Optimization Coverage of Wireless Sensor Networks Based on Energy Saving. Technology, Luoyang , China. Xi an , China

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1 , pp Optimization Coverage of Wirele Senor Network Baed on Energy Saving Zeyu Sun 1, Heng Li 1, Heng Chen and Wei Wei 3 1 Computer and Information Engineering, Luoyang Intitute of Science and Technology, Luoyang 47103, China Electrical and information Engineering, Xi an Jiao Tong Univerity Xi an , China 3 School of Computer Science and Engineering, Xi'an Univerity of Technology Xi'an , China lylgzy@163.com Abtract The eparate ituation of reearch ha area coverage or target coverage in wirele enor network. Baed on directional ening model the algorithm ued the virtual potential field to make the enor node hift poition and change direction automatically in monitoring area, With the completion of multiple prior coverage of hot target which needed higher quality requirement the algorithm could maximize the coverage rate throughout the monitoring area Simulation experiment how that the algorithm ha a good ability of elforganizing. It can atify the requirement of coverage quality of the hot target and the whole monitoring area and ave reource effectively of the network. Keyword: wirele enor network, optimization coverage, probability model, enor node 1. Introduction WSN (Wirele Senor Network) i a kind of elf-organization network ytem which conit of large number of inexpenive enor node, and it node are characterized by a certain ening ability, computing power and communication capabilitie. It i widely ued in the field of defence and military, environmental monitoring, recue work and etc., WSN work in uch a way that following way: large number of enor node are ditributed in dicrete form within the coverage area, and data i ent to or collected from node directly or indirectly [1-3]. Uually the target node i covered in a manner that enor node are high denity deployed to monitor the target area, and to improve the quo of network, information i exchanged among enor node to achieve target node coverage and information proceing. But there re ome defect, firt, deployment of larger number of enor node in target area reult in exitence of coniderable amount of redundant node, which conume much network energy and reduce the network Qo. Second, due to the exceive conumption of node energy, and non-rechargeable feature of node, the network tend to collape quickly. How to ditribute enor node in target area reaonably to determine the minimum point et under certain coverage requirement, and how to limit the power conumption maximally, become key problem which influence the network lifetime directly. In ummary, the olving of energy iue and coverage problem mean monitoring the given area at the minimum ISSN: IJFGCN Copyright c 014 SERSC

2 node number and low energy cot, meanwhile, the quality of coverage hould be guaranteed. It i alo the tudy focu of thi paper.. Background The method of deployment of the wirele enor network node can be divided into determinitic deployment and randomne deployment. Uually the determinitic deployment method i adopted when the network i mall, and a good monitoring regional condition can be guaranteed. The advantage of thi method i that by controlling the poition of each node through artificially deployment, the optimal olution meeting the network coverage requirement can be achieved. On the contrary, if artificial way i not feaible, uually aircraft or other tool i ued to randomly ditribute enor node in a certain area, becaue of the uncertainty of node poition, more node will be needed compared to the determinitic method, then the node redundancy problem come out. Thu, the problem of energy conumption and node coverage become one of the major reearch topic in the filed of wirele enor network. In reference [4.5], by exploiting the Force Filed Theory of mobile network and Round Coverage Thinking in wire enor network, VFA algorithm i propoed. When the node in a wirele enor network are ditributed unevenly, thi algorithm can be ued to catter the intenive node in order to effectively cover the target area, but the energy conumption problem of whole network i not fully conidered. In reference [6, 9, 18], a enor network coverage and connectivity probability model in the cae that node are random cattered i propoed. By exploiting thi model, the node number which meet different coverage and connectivity requirement can be calculated and the calculation i imple; but thi model i only tudied with the complete coverage cae, meanwhile, the connectivity rate under multi-network coverage i not conidered. In reference [10, 13, 15] propoed perception coverage and connectivity retore tudy in mobile enor network, the idea i tudy the coverage area and connectivity iue a a whole, Coverage Conciou connectivity Retoration i ued to retore one or more node from the failure node, thu the connectivity i retored and thi node at initial poition in coverage area are monitored. Becaue the energy conumed for data collection every time i not equal, and it i not uitable to re-divide the interecting coverage et during the recovering proce of the failure node [8, 11, 1]. To thi end, by uing the Gauian denity function and the coverage area probability function the quantitative comparion between the node ening radiu and the number of node i given in the minimum node et theory model, o that the coverage for the target area i done. 3. Modeling and Analyi 3.1. Baic Definition The reearch work of thi paper i baed on the following aumption: Hypothei 1: wirele enor network communication radiu and the ening radiu are dic-haped. Hypothei : Coverage radiue of each node are equal, and the movement of the node are ynchronized parallel. Hypothei 3: through ome poitioning algorithm the pecific location information and coverage area boundary information can be obtained. Definition 1: if the coordinate of Node S i (x, y ), the target node up coordinate i (x p, y p ), then the Euler ditance between the node S and detination node p D(,p) i : 36 Copyright c 014 SERSC

