Energy Efficient Coverage Optimization in Wireless Sensor Networks based on Genetic Algorithm

Transcription

1 Unversal Journal of Communcatons and Network 3(4): 82-88, 2015 DOI: /ujcn Energy Effcent Coverage Optmzaton n Wreless Sensor Networks based on Genetc Algorthm Al Norouz *, Ahmet Sertbas Department of Computer Engneerng, Istanbul Unversty, Avclar, Turkey Copyrght 2015 by authors, all rghts reserved. Authors agree that ths artcle remans permanently open access under the terms of the Creatve Commons Attrbuton Lcense 4.0 Internatonal Lcense Abstract There has been a great deal of attenton pad to Genetc Algorthm (GA). The algorthm, as a methodology, s a mult objectve methodology whch can be used n dfferent felds such as self-organzng wreless sensor network. The technque examnes the appled parameters and at the same tme takes nto consderaton the ftness functon by the way of or consderng the whole operatonal modes n produced feasble states. Majorty of the GA mplementatons n clusterng algorthm only deal wth optmzaton of few parameters ncludng coverage and energy consumpton wth notceable effect on network qualty. Keepng network coverage can be modeled as mathematcal programmng problem whch s featured wth heavy load of computaton. On the other hand, wreless sensor networks (WSNs) can be of dynamc nature, f so t needs to have proper reacton to events; so that slghtest management decson may lead to consderable problems on the qualty of the network. Ths problem s dealt wth n ths study through a hybrd method n MATLAB wth the help of Genetc Algorthm toolbox and custom codes. The optmum soluton was obtaned by mathematcal algorthm that conforms to all the mentoned parameters. narrowly pertnent to energy savng, connectvty, network reconfguraton, network lfetme, and so on. Nodes n the present study are deployed for random sensor feld. Thus, a key problem s coverage as t has effects on montorng and trackng object. In general, coverage s dscussable from two ponts of vews; the best and the worst observable cases. Under the former approach, the goal s to spot the area of hgher observaton change and to determne the best support and gudance regons. However, under the latter case, the purpose s to spot the areas wth lower observaton change and to detect the blnd spots. In several cases, a preferred dstrbuton of sensors n the target feld s hardly achevable as manual deployment s almost mpossble and t may be affected by uncontrollable factors such as wnd and obstacles [3, 4]. Fg.1 shows the general scheme of network coverage n WSN [5]. Keywords Genetc Algorthm, Wreless Sensor Network, Coverage Mantenance, Energy Consumpton, Network Optmzaton 1. Introducton Wreless Sensor Networks (WSN) consst of many low-power, low-cost sensors wth lmted computatonal and communcaton capablty. The sensors are easly deployable n WSN settng for specfc functons. The cost of wreless platforms s decreasng whle thers functonalty ncreases. Ths trend promses a wdespread use of the wreless system for dfferent purposes such as health montorng or mltary sensng. Lke other networks, WSNs have nspred enormous number of researchers all over the world [1, 2]. A crucal ssue or WSNs s the coverage, whch s Fgure 1. Network Coverage concept n WSN Network longevty s the most mportant challenge of WSN because nodes wth lmted power-energy batteres are n charge of aggregatng and transferrng montored data to the base staton. These tasks are energy consumng and replacng or rechargng the batteres s nfeasble. Few algorthms and protocols have tred to keep balance energy consumpton and at the same tme keepng a satsfed coverage as a way to solve the ssue of lfetme. Thus, t s essental to study the ssues of dstrbutng the sensors evenly, so that the sensors use equal amount of

