprotocol using FuzzyC-Means (FCM) clustering algorithm. Exper-imental results demonstrate that the proposed protocol using HSAcan be realized in centralized cluster-based WSNs for safety andsurveillance applications in building environments. From the ob-tained experimental test results, it can be seen that the WSNs life-time has been extended using the proposed HSA protocol in com-parison with that of LEACH-C and FCM protocols.

Index Terms—Cluster-based protocol, harmony search (HS),meta-heuristic, tinyOS, wireless sensor networks (WSNs).

I. INTRODUCTION

A wireless sensor network (WSN) is an intelligent and low-cost solution that enables the efficiency and reliability im-

provement of many industrial applications such as safety andsecurity surveillance, home and building automation, and smartgrids. The WSNs generally consist of a large number of sensornodes which are low-power and small in size [1]. These sensornodes can work as autonomous devices and be deployed in var-ious types of environments. However, there aremany challengesto bring the WSNs into real-life applications. One of the mainconcerns when developing the WSNs is to extend their lifetime.

Manuscript received November 30, 2012; revised March 23, 2013; acceptedJune 16, 2013. Date of publication July 17, 2013; date of current version De-cember 12, 2013. Paper no. TII-12-0799.D. C. Hoang, P. Yadav, and S. K. Panda are with the Department of

Electrical and Computer Engineering, National University of Singapore, Sin-gapore 117576 (e-mail: [email protected]; [email protected];[email protected]).R. Kumar is with the Department of Electrical Engineering, Malaviya Na-

tional Institute of Technology, Jaipur, India 302017 (e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TII.2013.2273739

In many applications, a sensor node is powered by a finite en-ergy source such as a battery or a super capacitor that restrictsthe WSNs’ lifetime. The renewable energy sources like solaror wind have been investigated and integrated with the sensornodes recently for longer operation [2]–[5]. However, the in-termittent nature of these sources still has a significant effecton the network performance. Therefore, energy consumption oftheWSNs needs to be taken into account when planning the net-work operation.Cluster-based routing protocols are well-known techniques

that enable the operation of WSNs to be highly energy-effi-cient. They also have special advantages related to scalabilityas well as efficient communication [6]. The basic principle ofa cluster-based protocol is to organize the sensor nodes intogroups called clusters. In each cluster, a node is selected as thecluster head (CH) that has the responsibility to collect data fromother cluster members, aggregate, and forward the compact in-formation to a base station (BS). By using this principle, it isable to reduce the amount of data transferred within the net-work, thus energy saving is achieved. Many cluster-based pro-tocols have been proposed with the objective to maximize thenetwork lifetime in the literature such as energy-aware routing,TEEN,APTEEN, PEGASIS, low-energy adaptive clustering hi-erarchy (LEACH), and Fuzzy C-Means (FCM) [7]–[15]. How-ever, most of these protocols are studied by using simulationtools. Approximated models of the networks are used, hence theinvestigated performance may not represent the exact behaviorof the systems in practice.For example, the LEACH, which is a typical cluster-based

protocol working in a distributed manner, selects CHs based ona predetermined probability in order to rotate the CH role amongthe sensor nodes and avoid fast depletion of the CH’s energy[12]. As most of the cluster-based protocols, the operation ofLEACH consists of two phases: setup phase and data transmis-sion phase. During the setup phase, the formation of the clus-ters is carried out. In the data transmission phase, informationare acquired by sensor nodes and transferred to the BS. Simula-tion-based study of LEACH considers only energy consumptionfor receiving the advertisements from CHs at each sensor nodeduring the setup phase. However, since nodes elect themselvesto the CH randomly, the exact moment of transmitting the adver-tisement messages from the CH is unknown for the other nodes.Therefore, nodes must keep listening for these messages, andmore energy is consumed in practice. Furthermore, it is statedthat the operation of LEACH is divided into rounds, but how thenodes are synchronized to follow this scheme is not discussed.

1551-3203 © 2013 IEEE

HOANG et al.: REAL-TIME IMPLEMENTATION OF A HSA-BASED CLUSTERING PROTOCOL FOR ENERGY-EFFICIENT WSNs 775

LEACH-centralized (LEACH-C) is an improvement ofLEACH, which uses a centralized clustering mechanismto form the clusters [13]. LEACH-C enhances the networkperformance by producing better clusters by dispersing theCHs throughout the network. Lee et al. [14] have proposed afuzzy-logic-based clustering approach with an energy-predic-tion mechanism for LEACH during the CH selection to furtherenhance the network lifetime. Other protocols using the clus-tering algorithms like K-means and FCM to achieve a betterformation of clusters with more uniform distribution of sensornodes have also been introduced in [15] and [17]. However, allof these protocols are investigated through simulation studies;it is required to employ an approximated radio energy modeldescribed in [12] and [13] to evaluate network performance.A centralized cluster-based protocol using harmony search