3 (1) p p D (, p ) ( x x ) ( y y ) Definition :Let S be a collection of n node randomly ditributed in the target area, that i S = { 1,, 3... n }, E, i the et of edge of the network graph, which repreent the et of edge whoe e ij =1, where e ij denote the poitional relationhip of node i and target node p j, and e ij = 1 if and only if the Euclidean ditance of the two node i le than or equal to the ening radiu r, otherwie e ij = 0. W = {w 1, w, w 3... w N } i a et of initial energy of the enor node, W follow W~N(u,σ ) normal ditribution, w i repreent the initial energy of the enor node i, w i i the maximum energy amount during the working proce of node i. Definition 3: if communication radiu r c of enor node and ening radiu r follow r c r 3, completely coverage and connectivity can be enured. Definition 4: the coverage rate of a wirele enor network W deployed in the target region Ω i repreented a follow: r, C W f x d x f 0 f(x) i the target function of optimal node ubet; f(ω) i the target area. f(x) i defined a follow: 1 1 () f x w f x w 1 f x (3) f 1 (x) and f (x) denote the proportional function of the node et and the number of optimal ub, w 1 and w, repectively, expreed a the weight value. Defining 5: Gauian normal denity function: 1 r f ( x) ex p Then the number of node randomly ditributed i: r n lg 1 p lg (1 ) n Theorem 1: The probability that there i at leat one node: P 1 1 P Proof 1: Coverage probability event P (A) = p; then P (A+A)=P(A)+P(A)-P(A)P(A)=p+pp =1-(1-p) we can prove: P(A+A+A)=P(A+A)+P(A)-P(A+A)P(A)=1-(1-p) 3,P(n*A)=1-(1- p) n, o Theorem 1 i etablihed by mathematical induction provable. Theorem : provided that n enor node are randomly deployed in a network region whoe area i Ω, and ening radiu of each node i r, then the probability that there i a k N r k k node in a region of area r i N r e K!. Proof : By Poion' theorem, when the number of number of enor node N cloe to poitive infinity, the probability P approache infinity mall, the econdary ditribution B n, p can be approximately regarded a following the Poion ditribution p n,, Let np, and all the enor node in the network coverage area r are randomly ditributed, then the number of node in the network coverage area can be een a following obedience econdary ditribution B n, r, becaue when the enor node are ditributed in the coverage area, uually high-denity deployment method i adopted, o that the ening radiu of each node i much le than the network area Ω, when the number of enor node n within (4) (5) Copyright c 014 SERSC 37