2 Unversal Journal of Communcatons and Network 3(4): 82-88, energy wth acceptable energy coverage. At any rate, the problem of coverage cannot be avoded. As ponted out, WSN coverage s used to the coverage space of sensors wth consderable effect on the performance. Ths results n consderable decrease of the power consumpton. Fgure 2. coverage problem n WSN The area covered by the sensor may also have overlaps (Fg.2). By decreasng the overlaps, the network can cover larger space wth fewer sensors. The ssues of shared space covered by the sensors n a network are the coverage problem [6]. Another problem to be concerned wth s the area remaned uncovered between the covered areas (Fg.3). The spaces are techncally called holes and as the network cannot collect nformaton about the holes, mnmzng the hole s vtal. Fgure 3. Coverage hole n WSN Although, falure of a few nodes may not halt the overall network functons, whch can have a deep nfluence over the optmum network connectvty dependng the protocol used by the network [7]. Cluster based protocols are used more commonly as the optmum energy consumpton s acheved by the clustered nodes. For nodes under cluster archtecture, dfferent groups of nodes are formed based on the dutes assgned to the network and purposes of cluster. Many nodes members are assgned wth a head node known as cluster head. The cluster head transfers the collected data from the member of the cluster to the snk. The cluster heads can schedule ther group based on specfc Tme Dvson Multple Access (TDMA). In spte of the fact that there are several algorthms ntroduced [8, 9], concentraton on a specfc ssue leads to a complcated desgn. One of the most promsng heurstc methods to deal wth the problems of optmzaton s genetc algorthm (GA). The algorthm s based on nherent selecton whch also plays a role n bologcal evoluton. The GA modfes a group of ndvduals frequently and randomly to act as parents and use them to produce the next generaton. Afterward, the populaton evolves to acheve an optmal soluton by the way of successve generaton. To put t another word, the present study manly tres to answer the problem of coverage n WSNs, whle mnmzng the overlappng nfluence. Smulatons were done n MATLAB. In what follows, secton II revews the lterature of WMSN and on network coverage. Secton III deals wth the basc concepts ncludng termnology and the hypotheses needed n the proposed method. Also soluton of the problem of coverage by the proposed algorthm s dscussed n secton III followed by secton IV on evaluaton of performance and results of smulaton. Secton V s the concluson. 2. Related Works In majorty of recent studes, sensor nodes are statc and plenty of extra nodes are nstalled to reach the preferred level of coverage. Ths may cause heavy costs, whle there s no guaranteed coverage by random dstrbuton. There have been several algorthms proposed to desgn an optmum WSN, each of whch has concentrated on a specfc ssue and led to a complex desgn. Ths secton explans some of prevous studes on the problems of network coverage optmzaton. One of the poneerng works n the feld was conducted by Ref. [10]. He proposed a blanket coverage method that developed a statstcal arrangement of sensor nodes, whch eventuated n an extra coverage. Reference [8] Proposed a GA-based method whch used a repulsve behavor to scatter the nodes over the desred area. The authors worked on a complete model network n whch only offlne plannng was requred for further development. Reference [11] proposed an nteractve method, based on GA, to reach an optmal soluton for energy consumpton, schedule of transmsson, and so on. Another method was ntroduced n Ref. [12] to determne and add to multmeda coverage of each sensor. The method s desgned to automatcally poston the sensor. As the method requres, the coverage rato of a specfc number of nodes can be obtaned and effectveness of the method s reduced by decreasng of the number of nodes n the method. A novel classfcaton for WSNs based on 3D geometrcal fgures was ntroduced by Ref.[2]. To obtan the covered volume by the sensors, they proposed an equaton under the ttle of volume n the sphercal coordnaton. Apparently, the classfcaton s practcal n places where the space s not n the surface [2].

3 84 Energy Effcent Coverage Optmzaton n Wreless Sensor Networks based on Genetc Algorthm Reference [13] smplfed the complex coverage problem though a step by step process. Wth the use of mathematcal modelng, theoretcal analyss and formula deductng, classcal geometrc theores and va mathematcal nducton, the analyss formula of mnmum number of nodes was deduced theoretcally under crcumstances of entre and seamless coverage n WSN [13]. The problem of moblty of sensor networks was dealt by Ref. [3] n whch some part of the sensors could change ther locatons. When a sensor spots coverage holes, t changes ts poston followng specfc polces. To have less cost of sensor moblty, they offered a densty moblty scheme (DMS) that may mprove coverage of the system through dsplacng the nodes to the nearest hole found. Under the DMS, a moble sensor can change ts poston to a sparse area only when ts neghbor nodes cover majorty of the sensng area [3]. Another novel modelng of the problem of coverage n wreless camera-bases sensor networks was ntroduced by genetc algorthm to reduce overlappng effect as a soluton to coverage problem [14]. Theoretcal analyss of the problem has been employed to ncrease coverage of an area up to a maxmum level wth reduced overlappng by the cameras. Reference[15]ntroduced a GA based nteractve approached for Moble Sensor Networks. In ths work, the ftness functon was used to obtan the sutable drecton of node locomoton, keepng n mnd ether coverage of the target area or estmaton of the optmum energy consumpton. 3. Proposed Algorthm The present study uses genetc algorthm to acheve better soluton than that the other methods do. The GA ncludes the general steps as followng. 1. To generate randomly an ntal populaton M(0). 2. To compute and keep the ftness u(m) for each ndvdual m n the present populaton M(t). 3. To defne selecton probabltes p(m) for every member m n M(t) so that p(m) s relatve to u(m). 4. To generate M(t + 1) by choosng the ndvduals probablstcally out of M(t) to form new generaton by employng crossover and mutaton. 5. Repeat step 2 untl satsfyng soluton s acheved. GA s usually used to solve the problems featured wth wde search space n whch accuracy does not matter. As our method calls, the maxmum area of montorng s fully covered by a mnmum actve sensor. We assume a WSN wth many number of small battery-drven sensor nodes nstalled n the target feld. The sensor nodes sense the envronment for nformaton such as temperature on perodcal bases. Ths secton ntroduces the network defnton and assumpton. Defnton 1: The sensng range of a sensor s a geographcal area n whch any event may occur. Defnton 2: the sensng neghbor: a set of sensors postoned n sensng range. Defnton 3: A WSN s homogenous when all the sensors have equal sensng range, communcaton range, and ntal energy. Otherwse, t s heterogeneous [4]. Defnton 4: Ftness functon s to evaluate the goodness of each soluton;.e. chromosome. Takng nto account actual WSN applcaton, the present study consders hypothetcal parameters A, B, and C for 2D felds; the parameters help us to follow more practcal approach. They are commonly used by researches n the feld [16]. Thus, we have three knds of sensors to observe specfc objects. To smplfy the problem, t s assumed that the spatal varablty of A, B, and C are pctured as the densty of sensors n area unt n whch the objects are montored n the form of ρ << ρ << ρ.ths concept both deals wth A B C general and specfc aspects of specal objectve networks. The smulaton parameters are shown n Table 1. Table 1. Smulaton parameters Area (N) 100*100 Number of Sensors(N) 200 Intal Energy Node energy Base Staton dstance Packet Sze Sensng range 2J 50 nj/bt 200 m 200 bts 15 m Assume squared Eucldean fled wth length l subdvded nto equal area so that any nterested subarea s montored by sensors located at the vcnal ntersecton lnes. Ths trck s adopted by many researchers as grd based wreless sensor network layout. The sensors are small, lmted-powered, wth certan-transmsson range, and sensng-mode selecton node, whch can choose one of the three operatng modes on the bass of ts capablty and condton status. Snce densty of parameter A s the lowest, t has hghest transmsson range and accordngly C has lowest range. In ths work, clusterng soluton was adopted to acheve optmum energy consumpton as the cluster conssts of one certan adjonng sensor wth same operatng mode - cluster-n-charge. Every cluster communcates wth the Base Staton (BS) or the snk through mult-hop. A mult-objectve algorthm deals wth optmzaton of two mportant parameters of energy consumpton and coverage problem. Thus, the problem was dvded nto two sub-problems. The strategy s conssted of: 1. Fndng the soluton for coverage and power consumpton ssues by fndng mnmum number of nodes whch are needed to provde coverage for the whole envronment wth the use of local search and genetc algorthm. 2. To make sure that clusters-n-charge and member