algorithm (HSA), a music-based metaheuristic optimizationmethod, has been proposed in [26] that considers both energyefficiency and minimized intra-cluster mean distances in thesetup phase to obtain the optimal cluster formation and selectionof the CHs. This problem can be seen as an NP-hard problem[16]. The classical clustering methods may fail to find theoptimum solution of this problem and often get trapped in localminima, since these methods are highly sensitive to startingpoints and frequently converge to a local optima or divergealtogether [18]–[20]. Metaheuristic algorithms, such as geneticprogramming, evolutionary programming, genetic algorithms(GA), differential evolution, HSA, ant colony optimization,particle swarm optimization (PSO), and Bee algorithm, erad-icate some of the aforementioned difficulties and are quicklyreplacing the classical methods in solving practical problems[21]–[24]. HSA is one of the recently developed evolutionaryoptimization algorithm developed by Geem et al. [25] in 2001,and it is inspired by the music improvisation process. Theperformance of the protocol using HSA is investigated viasimulation and compared with the conventional methods andother evolutionary algorithms such as GA and PSO in [26] toillustrate the enhancement of network lifetime. However, tothe best of our knowledge, the realization of such cluster-basedprotocols for WSNs together with integration of evolutionaryalgorithms is hardly reported in the literature. A design frame-work have been proposed to enable the implementation ofWSNs for high-level real-time industrial applications [27].However, the framework is used to design high-performancenetworks like Ethernet or WLAN for industrial automaticcontrol applications where the quality of service (QoS) is themain focus. Hence, it is essential to develop and study thecluster-based protocols for real-time operation where sensornodes have resource constraints such as weak computationalability, low memory, and limited energy.In this paper, a framework for the design of centralized

cluster-based protocols for WSNs has been proposed. Thecentralized mechanism of clustering the network enables itto take the benefit of the strong computation ability of theBS. Therefore, the optimization algorithm like HSA can beexecuted in a reasonable period of time for real-time operation.Based on this framework, a protocol using HSA is designedand practically implemented on a WSN test-bed. The HSA isimplemented and run at the BS, which consists of a computer

connected to a gateway node. Meanwhile, other services of theprotocol for exchanging network information and transferringdata are developed on IRIS mote [28], a hardware platform ofthe WSN from MEMSIC with the support of the embeddedoperating system, TinyOS [29]. Online configuration of thenetwork is performed by HSA at the BS in order to optimizethe formation of the clusters as well as the selection of the CHs.The objective is to improve the lifetime of the WSNs and makeit applicable in a real-life application like ambient temperaturemonitoring for fire detection.The remainder of this paper is arranged as follows. Section II

provides the preliminaries and assumptions for the WSN test-bed. The clustering protocol is described and formulated as anoptimization problem in Section III. Section IV introduces thestate-of-the-art HSA used for the formulated problem. The de-sign and practical implementation of the protocol using HSAare presented in Section V. Experimental results and discussionsare provided in Section VI. Finally, we conclude our findings inSection VII.

II. PRELIMINARIES

TheWSN test-bed consists of a number of sensor nodes thoseare randomly deployed in an area of interest. Each sensor nodecan operate either in sensing mode to perceive the environ-mental parameters or in communicationmode to send data to theBS periodically. When operating in the communication mode,these sensor nodes can either be assigned the role as clustermember or CH. The cluster members only transmit data to thecorresponding CH, whereas the CH is responsible for gatheringdata packets from the cluster members. Each CH compressesthe gathered data into one single packet and forwards it to theBS. The role of being a cluster member or a CH can be changedduring the network operation basing on the solution of the clus-tering algorithm.The laboratory-based WSN test-bed has the following

features:• sensor nodes as well as BS are stationary after being de-ployed in the field;

• the network is considered homogeneous, and all of thesensor nodes have the same initial energy;

• each sensor node knows its own geographical position thatis set during the installation of the nodes;

• all nodes measure the environmental parameters at a fixedrate and send the data periodically to the receiver nodes,those are either the CHs or the BS;

• the BS and all of the nodes in the network are able to com-municate directly during the network setup phase in orderto exchange the information of nodes status;

• nodes have the capability of adjusting their transmissionpower suitable to transmit data to the CH or the BS duringthe data transmission phase.

III. OPTIMIZATION PROBLEM FORMULATION AND

CLUSTERING PROTOCOL

A. Optimization Problem Formulation

A WSN of sensor nodes randomly deployed into an envi-ronmental field are organized into clusters: . In


the proposed centralized cluster-based protocol for this WSN,the BS needs to select the CHs with higher residual energyamong the sensor nodes and then forms the clusters with equaldistribution of the sensor nodes based on their information of lo-cation and residual energy. This process can be formulated as anoptimization problem and mathematically expressed as shownin

where

(1)

As presented in (1), consists of two parts. The first partis the maximum of the total of the Euclidean distance of the

nodes to their CHs and is thenumber of nodes that belong to cluster . By minimizing , ittends to minimize the intra-cluster mean distance between thesesensor nodes and their respective CHs. Meanwhile, the secondpart is the sum of the ratio of present residual energy of allalive nodes in the network with the presentenergy level of the , which is , in the present round.By minimizing the objective function , it is expected thatthe cluster formation and the CH selection of the WSN can beoptimized for increasing the efficiency of energy consumptionwithin the network. In a practical system where each sensornode is powered by batteries, the residual energy of node ,

can be represented by its present battery terminal voltage. The locations of the nodes are set during the installation

in this study, however, it can be obtained by implementing lo-calization services as addressed in [30]. In order to minimize ,the needs to be minimal, while must bemaximized. The sensor node with higher energy level withineach cluster tends to be the CH. Hence, minimizing resultsin the selection of the optimal CHs in terms of residual energyin the network.The constant indicates the contribution of and in the

objective function . In order to get rid of the selection ofthe nodes with low energy level to be the CH, the CH nodesare selected in the set of candidates: .Only the sensor nodes which have an energy level higher thanthe average energy level of all of the nodes in the networkcan be the CH candidates.