4 the coverage area i gradually increaing, n r gradually approache infinitely mall, the binomial ditribution can be approximated een a the Poion ditribution, that i, the k probability that at any point of the network the coverage rate i K, i P e k!, where K N. 3.. Probability Model Generally peaking, the coverage rate directly reflect the degree of concern of target [7]; node with high concern degree are uually accompanied by high coverage rate. In order to tudy uch a problem, now network model of the relation between the enor node and the detination node i given. Thi i hown in Figure 1: Figure 1. Network Model Figure 1 how the enor layer, in which the network i a combination of five enor node and nine target node, and it decribe the relationhip between the enor node and the detination node, interect of the coverage mean it i covered by multiple node, where k = 1 denote ingle node coverage, k= denote covered by two node, and o on. For the enor layer, each node cover certain number of target node, thu forming the relationhip with the detination node[14,17], let the degree of aociation between the node i, it manifetation i: A(1,3,8,9); B (1,,3); C (3,4,5,8); D (5,6);E (6,7,8), and on the contrary, 1 (A, B); (B); 3 (A, B, C); 4 (C); 5 (C, D); 6 (D, E) ; 7 (E); 8 (A, C, E); 9 (A); (1,3) (A, B); (3,8) (A, C). Theorem 3: In the cae of multiple coverage, let k 3, provided that within the network monitoring area, any arch region with an edge of r i adjacent to an arch region with an edge of r i, and within the double curved area they formed, there exit k active enor node; then thi area i covered by k degree. Here r i i the length of an edge of the equilateral triangle which i formed by the node center. Proof 3: aume that the network monitoring area i an arch meh region, which i formed by the arch area divided from the monitoring area itelf, and for any arch region, there exit another adjacent arch region, within the double curved area formed by the two above arch area, there re k active node, and any of the arch area ha a ide length of r i, the maximum ditance between any node and the triangle within which the two curved area exit doe not exceed r i. Then any point in the two adjacent arch area i at leat covered by k node at the ame time. Similarly, if through judgment other arch area are alo covered by k degree. Then thi area i k-degree covered. 38 Copyright c 014 SERSC

5 3.3. Parameter Etablihment l: ide length of the quare coverage region Ω: the network region, ie Ω= l n: number of randomly deployed network node r : the ening radiu of enor node, r <l r t : the communication radiu of enor node, r t <l E(C): The expectation of enor node coverage P(S n ): a network expect coverage rate of n randomly deployed node P(C n ): network connectivity probability of n randomly deployed node Uually, coverage directly reflect the extent that the objective are concerned, the concerned target node region having higher coverage, taking into account the functional relationhip between enor node p in the region II expectation and coverage region, hown in Figure. Figure. Target Region and Node p in II Region Figure how the relation model of working enor node, dormant node and target node and the diagram of enor node p in coverage region II. Senor node 1-9 are in working tate, and the ret are in a dormant tate. Relation among the information obtained by perception of the target node, the node coverage region and the target node i a follow: Table 1. Relation of the Senor Node and Target Node target node target node target node 1 1, 9 7,8,6, , , ,8, , 8, ,7 1,,6 8, ,7,8,3 3 9,10 8 Now take the analyi of Figure a the example. The quare region l i divided intoⅠand II two part. Randomly deploy the node in the monitoring region to contruct a finite et S Copyright c 014 SERSC 39

6 with the coverage region of each node being E(C), o that the coverage probability of each node i E(C)/Ω. When the node et i empty, the network coverage of the n deployed node will be P(S)=(1-E(C)/Ω ) n Thu the network node probability value ha been obtained in the ituation that collective S i not an empty et. n p 1 1 E C / (6) When the number of node n, lim E p S 1 which indicate that the number of node x i large enough, thi coverage region i completely covered [16]. Conidering the boundary effect in olving the node coverage region and expectation, becaue the quare region i divided into region I and II, baed on the concept of expectation value in probability, expectation value of the coverage of network node can be obtained: ( ) ( ) ( ) ( ) E C P E C P E C (7) Ⅰ Ⅰ Ⅱ Ⅱ Among them, P ( Ⅰ ) and P ( Ⅱ ), repectively repreent the probability value of the randomly deployed node in region Ⅰand II. E ( C Ⅰ ) And E ( C Ⅱ ) denote the correponding expectation of coverage. From the even ditribution function of the random deployment of enor node, then get: P ( ) 1 r l Ⅰ P ( ) r 1 r l Ⅱ Aume that node p i in the region Ⅰ, it coverage being completely contained, o the coverage expectation i: (8) r (9) Ⅰ E ( C ) When a node p i in region II, the region hould equal to it ening region of the circumference ubtracting bow region S ABCD. A, B i the interection of the ening circle of node p and network boundarie, whoe angle i the central angle, formed by the node p and A, B. i.e. A p B a rcco y r o:, 4. Deign Algorithm E ( C ) in Ⅱ 1 1 r d x d y l r r l r r 0 0 r in l r l r r d x Idea Algorithm Through Geometry theory, the multiple coverage area formed by the enor node are deployed in the range of the critical target node, and let the critical node reide in the coverage area of multiple enor node, when redundant data appear in one node, node tatu cheduling mechanim i adopted to allow ome of the node witch into leep tate to reduce the conumption of network energy. By uing Gauian normality denity function and of the probability function for coverage area, each ub-region i iterative optimized and the optimal ubet i obtained, thu making the entire network node be optimized, and the d y (10) 40 Copyright c 014 SERSC