4 Unversal Journal of Communcatons and Network 3(4): 82-88, nodes of a cluster are connected. To ths end Kruskal algorthm was used. A novel algorthm was adopted to propose some of possble optmum network topologes that also mnmze constrants such as operatonal energy, number of unconnected nodes, and overlap of cluster-n-charge errors. Part of genetc algorthm formula whch s actually an mproved verson of Nakmura formula was used to propose an approprate ftness functon [17]: A s the gven montorng area, S denotes set of sensor nodes, D stands for set of demanded ponts, A d s set of sensors montorng demanded areas, NC s penalty cost of no coverage of demand pont, AE s turnng energy on, PC denotes penalty cost of path from every node to BS (obtaned by Djekstra s algorthm durng a pre-processng phase) and t s assgned to every node to dstngush expensve nodes and the model varables; X j s 1 (when node covers demand pont j) and 0 otherwse, y s 1 (when nodes s actve and 0 otherwse), h j s 1 (when the demand pont j s not covered).the varables of the model are: X j = 1 when node covers demand pont j and 0 otherwse y = 1 when nodes s actve and 0 otherwse h j = 1 when demand pont j s not covered. The model can be formulated as: s ( AE + PC ) mn y + NC j hj (1) Subject to: ( x j + hj ) j x j y j D d 1, j D & j A (2) S j A d, & (3) d 0 x 1, j A, h 0, j D j { 0,1 }, { x h} R y, The above formula mnmzes the number of requred actve node, whch leads to ncrease n network energy as well as the number of uncovered nterested area. Constrants 2 and 3 ndcate each demanded pont montored by a sensor or remaned uncovered and mposes that only actve node can sense respectvely. We mprove our Ftness Functon (FF) of algorthm through consderng penalty cost of overlappng cluster-n-charge errors and energy consumpton represented by OPCE and EC respectvely as follows [18].: FF = mn( Usage _ Cost + Penalty _ Cost) Usage _ Cost = s Penalty _ Cost = j ( AE + PC + EC) ( NC jk + OPCE jk ) j M k D y h k (4) Subject to: M { A, B, C} EC s numercally measured and t depends upon the mode of sensor network. Obvously, snce the sensor node n A mode has hgh communcaton range t consumes man porton of the energy followed by mode B and C n order of regardng energy consumpton. In what follows, t s assumed that A mode consumes four tmes the power that s used by C and B mode consumes two tmes more than C mode. EC s gven by: 4n EC = A + 2nB n S + n OPCE n the ftness functon computes wasted energy for montorng errors of overlappng cluster-n-charge. 4. Evaluaton and Results Plenty of optmum solutons are obtaned by genetc based algorthm, though connectvty of the nodes s not taken nto account. Ths presents outflow of collected data toward the BS. Kruskal algorthm was utlzed to examne connectvty of network n the 2nd part of ECEP. The process proposed s comprsed of four steps: The network s assumed as graph G where an edge exsts between vertces x and y n graph G when the maxmum communcaton range between two partcular nodes x and y exceeds the dstance between x and y. By ntroducng the Kruskal algorthm, a mnmum spannng tree (MST) s acheved so that a shorter path between each two vertces for routng aggregated data s acheved. The connectvty wth specfc shortest paths s acheved when the number of MST tree edges s the same as the number of vertces -1; otherwse, nactve nodes are actvated (ths explans shorter transmsson range than communcaton dstance for some nodes). Kruskal technque s used on newly actvated and dsconnected nodes. Ths results n formaton of new lghtest tree. The shortest path between each dsconnected node to the BS s obtaned and the nternal sensor nodes of the paths are added to the set E. Any newly actvated node not lsted n E s turned deactvated. Ths helps preservaton of network energy whle the qualty s the same. Fnally, one or two network typology(s) were developed based on the range of transmsson of nodes and poston of sensor node. The network(s) that realzed maxmum network coverage are characterzed wth optmum coverage and energy usage. To mplement the new approach, a square L*L feld was used n whch the subdvded areas are about equal. Each node s postoned at the ntersecton of subareas and they can take values 1: nactve=00; 2: mode A actve =01; 3: C (5)