B. Clustering Protocol

The operation of the protocol includes two phases: clusteringsetup phase and data transmission phase. In clustering setupphase, sensor nodes are grouped into clusters in such a way thatthe objective function (1) is optimized. Then, the CHs collectdata from all cluster members and transfer to the BS during thedata transmission phase. The two phases are performed in eachround of the network operation and is repeated periodically.1) Phase 1: Clustering Setup: In the clustering setup phase,

the sensor nodes send ADVERTISEMENT (ADV) messagesto the BS with the information of their geographical locations

and battery voltage representing the residual energy level of thenodes; this information is later forwarded by the gateway nodeto the computer for calculation. Once the optimal cluster forma-tion of the network is found, the BS attaches the information ofthe CH and to which cluster the member nodes belong into theASSIGNMENT (ASG) message and transmits this message toevery sensor node.The network can be clustered as shown in Algorithm 1.

Algorithm 1 Cluster Formation.

repeat

Initialize randomly selected CHs

for to do

Calculate the distance betweenand all CHs .

Assign to the cluster in whichis minimum

end for

Compute the value of the objective function given in (1)

Update the CH by using HSA

until The maximum number of iterations is reached

After the clusters are created, the data transmission phaseis performed that allows the cluster members of each clusterto send data towards the BS through the CHs. The process offorming clusters is repeated periodically whenever the datatransmission phase is completed.2) Phase 2: Data Transmission: After all of the nodes re-

ceive the ASGmessage, and the transmission schedule is initial-ized, the sensor nodes start to perform sensing task and transmitdata to the CHs. Transmission power of cluster member nodes isoptimized because of the minimum spatial distance to the CHs.A loose time synchronization process is created amongst thenodes of each cluster. The CH radio component is turned onfirst at time as shown in Fig. 1(b), and it is kept on untilall of the cluster members’ data packets are received or a pre-defined listening time period is over. Then, it is turned off attime . Meanwhile, cluster member nodes turn on their radiocomponent for a very short period of time which isin order to transmit data. This period of time is guaranteed that

and for every cluster member in thecluster as shown in Fig. 1(a). Data aggregation and fusion arecarried out at the CHs, and only the compressed data packet issent from the CHs to the BS, thus the amount of informationtransmission is reduced that results in the reduction of energyconsumption.

IV. HARMONY SEARCH ALGORITHM

This section describes how HSA is designed and applied tooptimally cluster the WSNs during the setup phase. The HSA isinspired from musical process of searching for a perfect state ofharmony [25]. In the HSA, musical performance seeks a perfect


Fig. 1. Data transmission phase within a cluster for different types of sensornodes. (a) Cluster member. (b) Cluster head.

state of harmony determined by aesthetic estimation, as the op-timization algorithm seeks a best state (i.e., global optimum) de-termined by objective function value. It has been very successfulin a wide variety of optimization problems, presenting severaladvantages with respect to traditional optimization technique[31], [32]. HSA imposes fewer mathematical requirements anddoes not require initial value settings of the decision variables.Therefore, it is potential to be implemented in real-time systemslike WSNs. The HSA approach used to minimize the objectivefunction (1) consists of five main steps as explained below.Step 1) Initialize the optimization problem and algorithm

parameters. In the formulated problem, HSA isapplied to minimize the intra-cluster distances andoptimize the energy consumption of the network,which is defined by the objective function as givenin (1). The solution vector is the identification (ID)of the CHs among the candidates in the net-work. Harmony Memory Size (HMS), the numberof solutions vector in Harmony Memory Matrix, isselected. It is similar to population in GA. Otherparameters used to create new solution vector suchas Harmony Memory Considering Rate (HMCR)and Pitch Adjusting Rate (PAR) are initiated. Themaximum iteration of executing the algorithm(stopping criterion) is also set.

Step 2) Initialize the harmony memory (HM). A HM con-sisting of an HMS number of solution vectors forthe formulated problem is randomly generated. ThisHM with the size of HMS can be represented by

......

. . ....

...(2)

Each row of the HM is a random solution vectorcontaining elements. These elements are the IDs of

different CHs selected from the set of candidates.The value of the objective function given by (1) iscomputed for each harmony vector and representedby .

Step 3) Improvise a new harmony from the HM. Afterdefining the HM as shown in (2), the improvisationof the HM is carried out by generating a new har-mony vector . Each componentof the new harmony vector is generated using

with probabilitywith probability (3)

based upon the HMCR defined in step 1), whereis the th column of the HM is de-

fined as the probability of selecting a componentfrom the members, and is,therefore, the probability of generating it randomlyfrom the set of candidates. If this rate is too low, onlyfew elite harmonies are selected and it may convergetoo slowly. If this rate is extremely high (near 1), thepitches stored in the harmony memory are mostlyused, and newer pitches are not explored well.If is generated from the HM, then it is furthermodified or mutated according to . Thedetermines the probability of a candidate from theHM to be mutated and is the probabilityof doing nothing. The Pitch adjustment for the se-lected is given by

with probabilitywith probability (4)

where is nearest node whose energy is greaterthan the average residual energy.After each new element is selected, it is elim-inated from the set of CH candidates to avoidduplicated node IDs in the harmony vector

when generating other ele-ments.

Step 4) Update the HM. The newly generated Harmonyvector is evaluated in terms of the objective func-tion value. If the objective function value for the newHarmony vector is better than the objective func-tion value for the worst harmony in the HM, thennewHarmony is included in the HM and the existingworst harmony is excluded from the HM. The so-lution vector with the smallest fitness value can beconsidered the optimal solution of the problem in thecurrent iteration.