7 number of minimum coverage node can be determined. The algorithm ha a le computational overhead, low complexity, and effectively ave the energy conumption of each node, which in turn prolong the network life cycle, and improve the quality of the network performance. 4.. Dynamic Form When the target into a cluter head monitoring area, to the neighbor cluter head node end a packet containing the target information, all the monitoring to the target cluter are dynamically in the target around to form a group, cluter member node only with the cluter node communication, the cluter head and between cluter head can be mutually communication. Involved in tracking ha the cluter number depending on the ize of the radiu of the grid [14, 19, 0]. For example, if the acce grid ide length equal to the radiu of communication node, then a maximum of only four cluter capable of imultaneouly monitoring to the target. When at the ame time two or more than two cluter head and monitoring to the target, we elect thee cluter in a cluter head node a a leader node, cluter head firt to the neighbor hair to end their and monitoring the ditance between the target data information, if the cluter head received a ditance cloer to the target hair to information, give up campaign to become leader node. Selection criteria for: firt, chooe from the cloet cluter head node; econd, if there i two or more than two cluter head node and the target and the ditance between the ame, reidual energy larger the lead node. All the monitoring to the target cluter head node will be ent to a leader node data firt, and then by the leading node calculation and data fuion are tranmitted to a data centre node. A hown in Figure 3: Figure 3. The Target Node Coverage Area Diagram When the mobile target leading away from the node, becaue of the need to tranmit data over long ditance to the leader node, or a new cluter head node monitoring to the target, then a leader node i no longer applicable act a a leader node, fat the election of a new leader node i very neceary. Here we hall, when there i a new cluter head node join the mobile target tracking, under the leaderhip of node election rule, in all involved in tracking the cluter head node elect a ditance to a target the nearet cluter head node a it new leader node, data reported by the new leader node i ent to a data centre Decription of the Algorithm Step1: randomly deploy N enor node in monitored region of the target, through the relationhip among the area of coverage, enor node and the target node, from the frequent Copyright c 014 SERSC 41

8 item et the target node et with the mot node, T max i elected, and enor node et i elected from the enor node with high-energy. Step: remove the frequent node et that ha been elected in T, and then a new target node et T 1k i formed. Judge whether T 1k i empty. If it i, then turn to tep 5. Step3: Let the optimal ubet, calculate the coverage et and energy conumption of the concerned target node through Gauian normal denity function and probability function of coverage area, when the energy conumption i le than a pecific value, return G, otherwie elect the enor nod with more energy a the overlay node. Step4: calculate the probability of the node by uing the formula (4-6), then initialize S, 1 N n, G ' S ', T ', E ', W ' G S, T, E, W, determine whether S N i an empty et, and reet input item: G = (S, T, E, W), the output item: optimal ubet initialized, judge S N i equal to the empty et. It i not an empty et. If it not, et S S S, n n 1. N N h n Step5: determine the current energy of the enor node within the target area which i covered the current enor node, if c n w, go to Step. Step6: calculate to determine whether there are till target node not covered. If there i, then the target i covered by only one enor node. If the target node are all covered, then complete coverage i achieved. Initialization: et number of enor node to 0. Input: enor node et S={ 1,,, n }; Target monitor area Ω: the maximum target et; Output:the minimum node et S which cover the monitor area; Begin IF(N i empty ) S=empty et (N=0) //cover node et i empty End IF While (N) {Do S=S+Si // input the number of enor to node et S N=N+1 //increae the node number by 1 Do {Switch (n) n=1 IF (ening area of node S i i not covered) Set itelf into Active tatu; Send STATUS meage to neighbor node; S i Si; Keep litening tatu; End IF n= for k=1 to N do Tranfer (Gauian probability function) IF (flag) Flag=1 //meet the Gauian probability function, and determine the minimum coverage et Ele Flag=0 //call the Gauian probability function again End IF End For }} while(s) 4 Copyright c 014 SERSC