5 86 Energy Effcent Coverage Optmzaton n Wreless Sensor Networks based on Genetc Algorthm mode B actve=10; and 4: mode C actve=11. The network encodng technque s pctured n Fg. 4. fnal results,.e., any canddate ndvduals wll tend toward the optmum soluton. The number of teratons s constant at 100. Table 2. GA parameter Values Number of canddate ndvduals 400 Length of Chromosome 20 Crossover Rate.5 Mutaton Rate.2 Iteraton 100 Fgure 4. Network wth represented encodng The settng of the whole nodes n network can be represented by gen and the ordered set of gen makes a specfc chromosome. In ths way, for a network wth L nodes, encodng s 2.L2 bts, (L.2 gens that need 2 bts to explan the mode of correspondng node). Genetc algorthm technque encompasses a set of chromosomes whch are known as the populaton that are mproved by generaton process [18]. To put t another way, the algorthm copes the nature as t has nput data, and the prmary populaton that are randomly obtaned. Followng generaton process, the developed populaton or the result represents the optmum soluton regardng man problem. All of the mprovement carred out by generaton process cover crossover, scorng, selecton, and mutaton functons. By crossover we refer to a producton functon wth specfc rate whch mates two dfference chromosomes to develop a new populaton. Among the many crossover methods, sngle pont was adopted n ths work. Scorng or assgnng by ftness functon s the key of genetc algorthm that s nfluenced by the problem [19].. At ths part, the algorthm assgns a weght to any chromosome accordng to ts content. That s, every chromosome stands for a soluton that s developed through teratons. Hgher ftness value s obtaned by the better chromosome, whch survves to the next generaton. The ftness functon s manly desgned based on the problem and the researchers manly concentrate on representng ntellgent ftness functon to dfferentate qualfed ndvduals. Under the selecton process, the better chromosome to create the new populaton wth mutaton technque s adopted so that some specfc chromosomes are entered to the new generaton. In ths paper, populaton sze, productve functon, mutaton rate were adopted 400, sngle pont, and 0.5 respectvely. Table 2 shows the GA parameters used to smulate the envronment. The canddate chromosomes can be chosen randomly because ths selecton does not affect the Because of stochastc bases of GA, repettve runs of the algorthm eventuated n dfferent solutons wth dfferent performances. Thus, average of the results of runs was reported. By usng WSN smulator, appx. of the mplemented proposed algorthm acheved 100% coverage on the desgnated area. Power consumpton, number of actve nodes, and lve packet over tme are lsted n Table 1. The startng number of 18 packets delvered to the BS was ncreased to 83 packets after second Table 2. Observed values n the early tmes of network Tme (Nanosecond) Power Actve Sensors / 35 Lve Packets 00: : : : : : : : : : : Table 3 ndcates the last tme the network whch ded n 5:13:781. In addton, the number of lve packets gradually decreases as the number of actve sensors approaches 0. Table 3. Observed values n the last tmes of network Tme (Nanosecond) Power Actve Sensors/35 Lve Packets 03: : : : : : Fg.5 represents effect of number of teratons of genetc algorthm on the network lfetme. Iteraton 50 and 57 ndcate that ncreasng ndvduals does not always lead to more optmal soluton. In other words, satsfyng stop

6 Unversal Journal of Communcatons and Network 3(4): 82-88, crteron s suffcent to obtan optmal soluton. [3] [4] E. Zhao, Y. Lv, "A densty moblty scheme for mprovng coverage n wreless sensor networks," n Proc. Web Informaton Systems and Mnng,, Chna, 2009, pp , do: /WISM , [Onlne]. Avalable: &snumber= A. Norouz, A. Sertbas, Effcency analyss and comparatve performance evaluaton of routng protocols n moble Ad Hoc networks, Studes n Informatcs and Control, vol. 21 (2), pp , 2012, ISSN , [Onlne]. Avalable: 5. Conclusons Fgure 5. Network lfetme n specfed scale GA s a one of the commonly adopted soluton when the search must be done on a wde area and accuracy of the results s not much of a concern. Based on the proposed method, the wdest feasble area was covered usng a mnmum number of actve sensors. A hybrd method was proposed n ths study to deal wth the man problems of WSN (e.g. coverage and energy consumpton) to ths end, two genetc algorthms and Kurskal technques were used. Wthn the GA based part both the coverage and optmzaton of energy consumpton were under consderaton. The man achevement of the present study was demonstraton of the capacty