Step 5) Go to step 3 until termination criterion is reached.The current best solution is selected from the HMafter the termination criterion is satisfied. This is thesolution for the optimization problem formulated.

V. DESIGN AND IMPLEMENTATION OF HARMONY SEARCHALGORITHM CLUSTER-BASED PROTOCOLS

The HSA cluster-based protocol (HSACP) is realized in real-life application usingWSNs to monitor the ambient temperatureof an indoor environment. The layer at the node level is designed


Fig. 2. Diagram of the routing framework for a centralized cluster-basedprotocol.

and developed with the support of the TinyOS operating system2.1.1. Meanwhile, at the BS level, where the HSA is performed,the program is written in Java language and run on a Linux-based computer.Fig. 2 illustrates the framework for designing the network

protocol. The BS and sensor nodes exchange the networkconfiguration information and store in their memory for sub-sequent usage. At the sensor nodes level, there are two maincomponents: Data Transmission Engine and Cluster SetupEngine. The Cluster Setup Engine contains three modules:Cluster Information Update, Data Transmission Schedule, andNode Information Update. The present status of the nodes suchas location and battery voltage are obtained by Node Infor-mation Update module, and then provided to the BS where itis stored in a node information table. The Data TransmissionSchedule module creates a schedule for the node to transferdata to the CH based on the setting values in the received ASGmessage. The Cluster Information Update module updates theinformation of the cluster configuration such as the ID of CHand the number of cluster members. The Data TransmissionEngine takes the responsibility of performing data aggregationand transmitting data packets with assistance of the DataAggregation and Packet Transmitting modules respectively.At the BS level, there are also two main components: ClusterManagement and Data Acquisition. The HSA is integratedin the Clustering Algorithms module of the Cluster Man-agement component. This module takes the responsibilityof selecting the CH and forming the clusters, the calculationresults are kept in the Cluster Information Table module asshown in Fig. 2. The Data Acquisition component at the BS isused for processing and storing data received from the sensornodes. The framework shown in Fig. 2 is applied to developthe proposed HSACP and later implemented on the hardwareplatform, as illustrated in Fig. 3. The routing layer is built ontop of the TinyOS ActiveMessageC component. The carriersense multiple access/collision avoidance (CSMA/CA) mech-anism is used for avoiding packet collision at the receiver bythe ActiveMessageC component during the data transmissionphase. In most of the existing cluster-based protocols proposedand evaluated by simulation, time-division multiple access(TDMA) mechanism is used instead of CSMA/CA due to itshigh efficiency in terms of energy consumption. However, avery strict time synchronization mechanism among the sensor

Fig. 3. Main components of the routing layer.

nodes is required to implement TDMA that results in difficultyfor practical implementation, especially when the topology ofthe network changes frequently [33]. Meanwhile, CSMA/CAis widely applied and integrated as a built-in function in manytransceiver as it is simple, flexible, and robust [34]. By using thismechanism, a loose synchronization scheme within a clustercan be carried out that is much simpler and possible for prac-tical implementation. The component CluteringCtrlEnginePplays the role of sending the ADV messages and receivingthe ASG messages during the setup phase of cluster-basednetwork. During the data transmission phase, it provides theinformation of the CH as well as the data transmission timeschedule to the ClusteringDataEngineP component. The com-ponent ClusteringDataEngineP contains the interfaces to sendand receive data that are used by the application layer. Sincethe data type depends on specific applications, data aggregationis implemented at the application layer for easier extraction ofthe data.The operation of the network protocol at the BS and sensor

node is shown in the flowchart in Fig. 4. At the beginning ofthe operation cycle, all of the sensor nodes in the network sendan ADV message to the BS and keep the radio component onto wait for the ASG message to be sent by the BS. Once, theASG message is received, the sensor node extracts the infor-mation to identify whether it is a CH or a cluster member toset the operation of the data phase accordingly. The structureof the ASG message is provided in Fig. 5. The cluster mem-bers simply send data to their CH after performing the sensingtask.Meanwhile, the CH needs to collect data from its members,aggregates data with its own measurement, and then sends thecompressed data packet to the BS before turning off the radiocomponent as shown in Fig. 4.The BS takes the responsibility to control the formation of the

network and acquires sensing data from the nodes. During thesetup phase, the BS collects information of each sensor nodewhen receiving the ADV message which has the structure asshown in Fig. 6. It includes the location and battery voltage ofeach sensor node. The sensor node information received at theBS that consists of a gateway connected to the computer viaa serial port. Once the ADV messages from all of the nodesare collected, the HSA algorithm is executed on the computer.


Fig. 4. Flowchart of the network operation at the BS and sensor nodes. (a) Basestation. (b) Sensor nodes.

Fig. 5. Structure of an assignment message.

Fig. 6. Structure of an advertisement message.

When the computation is completed, the BS sends assignmentmessages to every node in the network which contains the in-formation of the CH as well as the time schedule for data trans-mission and cluster reorganization. The data acquisition is laterinitiated to obtain and display the sensing measurements of thenetwork.The implementation of this protocol is carried out on

Crossbow’s IRIS hardware platform [28] supported by theTinyOS operating system. An IRIS sensor node consists of alow power microcontroller, Atmel ATmega1281, and a trans-ceiver, AT86RF230 with an extension connector to interfacewith sensor boards. A 30 sensor node WSN is deployed in aroom of 20 m 20 m area. Each node is equipped with thesensor board MDA100CB that contains a thermistor YSI 44006[35]. The ambient temperature is perceived by the sensor nodesand sent to the BS via the CHs. By monitoring the temperature,

Fig. 7. Structure of data aggregation packet sent by the CH.

