Augmented Node Placement Model in t-WSN Through Multiobjective Approach  

作　　者：Kalaipriyan Thirugnansambandam Debnath Bhattacharyya Jaroslav Frnda Dinesh Kumar Anguraj Jan Nedoma 

机构地区：[1]School of Computer Science and Engineering,VIT University,Chennai Campus,Tamilnadu,India [2]Department of Computer Science and Engineering,Koneru Lakshmaiah Education Foundation,Vaddeswaram,Guntur,India [3]Department of Quantitative Methods and Economic Informatics,Faculty of Operation and Economics of Transport and Communications,University of Zilina,01026 Zilina,Slovakia [4]Department of Telecommunications,Faculty of Electrical Engineering and Computer Science,VSB-Technical University of Ostrava,70833 Ostrava-Poruba,Czech Republic

出　　处：《Computers, Materials & Continua》2021年第12期3629-3644,共16页计算机、材料和连续体（英文）

基　　金：This research has been funded with the support of the project SP2021/45,assigned to VSB-Technical University of Ostrava,the Ministry of Education,Youth and Sports in the Czech Republic.

摘　　要：In Wireless Sensor Network(WSN),coverage and connectivity are the vital challenges in the target-based region.The linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement position.In this paper,a multiobjective problem on target-based WSN(t-WSN)is derived,which minimizes the number of deployed nodes,and maximizes the cost of coverage and sensing range.An Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II(NSGA-II)is incorporated to tackle this multiobjective problem efficiently.Multiobjective problems are intended to solve different objectives of a problem simultaneously.Bio-inspired algorithms address the NP-hard problem most effectively in recent years.In NSGA-II,the Non-Dominated sorting preserves the better solution in different objectives simultaneously using dominance relation.In the diversity maintenance phase,density estimation and crowd comparison are the two components that balance the exploration and exploitation phase of the algorithm.Performance of NSGA-II on this multiobjective problem is evaluated in terms of performance indicators Overall Non-dominated Vector Generation(ONGV)and Spacing(SP).The simulation results show the proposed method performs outperforms the existing algorithms in different aspects of the model.

关 键 词：Focused wireless sensor network m-coverage k-connectivity problem non-dominated sorting NSGA-II 

分 类 号：TN9[电子电信—信息与通信工程]