of general algorthm to fnd the soluton of coverage problem n WSNs. Three types of sensors wth hgh, medum, and low ranges of transmsson were adopted for smulaton; the sensors were used to montor a grd based envronment. Results of smulaton showed merts of usng large number of low power sensors for communcaton over usng less number of sensors wth hgh energy consumpton. REFERENCES [1] [2] H. Kaschel, J. Mardones, Gustavo Quezada, Safety n wreless sensor networks: Types of attacks and solutons, Studes n Informatcs and Control, vol. 22 (3), pp , ISSN , 2013, [Onlne]. Avalable: N. Attarzadeh, A. Barat, A. Movaghar, "A new method for coverage n wreless sensor networks," n Proc. Dependable, Autonomc and Secure Computng, DASC '09. Eghth IEEE Internatonal, Chna, 2009, pp , do: /dasc , [Onlne]. Avalable: &snumber= [5] [6] [7] [8] [9] F.P. Quntão, F.G. Nakamura, G.R, Mateus, A hybrd approach to solve the coverage and connectvty problem n wreless sensor networks, In Proc. 4th European Workshop on Meta-heurstcs: Desgn and Evaluaton of Advanced Hybrd Meta-heurstcs, Unted Kngdom, 2004, [Onlne]. Avalable: 3/FredPaper.pdf M. K. watfa, and S. Commur, "A coverage algorthm n 3D wreless sensor networks", n Proc. 1st Internatonal Symposum on Wreless Pervasve Computng, Thaland, 2006, pp , do: /ISWPC , [Onlne]. Avalable: &snumber=33870 C. H. Kaschel, B. L. Sanchez, F. J. G. Mardones, C. G. Quezada C., Desgn and constructon of lnk qualty and localzaton protocol algorthms at WSN over IEEE physcal protocol, Studes n Informatcs and Control, vol. 20 (3), pp , 2011, ISSN ,, 2011, [Onlne]. Avalable: A. Howard, M. Mataríc, G. Sukhatme, Moble sensor network deployment usng potental felds: a dstrbuted, scalable soluton to the area coverage problem, n Proc. Internatonal Symposum on Dstrbuted Autonomous Robotcs Systems, Japan, 2002, pp [Onlne]. Avalable: _30 A. Mollanejad, L. M. Khanl, M. Zeynal, DBSR: Dynamc base staton Repostonng usng Genetc algorthm n wreless sensor network, IJCSI Internatonal Journal of Computer Scence,vol. 7, Issue 2, No 2,pp , March [Onlne]. Avalable: [10] D Gage, Command control for many-robot systems, Unmanned Systems Magazne, vol. 10, no. 4, pp , [11] S. Hussan, A. W. Matn, and O. Islam, Genetc algorthm for energy effcent clusters n wreless sensor networks, n Proc. 4 th Internatonal Conference on Informaton Technology: New Generatons, USA, 2007, pp , do: /ITNG , [Onlne].Avalable: &snumber= [12] N.Tezcan, and W. Wang, Self-orentng wrelsess multmda sensor networks for occluson-free vewponts, Computer Networks, Volume 52, Issue 13, pp , ISSN , [Onlne]. Avalable:

7 88 Energy Effcent Coverage Optmzaton n Wreless Sensor Networks based on Genetc Algorthm [13] Xueqng Wang, Fay Sun, Xangsong Kong, Research on optmal coverage problem of wreless sensor networks, n Proc. Internatonal Conference on Communcatons and Moble Computng, Yunnan, 2009, pp , DOI /CMC , [Onlne]. Avalable: &snumber= [14] Habbzad Navn, B. Asad, S. Hassan pour, M. Mrna, Solvng coverage problem n wreless camera-based sensor networks by usng genetc algorthm, n Proc. IEEE Internatonal Conference on Computatonal Intellgence and Communcaton Systems, Inda, 2010, pp , DOI /CICN Avalable: &snumber= [15] A. Norouz, F. S. Babamr, A. H. Zam, An nteractve genetc algorthm for moble sensor networks, Studes n Informatcs and Control, vol. 22 (2), pp , 2013, ISSN , [Onlne]. Avalable: [16] A. P. Bhondekar, R. Vg, M. L. Sngla, C Ghanshyam, P. Kapur, Genetc algorthm based node placement methodology for wreless sensor network, n Proc. Internatonal Mult Conference of Engneers and Computer Scentsts, Hong Kong, 2009, vol. I, ISBN: [Onlne]. Avalable: pp pdf [17] F.G.Nakamura, Planejamento dn.amco para controle de cobertura e conectvdade em redes de sensores sem fo planas. Master's thess, Unversdade Federal de Mnas Geras (n Portuguese), PP 45-78, [18] Al Norouz and Abdul Halm Zam, Genetc Algorthm Applcaton n Optmzaton of wreless sensor networks (Accepted for publcaton 19 November 2013), The Scentfc World Journal, Hndaw publshng cooperaton, to be publshed. Avalable : [19] A. Norouz, F. Babamr and A. Zam, "A New Clusterng Protocol for Wreless Sensor Networks Usng Genetc Algorthm Approach," Wreless Sensor Network, Vol. 3 No. 11, 2011, pp do: /wsn [20] Akyldz, I. F. and Vuran, M. C. (2010) Front Matter, n Wreless Sensor Networks, John Wley & Sons, Ltd, Chchester, UK. do: /