9 4.4. Node Scheduling Strategy The enor node i a round number a the cycle to work. During an initialization phae, the enor node cloed it induction module; update their information and the neighbor node. In the cheduling proce to go through five tate, repectively, the tart tate, judge tate of competition tate, hibernation, the litening tate, a five tate converion contitute the enor node cheduling trategy. Firt of all, to judge if the node meet the dormancy condition, uch a meet into hibernation, or into the competitive tatu, when entering into competition, tart a timer; econdly, when the node competitive ucce, node to the tart tate, competition failure node into the litening tate; again, in a ene node on ucce to receive the neighbor node to broadcat new On-duty Meage, update it neighbor node' information, thu entering the judgment condition; fourth, in the tarting tate of the node to it neighbor node end a On-duty Meage, which contain the tart node only ID identification and location information, and carrie on the effective coverage of the work; fifth, in order to ave the energy of the node, for accurate monitoring region to effectively cover, the enor node will turn off unneceary device to prolong the network life cycle. In practical application proce, the enor node according to the neighbor node' information to dipatch their information, until ure enor node itelf a the tart tate or reting tate o far, a hown in Figure 4: 5. Evaluation Sytem Figure 4. Node Scheduling State Diagram In order to evaluate the feature of the algorithm, thi paper MATLAB6.5 i adopted a a imulation platform in thi paper, the enor node are randomly deployed in different network area, the parameter are included in Table. Table. Simulation Parameter parameter value parameter value dimenion 1 100*100m ε amp 0(pJ/b)/m dimenion 00*00m E R-elec 30nJ/b dimenion 3 400*400m E min 0.0J Number 180 Header 0B R m Initial energy J E T-elec 50nJ/b broadcat 0B ε f 10(pJ/b)/m each round 100m The wirele communication model for Senor node tranmitting data and receiving data are repectively the following: E ( k, d ) E k E ( k, d ) Tr T elec am p E k d k d d T e le c f 0 4 E k d k d d T elec am p 0 (11) Copyright c 014 SERSC 43

10 In the above formula, E T-elec and E R-elec denote the energy conumption of wirele tranmitting module and wirele receiving module; ε f and ε amp tand for the energy conumption parameter of patial model and multiple attenuation model; d 0 i a contant. Experiment I: The firt cae i, with the ame repective parameter, execute 50 time and get the mean value, then execute for 400 to compare with the LEACH protocol the quantitative relationhip between number of remaining node and the number of turn, a hown in Figure 5: Figure 5. Remaining Node and the Round Number A can be een from Figure 5, with increaing of time, the number of remaining node of propoed algorithm i higher than the LEACH protocol, and then the concluion that with the increaing of time, the energy conumption of the propoed algorithm i lower than that of LEACH protocol, and the network lifetime i extended, alo the network reource are optimized. Experiment II: In order to achieve the cale of network coverage, and thu better evaluate the performance of the model in different ize, which mainly reflect the minimum number of node need to by deploy in different network coverage, each imulation experiment executed 50 time at average. Curve of node coverage change i hown in Figure 6: Figure 6. Coverage Rate for Different Coverage Area 44 Copyright c 014 SERSC