TABLE ITRANSMISSION POWER-LEVEL SETTINGS WITH RESPECT TO DISTANCES FOR

AT LEAST 90% SUCCESSFUL TRANSMISSION

the fire event can be detected. However, it is not necessary forthe BS to collect the measurements from all of the nodes inthe network that creates high traffic load at the BS. Abnormalincrement of temperature can be found by preprocessing thedata within each cluster at the CH level, the CH later informsthe BS only about the danger. Hence, the temperature value inthe data packet from each sensor node in a cluster is retrievedand processed at the CH level. Only the maximum, minimum,and average temperature values of the cluster are includedin the data aggregation packet sent from the CH to the BS asshown in Fig. 7. In order to reduce the computational burden forthe CH, the raw value read by the microcontroller is convertedinto Celsius degree before sending, the conversion follows theformula given in [35].The transmission power (TX_POWER) of each sensor node’s

transceiver is set based on the distance between the membernodes of the cluster and the CH in such a way that it guar-antees at least 90% successful direct transmission between thetwo nodes. The transceiver, AT86RF230 of the IRIS platformallows users to adjust the transmission power among 16 levelsthat can help to save the energy of the nodes. Since the areaof deployment field is 20 m 20 m, eight lower levels of theTX_POWER are used as shown in Table I. The values in Table I,obtained by experiment, provides setting values of TX_POWERto achieve at least a 90% success rate of direct data transmissionbetween two sensor nodes with respect to various communica-tion ranges from the sender to the receiver in the line of sight.The IRIS sensor node can be powered by any combination of

batteries with a dc output range of 2.7-3.3 V. Themost simplistictechnique to estimate the battery’s state-of-charge is to mon-itor its terminal voltage. Typically, a loaded AA battery suchas the rechargeable nickel–metal hydride (NiMH) has a voltageagainst the percentage of capacity discharged as shown in Fig. 8.Before starting the experiment, the batteries are fully chargedto their maximum capacity with the respective voltage .Every cycle of reorganizing the network, each sensor node mea-sures its present battery voltage and attaches this voltageto the advertisement message. In the first cycle of the networkoperation, the current battery voltage is considered as the initialvoltage of the respective node: . Both the values of

and of all of the sensor nodes are kept in a memorytable of the BS layer programme. The sensor node is identi-fied to run out of energy by the BS if .Hence, the voltage is used to represent


Fig. 8. Relationship between a AA size NiMH battery voltage and percentagesof its capacity discharged.

Fig. 9. Experiment setup and visualization. (a) Experiment setup of the WSN.(b) The GUI displays the topology of the network and sensing measurements.

the residual energy of the sensor node , which is in ofthe objective function (1).

VI. RESULTS AND DISCUSSIONS

Experiments are conducted for a 30-node WSN deployed asillustrated in Fig. 9(a). The sensor nodes form the cluster-basednetwork, perceive ambient temperature, and send the measure-ment to their CHs. The CHs aggregate these information andtransmit the compressed data packets to the BS. Network for-mation and monitored data are displayed on a graphic user in-terface (GUI) as shown in Fig. 9(b).

TABLE IISETTING VALUES FOR EXPERIMENTS

TABLE IIISTATISTICS OF THE FITNESS VALUES OF THE OBJECTIVE FUNCTION,

AFTER 50 TRIALS

The setting values installed for network configuration duringthe experiments are provided in Table II. Theses values can bevaried and optimized for different applications. In the presentcase of study, the values of time intervals in Table II are chosento shorten the experiment duration for observation.

A. Investigation of the Convergence and Computational Time

As presented in Section III, consists of two parts: and, which have the contribution to as defined by the coef-

ficient . By setting to 1 and 0, then executing the algorithmfor a 30-node WSN, it is found that the values of andare in the range of 228.14–262.23 and 14.89–16.23. Therefore,

. The value of is large compared with that of ,therefore, is dominant in the formulated objective function

. Thereby, energy efficiency of the CH selection decided bymight be reduced. In order to avoid this issue, the coefficientin (1) is set as small as , which gives an equal con-

tribution of and , i.e., . The HSA hasbeen applied for finding the optimal CHs for the WSNs. Theparameters of HSA such as the , and the areselected as 50, 0.95, and 0.7, respectively. Since evolutionaryalgorithms are heuristic in nature, we thus performed 50 trialsto obtain the best solution. Table III shows the worst and bestfitness values as well as the mean and standard deviation of thesolution found after 50 trials.The convergence of the best fitness value for the objective

function given by (1) over the number of generations using HSAis shown in Fig. 10. It can be observed from Fig. 10 that thefitness value converges after less than 30 iterations.The computational time of the algorithm as well the time

taken by different protocols for completing the setup phase areshown in the Table IV. The study of these periods of time is con-ducted with a 30-node network, the calculation is made for 50cycles of reorganizing the network and average values are pro-vided. As observed from these values, the computational time


Fig. 10. Convergence of the objective function when using HSA.

TABLE IVCOMPUTATIONAL TIME AND SETUP PHASE DURATION

of the LEACH-C, FCM, and HSA are insignificant when com-pared with the time duration of the setup phase. Therefore, itenables these algorithms to be applied for online configurationof the network. In addition, the setup phase duration of all threestudied protocols are almost the same. This means that the costof optimization process by using HSA is comparable with thecost of setting up the network done by the other two methods.Hence, the difference of the network performance with theseprotocols only represents the achievement of the optimizationprocess during the data transmission phase.