11 Figure 6 how the graph of the number of enor node needed to deploy to achieve different node coverage under different network dimenion. The Figure 6 how that, with the expanion of the network, to meet the demand for network coverage, the number of node required to be deployed will increae, and the higher the coverage of the network, the number of node need to be deployed increae can be obtained from Figure more fat, o that the concern target node can achieve complete coverage. Experiment: Figure 7 how a diagram of the number of enor node need to be deployed for the ame network ize 400 * 400m under different node coverage requirement, and compare with the experiment of literature SCCP algorithm [1], to meet certain demand for network coverage, the number of node deployed will be gradually increaed a time progree, and the network coverage will alo increae, o that completely coverage i achieved for the ame coverage area and different node coverage for target area, a hown in Figure 7: Figure 7. Coverage Comparion of Propoed Algorithm and SCCP Conider coverage and connectivity rate influenced by the boundarie. Figure 8 how the number of enor node required to be deployed under the condition without boundary effect with the ame network ize l=400m coverage and connectivity rate. From the Figure 8, with the increae of network coverage and connectivity, the number of node required increae ubtantially; and the influence gradually become maller and in equilibrium at lat when the network coverage and connectivity rate increae. Copyright c 014 SERSC 45

12 Figure 8. Curve of Network Coverage Rate/Connectivity Rate with/without Boundary Influence Figure 8 reflect the number of node required to be deployed to achieve different coverage and connectivity rate without the boundary influence. Compared with the boundary influence, the number of node deployed increae lightly, and with the increae of node, node denity will become larger, o the boundary influence become lower. 6. Concluion In thi paper, the Gauian normal denity function and probability function of the coverage area are adopted to optimized et of enor node to form a minimal ubet and determine the maximum target et, by tate tranition of enor node, node enter different tate, work in turn, thu network energy conumption i aved, network lifetime i extended, the ratio of network reource and quality of ervice i improved, alo redundant node are reduced, and lat, the network performance i optimized []. Finally, through imulation, the effectivene and tability of the algorithm are verified, due to the preence of vat network throughput and contrain by external factor, amativene to very large enor network i the next focu of the tudy ubject. Acknowledgement Science and Technology Department of Henan Province Natural Science Fund Project: No ; Natural Science Foundation for Young Scientit of Shanxi Province under Grant No. 013JQ804; Scientific Reearch Program Funded by Shaanxi Provincial Education Department Project No.013JK1139; Supported by China Potdoctoral Science Foundation Project No.013M54370; Science and technology reearch key project in Henan province department of education Project No:14B50099; Natural Science and Technology Reearch of Foundation Project of Henan Province Department of Science under Grant No: ; Supported by China Potdoctoral Science Foundation No:013M54370; upported by the Specialized Reearch Fund for the Doctoral Program of Higher Education of China Grant No: Copyright c 014 SERSC

13 Thi paper wa alo upported by The Key Science- Education Project of the Henan province Twelfth Five-Year-Plan under Grant No. 011-JKGHAB-0051 Reference [1] Y. Zou and K. Charrabarty, Senor deployment and tarter localization bae on virtual force proceeding, Elevier Ad Hoc Network, vol. 3, no. 86, (003). [] B. Jacque and M. Abdallah, M. Ahmed, Localization and Coverage for high denity enor network, Computer and Communication, vol. 7, no. 770, (008). [3] T. Neelofer and Y. Mohamed, Coverage-aware Connectivity retoration in mobile enor network, Journal of Network and Computer Application, vol. 11, no. 363, (010). [4] G. Xing, X. Wang and Y. Zhang, Integrated coverage and connectivity configuration for energy conervation in enor network, ACM Tranaction on Senor Network, vol. 1, no. 36, (005). [5] T. Chan and Q. Zhao, On the lifetime of wirele enor network, IEEE Communication Letter, vol. 4, no. 55, (005). [6] H. Zhang and J. Hou, On the upper bound of lifetime for large enor network, ACM Tran on Senor Network, vol. 1, no. 7, (005). [7] M. Cardeim, D. Macallum and M. Cheng, Wirele Senor Network with energy efficient organization, Journal of interconnection network, vol. 1, no. 13, (008). [8] F. Ning, G. Wang and X. Xing, Coverage algorithm with node equence in wirele enor network, Journal of Central South Univerity, vol. 10, no. 08, (011). [9] P. Zhou, X. Cui and S. Wang, Virtual force baed wirele enor network coverage enhancing algorithm, Journal of ytem imulation, vol. 9, no. 1416, (009). [10] W. Wei, S. Vikram and W, Bang, Coverage for Target Localization in Wirele Senor Network, IEEE Tranaction on Wirele Communication, vol. 6, no. 667, (008). [11] L. Liang and M. Huadong, On Coverage of Wirele Senor Network for Rolling Terrain, IEEE Tranaction on Parallel and Ditributed Sytem, vol., no. 118, (01). [1] Y. Yonrim and K. Y. Hyuk, An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wirele Senor Network, IEEE Tranaction on Cybernetic, vol. 10, no. 1473, (013). [13] L. Liang, Z. Xi and M. Huang, Percolation Theory-Baed Expoure-Path Prevention for Wirele Senor Network Coverage in Internet of Thing, IEEE Senor Journal, vol. 1, no. 365, (013). [14] H. Chihfan and L. Ningyan, Randomly Duty-cycled Wirele Senor Network: Dynamic of Coverage, IEEE Tranaction on Wirele Communication, vol. 5, no. 318, (006). [15] M. Teddy, A. V. Andrey and V. Savkin, A Ditributed Self-Deployment Algorithm for the Coverage of Mobile Wirele Senor Network, IEEE Communication Letter, vol. 9, no. 877, (009). [16] M. Habib and K. Sajal, Critical denity for coverage and connectivity in Three-dimenional wirele enor network uing continuum percolation, IEEE Tranaction on Parallel and Ditributed Sytem, vol. 11, no. 87, (009). [17] D. Tian and N. Georgana, A node cheduling cheme for energy conervation in large wirele enor network, Journal of Wirele Communication and Mobile Computing, vol., no. 71, (003). [18] C. Hang, Y. Teng and H. Wu, Ditributed protocol for enuring both coverage and connectivity of a wirele enor network, ACM Tranaction on Senor network, vol. 1, no. 1, (007). [19] S. Shakkottai, R. Srikant and N. Shroff, Unreliable enor grid: Coverage, Connectivity and Diameter, Ad Hoc Network, vol. 7, no. 70, (005). [0] Z. Chi and Z. Yanchao, A coverage inference protocol for wirele enor network, IEEE Tranaction on Mobile Computing, vol. 8, no. 850, (010). [1] X. Xing, G. Wang and J. Wu, Square Region-Baed Coverage and Connectivity Probability Model in Wirele Senor Network, th International Conference on Collaborative Computing: Networking, Application and Work haring, (009) November 1-8, Wahington, USA. [] M. Hefeeda and H. Ahmadi, Energy-efficient protocol for determinitic and probabilitic coverage in enor network, IEEE Tranaction on Parallel and Ditributed Sytem, vol. 7, no. 579, (010). Copyright c 014 SERSC 47

14 Author Zeyu Sun, he wa born in 1977 in Changchun city, Jilin province, in 010 graduated from Lanzhou Univerity, Mater of Science; xi 'an Jiaotong Univerity tudy for a doctorate at preent. He i a lecturer in Luoyang intitute of technology of computer and information engineering, i alo a member of China computer ociety. The main reearch interet i in wirele enor network, parallel computing and Internet of thing. Heng Li, he wa born in 1979 in Luohe City, Hennan province. He i a lecturer in Luoyang Intitute of Technology of Computer and Information Engineering. The main reearch interet i in wirele enor network and Internet of thing. Heng Chen, he wa born in 1979 in Linyi City, Shanxi province, received hi Ph.D. and M.S degree from Xi'an Jiaotong Univerity in 01 and 003. Currently he i a lecturer in Xi'an Jiaotong Univerity. Hi reearch interet are wirele network, wirele enor network application and mobile computing. Wei Wei, he wa born in 1975 in Xi'an city, Shaanxi province, received hi Ph.D. and M.S. degree from Xi an Jiaotong Univerity in 011 and 005. Currently he i an aitant Profeor at Xi an Univerity of Technology. The main earch interet i in wirele network and wirele enor network application, mobile computing, ditributed computing and pervaive computing. 48 Copyright c 014 SERSC