B. Experimental Results of the Network Performance

First, the operation of the real-time networking system usingHSACP is illustrated in Fig. 11(a) and (b). Fig. 11(a) and (b)shows the current consumption of the sensor nodes during thesetup phase and data transmission phase, respectively, in thenetwork of 30 nodes using HSA protocol. During the setupphase, all of the nodes have to turn on their radio componentto send the advertisement message and listen to the assignmentmessages transmitted from the BS. The time interval taken tocomplete the setup phase is in the range of 540 to 700 ms for allof the sensor nodes. A sample of the setup phase at four differentnodes is shown in Fig. 11(a), sensor nodes 1–4 spend around560, 549, 662, and 564 ms, respectively, to complete its setupphase with node 3 being the longest.During the data transmission phase, the cluster member tries

its best to send data before dropping the packet during the trans-mission time interval, given at the end of the setupphase. Meanwhile, the maximum time interval a CH turns onits radio is that includes time for receiving datapackets from all of the members, processing data and transmitthe compressed packet to the BS:

with the values given in Table II. Fig. 11(b)shows the data transmission phase of a cluster containing of 12

Fig. 11. Measurement of the sensor nodes current consumption in differentphases. (a) Current consumption of the sensor nodes during the setup phase. (b)Current consumption of the cluster members and CH during the data transmis-sion phase.

member nodes. In this figure, only three member nodes in thiscluster and its CH are shown. The cluster member nodes 1–3turn their radio on for around 11, 11, and 4 ms, respectively,meanwhile the time interval of turning on radio component ofthe CH is around 267 ms for data receiving, processing, andtransferring to the BS.In the next experiment, the lifetime of the network is studied

and compared with other conventional protocols such asLEACH-C and FCM cluster-based protocol (FCMCP).The LEACH-C is a typical centralized clustering protocol

proposed in [13] as an improvement of LEACH. Different fromLEACH, the process of CH selection and cluster formation iscarried out at the BS instead of performing at the sensor nodes.During the setup phase, every sensor node sends the advertise-ment message including its location and residual energy to theBS. The BS establishes a set of candidates consisting of thenodes with residual energy higher than the average. Simulatedannealing algorithm is adopted to find the optimal CHs in sucha way that the total distances from cluster members to their CHsis minimized [13]. The objective function is presented in

(5)

as given in [36], where is the selected CH, is the set ofselected CHs, is the number of nodes, and is distancefrom node to CH . After that, the assignment messages aresent to all of the sensor nodes. The data transmission phase isperformed in the similar way as HSACP.The FCMCP proposed by Hoang et al. organizes the network

into clusters by applying the FCM clustering algorithm [15].


Fig. 12. Network lifetime comparison amongst LEACH-C, FCMCP, andHSACP.

Later, it chooses the CH that has the highest residual energyor the highest battery voltage amongst the sensor nodes withineach cluster.The comparison of the lifetime amongst the networks using

LEACH-C, FCMCP, and HSACP is shown in Fig. 12. To eval-uate the performance of each protocol, the following perfor-mance indices are used: and , where is definedas the time until the first node in the network using protocolruns out of energy and is the time until the number of alivenodes is smaller than the number of clusters, which is three inthis case.It can be observed from Fig. 12 that the network with

LEACH-C has 12.72 h; meanwhile, in theFCMCP network, 16.76 h, and in the HSACPnetwork 18.56 h that is the longest period of time.Both LEACH-C and FCMCP assist the network to be orga-nized in a better way, and sensor nodes are evenly allocated intoclusters. Therefore, the traffic load at the CHs can be balancedamongst the clusters, and fast depletion of energy at the CHcan be avoided. However, in LEACH-C, residual energy isonly considered to establish the set of CH candidates but is notincluded to select the optimal CHs for high energy efficiency.Meanwhile, FCMCP chooses the CH only among the nodeswithin a cluster that may not be the best in the network in termof energy distribution. When HSACP is applied, the networkformation and CH selection are considered at the same time inorder to further optimize the network organization. The bestCHs in terms of energy efficiency can be selected, whereasclusters can be formed optimally. The efficiency of HSACPcan be seen clearer by comparing the time . Fig. 12 showsthat the time 35.17 h is much longer than thatof FCMCP, 21.54 h and that of LEACH-C,

20.68 h. At the beginning, all of the nodeshave the same energy level. However, during the networkoperation, at various time instances, each node has a differentresidual energy. LEACH-C and FCMCP algorithms focus onlyon obtaining uniform distribution of the sensor nodes intodifferent clusters; the selection of the CHs for energy efficientoperation is not optimized. It is therefore, the network lifetimemay not have been maximized. Meanwhile, HSACP involves

a better way of optimizing the network control, thus, betterperformance presented by longer network lifetime is achieved.

VII. CONCLUSION

In this paper, we present the details of practical implemen-tation of a centralized cluster-based protocol using HSA, ametaheuristic optimization algorithm. The proposed protocolhas been successfully developed and executed on a WSNtest-bed for real-time fire detection in an indoor building envi-ronment. The experimental results show that, by using HSACP,the network lifetime is extended significantly when comparedwith LEACH-C and FCMCP. It is clear from the result thatHSA can provide fast convergence with the best fitness valueand a computational time of less than 10 ms is comparablewith FCM. Thereby, it enables HSA to be applied for real-timeconfiguration of the network. Additionally, the proposed frame-work for designing clustering protocols can also be used as atool for real-time operation to investigate other optimizationalgorithms for WSNs. Future works such as investigation of anadaptive coefficient in the objective function and optimizationof the frequency of re-clustering the WSNs and transferringdata can be carried out to further improve the performance ofthe overall networks.

REFERENCES

[1] I. F. Akyildiz, W. L. Su, S. Yogesh, and C. Erdal, “A survey on sensornetworks,” IEEE Commun. Mag., vol. 40, no. 8, pp. 102–114, 2002.

[2] Y. K. Tan and S. K. Panda, “Energy harvesting from hybrid indoorambient light and thermal energy sources for enhanced performance ofwireless sensor nodes,” IEEE Trans. Ind. Electron., vol. 58, no. 9, pp.4424,4435–4424,4435, Sep. 2011.

[3] Y. K. Tan and S. K. Panda, “Self-autonomous wireless sensor nodeswith wind energy harvesting for remote sensing of wind-driven wildfirespread,” IEEE Trans. Instrum. Meas., vol. 60, no. 4, pp. 1367–1377,Apr. 2011.

[4] Y. K. Tan and S. K. Panda, “Optimized wind energy harvesting systemusing resistance emulator and active rectifier for wireless sensornodes,” IEEE Trans. Power Electron., vol. 26, no. 1, pp. 38–50, Jan.2011.

[5] B. Zhang, R. Simon, and H. Aydin, “Harvesting-aware energy manage-ment for time-critical wireless sensor networks with joint voltage andmodulation scaling,” IEEE Trans. Ind. Inf., vol. 9, no. 1, pp. 514–526,Feb. 2013.

[6] J. N. Al-Karaki and A. E. Kamal, “Routing techniques in wirelesssensor networks: A survey,” IEEE Wireless Commun. Mag., vol. 11,no. 6, pp. 6–28, Dec. 2004.

[7] M. Younis, M. Youssef, and K. Arisha, “Energy-aware routing incluster-based sensor networks,” in Proc. 10th IEEE/ACM Int. Symp.Modeling, Anal. Simulation Comput. Telecommun. Syst., Fort Worth,TX, USA, Oct. 2002, pp. 129–136.

[8] A. Manjeshwar and D. P. Agrawal, “Teen: a protocol for enhanced effi-ciency in wireless sensor networks,” inProc. 1st Int. Workshop ParallelDistrib. Comput. Issues Wireless Netw. Mobile Comput., San Fran-cisco, CA, USA, Apr. 2001, pp. 2009–2015.

[9] A. Manjeshwar and D. P. Agrawal, “Apteen: a hybrid protocol forefficient routing and comprehensive information retrieval in wirelesssensor networks,” inProc. 2nd Int. Workshop Parallel Distrib. Comput.Issues in Wireless Netw. Mobile Comput., Ft. Lauderdale, FL, USA,Apr. 2002, pp. 8–15.

[10] S. Lindsey and C. S. Raghavendra, “Pegasis: power efficient gatheringin sensor information systems,” in Proc. IEEE Aerospace Conf., BigSky, MT, USA, Mar. 2002, pp. 1125–1130.

[11] S. D. Muruganathan, C. F. Daniel, R. I. Bhasin, and A. O. Fapojuwo,“A centralized energy-efficient routing protocol for wireless sensor net-works,” IEEE Commun. Mag., vol. 43, no. 3, pp. 8–13, Mar. 2005.

[12] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-effi-cient communication protocols for wireless microsensor networks,” inProc. 33rd Annu. Hawaii Int. Conf. Syst. Sci., Jan. 2000, pp. 1–10.


[13] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An ap-plication-specific protocol architecture for wireless microsensor net-works,” IEEE Trans. Wireless Commun., vol. 1, pp. 660–670, 2002.

[14] J. S. Lee andW. L. Cheng, “Fuzzy-logic-based clustering approach forwireless sensor networks using energy predication,” IEEE Sensors J.,vol. 12, no. 9, pp. 2891–2897, Sep. 2012.

[15] D. C. Hoang, R. Kumar, and S. K. Panda, “Fuzzy C-means clusteringprotocol for wireless sensor networks,” in Proc. IEEE Int. Symp. Ind.Electron., 2010, pp. 3477–3482.

[16] N. M. A. Latiff, C. C. Tsimenidis, and B. S. Sharit, “Performance com-parison of optimization algorithms for clustering in wireless sensor net-works,” in Proc. IEEE Int. Conf. Mobile Adhoc Sensor Syst., Pisa, Italy,Oct. 2007, pp. 1–4.

[17] J. d. O. Valente and W. Pedrycz, Advances in Fuzzy Clustering and itsApplications. New York, NY, USA: Wiley, 2007.

[18] P. Yadav, R. Kumar, S. K. Panda, and C. S. Chang, “Improved har-mony search algorithm based optimal design of the brushless DCwheelmotor,” in Proc. IEEE Int. Conf. Sustainable Energy Technol., Kandy,Srilanka, 2010, pp. 1–5.

[19] P. Yadav, R. Kumar, S. K. Panda, and C. S. Chang, “An improved har-mony search algorithm for optimal scheduling of the diesel generatorsin oil rig platforms,” Energy Conv. Manag., vol. 52, no. 2, pp. 893–902,Feb. 2011.

[20] P. Yadav, R. Kumar, S. K. Panda, and C. S. Chang, “Energy-efficientthrust allocation for semi-submersible oil rig platforms using improvedharmony search algorithm,” IEEE Trans. Ind. Inf., vol. 8, no. 4, pp.913–924, Nov. 2012.

[21] S. S. Rao, Engineering Optimization: Theory and Practice, fourthed. Hoboken, NJ, USA: Wiley, 2009.

[22] E.-G. Talbi, Metaheuristics: From Design to Implementation.Hoboken, NJ, USA: Wiley, 2009.

[23] K. Tang and X. Yao, “Special issue on ‘Nature inspired problem-solving’,” Inf. Sci., vol. 178, no. 15, pp. 2983–2984, Aug. 2008.

[24] X.-S. Yang, Nature-InspiredMetaheuristic Algorithms. Frome, U.K.:Luniver, 2008.

[25] Z. W. Geem, J. H. Kim, and G. V. Loganathan, “A new heuristic op-timization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp.60–68, 2001.

[26] D. C. Hoang, P. Yadav, R. Kumar, and S. K. Panda, “A robust har-mony search algorithm based clustering protocol for wireless sensornetworks,” in Proc. IEEE Int. Conf. Commun. Workshops, 2010, pp.1–5.

[27] S. X. Ding, P. Zhang, S. Yin, and E. L. Ding, “An integrated designframework of fault-tolerant wireless networked control systems for in-dustrial automatic control applications,” IEEE Trans. Ind. Inf., vol. 9,no. 1, pp. 462–471, Feb. 2013.

[28] “Iris Wireless Measurement System, ,” MEMSIS Inc., San Jose, CA,USA, 6020–0124-02 rev a.

[29] P. Levis and D. Gray, TinyOS Programming. Cambridge, U.K.: Cam-bridge Univ., 2009.

[30] R. C. Luo and O. Chen, “Mobile sensor node deployment and asyn-chronous power management for wireless sensor networks,” IEEETrans. Ind. Electron., vol. 59, no. 5, pp. 2377–2385, May 2012.

[31] Z. W. Geem, Music-Inspired Harmony Search Algorithm. Berlin,Germany: Springer, 2008.

[32] K. S. Lee and Z. W. Geem, “A new meta-heuristic algorithm forcontinues engineering optimization: harmony search theory and prac-tice,” Comput. Methods Appll. Mech. Eng., vol. 194, no. 36–38, pp.3902–3933, Sep. 2005.

[33] M. Rengasamy, E. Dutkiewicz, andM. Hedley, “MAC design and anal-ysis for wireless sensor networks with co-operative localisation,” inProc. Int. Symp. Commun. Inf. Technol., Oct. 2007, pp. 942–947.

[34] S.-C. Kim and C.-W. Woo, O. Gervasi, D. Taniar, B. Murgante, A.Laganà, Y. Mun, and M. Gavrilova, Eds., “A scalability analysis ofTDMA-based ad hocMAC protocols,” inProc. Computational Scienceand Its Applicat., Berlin, Germany, 2009, pp. 580–592.

[35] “MTS/MDA Sensor Board User’s Manual, Revision A, Crossbow,”M.Gavrilova, Ed., 2007.

[36] W. B. Heinzelman, “Application-specifc protocol architectures forwireless networks,” Ph.D. dissertation, Dept Electr. Eng. Comput.Sci., Mass. Inst. Technol., Cambridge, MA, USA, 2000.

Duc Chinh Hoang (S’08) received the B.Tech. de-gree in electrical engineering from Hanoi Universityof Technology, Hanoi, Vietnam, in 2007. He is cur-rently working toward the Ph.D. degree at the Depart-ment of Electrical and Computer Engineering, Na-tional University of Singapore, Singapore.He is also a Research Engineer with the De-

partment of Civil and Environmental Engineering,National University of Singapore, Singapore. Hisresearch interests include wireless sensor networks,optimization, and smart building.

Parikshit Yadav (S’10) received the B.Tech. (Hons)degree in electrical engineering from MalaviyaNational Institute of Technology, Jaipur, India, in2007. He is currently working toward the Ph.D.degree at the Department of Electrical and ComputerEngineering, National University of Singapore,Singapore.His research interests lie in the fields of marine

power system, optimization, and offshore windpower generation.

Rajesh Kumar (M’08–SM’10) received the B.Tech.degree from the National Institute of Technology,Kurukshetra, India, in 1994, the M.E. degree fromMalaviya National Institute of Technology, Jaipur,India, in 1997, and the Ph.D. degree from theUniversity of Rajasthan, India, in 2005.Since 1995, he has been a Faculty Member with

the Department of Electrical Engineering, MalaviyaNational Institute of Technology, Jaipur, India, wherehe is an Associate Professor. He was a Post DoctorateResearch Fellow with the Department of Electrical

and Computer Engineering, National University of Singapore, Singapore, from2009 to 2011. His field of interest includes theory and practice of intelligent sys-tems, computational intelligence, and applications to power systems, electricalmachines and drives.

Sanjib Kumar Panda (S’86–M’91–SM’01) re-ceived the B.Eng. degree from Regional EngineeringCollege, Surat, India, in 1983, the M.Tech. degreefrom the Institute of Technology, Banaras HinduUniversity, Varanasi, India, in 1987, and the Ph.D.degree from the University of Cambridge, Cam-bridge, U.K., in 1991, all in electrical engineering.Since 1992, he has been a FacultyMember with the

Department of Electrical and Computer Engineering,National University of Singapore, Singapore, wherehe is currently an Associate Professor and Area Di-

rector of the Power and Energy Research Group. His research interests are incontrol of electric drives and power electronic converters, energy harvesting,renewable energy, assistive technology, and mechatronics.

Documents

Real-Time Implementation of a Harmony Search Algorithm-Based Clustering Protocol for Energy-Efficient Wireless Sensor Networks