PDF(3331 KB)
PDF(3331 KB)
PDF(3331 KB)
基于改进粒子群算法和特征点集的无线传感器网络覆盖问题研究
Area Coverage Problem Based on Improved PSO Algorithm and Feature Point Set in Wireless Sensor Networks
本文针对基于网格点的区域覆盖算法未考虑网络的固有特征,导致算法存在近似及复杂度偏高等问题,通过研究区域覆盖的特征,结合概率感知模型,对区域内两点的覆盖率关系进行分析,定义了特征点集的概念;对特征点集进行建模,将区域覆盖转化为基于特征点集的优化问题.利用改进粒子群算法解算此优化问题,通过惯性权重及局部增强因子扰动项,避免其陷入早熟状态;同时,针对集中式PSO算法不适用于无线传感网的问题,本文提出了一种并行分区式策略.仿真分析验证了所提算法的优越性和特征点距上界的存在性,该方法为区域覆盖问题的研究提供了新的思路.
Traditional grid point-based area coverage methods are committed to algorithm optimization,causing coarse approximation and high complexity problems.In order to solve these problems,based on the probabilistic sensing model,we first study the sensing probabilities of two adjacent points and obtain the fundamental mathematical relationship between them.According to this relationship,we define the concept of feature point set (FPS) to character the area.Then,we transform the probabilistic area coverage into optimization problem of FPS.Further,we design an improved particle swarm optimization (IWPSO) algorithm to solve this optimization problem,which can effectively avoid the premature problems in the convergence of PSO algorithm.Finally,through extensive simulations,we demonstrate that our algorithm outperforms the proposed solutions significantly,and provides a new train of thought for area coverage problem.
无线传感器网络 / 覆盖约束优化 / 概率感知模型 / 特征点集 / 惯性权重 / 并行分区式粒子群算法 {{custom_keyword}} /
wireless sensor networks / coverage optimization problem / probabilistic sensing model / feature point set / inertia weight / parallel local particle swarm optimization {{custom_keyword}} /
[1] 李劲,岳昆,等.基于融合的无线传感器网络k-集覆盖的分布式算法[J].电子学报,2013,41(4):659-665. LI Jin,YUE Kun,et al.Distributed set k-cover algorithms for fusion-based coverage in wireless sensor networks[J].Acta Electronica Sinica,2013,41(4):659-665.(in Chinese)
[2] WANG Xue-qing,ZHANG Shu-qin.Research on efficient coverage problem of node in wireless sensor networks[A].Proceedings of International Conference on Industrial Mechatronics and Automation[C].Chengdu,China:IEEE,2009.9-13.
[3] 陆克中,等.有向传感器网络覆盖增强问题的贪婪迭代算法[J].电子学报,2012,40(4):688-694. LU Ke-zhong,et al.A greedy iterative algorithm of coverage enhancing problem in direction sensor network[J].Acta Electronica Sinica,2012,40(4):688-694.(in Chinese)
[4] J Li,J Chen,T H Lai.Energy-efficient intrusion detection with a barrier of probabilistic sensors[A].Proceedings of IEEE Infocom[C].Orlando:IEEE,2012.118-126.
[5] M T XIANG,et al.Condition for the coverage and connectivity of wireless sensor network[A].Proceedings of Advanced Materials Research[C].Switzerland:TTP,2012.2589-2592.
[6] LI Hui,ZHANG Xiao-guang,et al.A hybrid deployment algorithm based on clonal selection and artificial physics optimization for wireless sensor network[J].Information Technology Journal,2013,12(5):917-925.
[7] Shen,et al.Grid scan:a simple and effective approach for coverage issue in wireless sensor networks[A].Proceedings of IEEE International Conference Communications[C].Istanbul:IEEE,2006.3480-3484.
[8] M HEFEEDA,H AHMADI.Energy-efficient protocol for deterministic and probabilistic coverage in sensor networks[J].IEEE Transactions on Parallel and Distributed Systems,2010,21(5):579-593.
[9] KUMLACHEW M W,GARY G Y.Vaccine-enhanced artificial immune system for multimodal function optimization[J].IEEE Transactions on System,Man,and Cybernetics-Part B:Cybernetics,2010,40(1):218-228.
[10] Y YOON,Y H KIM.An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks[J].IEEE Transactions on Cybernetics,2013,43(5):2168-2267.
[11] Hu X M,Zhang J,et al.Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks[J].IEEE Trans Evol Comput,2010,14(5):766-781.
[12] Yang Q,He S,et al.Energy-efficient probabilistic full coverage in wireless sensor networks[A].Proceedings of IEEE GlobeCom[C].Anaheim:IEEE,2012.591-596.
[13] B ANAND,I AAKASH.Improvisation of particle swarm optimization algorithm[A].Proceedings of International Conference on Signal Processing and Integrated Networks[C].Noida:IEEE,2014.20-24.
[14] 刘维亭,范洲远.基于混沌粒子群算法的无线传感器网络覆盖优化[J].计算机应用,2011,31(2):338-341. LIU Wei-ting,FAN Zhou-yuan.Coverage optimization of wireless sensor network based on chaos particle swarm optimization algorithm[J].Journal of Computer Applications,2011,31(2):338-341.(in Chinese)
[15] W Z W,et al.Study on coverage in wireless sensor network using grid based strategy and particle swarm optimization[A].Proceedings of IEEE Asia Pacific Conference on Circuits and Systems[C].Kuala Lumpur:IEEE,2010.1175-1178.
[16] S Y H,et al.Empirical study of particle swarm optimization[A].Proceedings of the IEEE Congress on Evolutionary Computation[C].Washington DC:IEEE,1999.1945 -1950.
[17] KULKARNI R V,et al.Particle swarm optimization in wireless sensor networks:a brief survey[J].IEEE Trans on Systems,Man and Cybernetics,2010,41(2):262-267.
[18] 王雪,等.无线传感网络布局的虚拟力导向微粒群优化策略[J].电子学报,2007,35(11):2039-2042. WANG Xue,et al.Dynamic sensor deployment strategy based on virtual force-directed particle swarm optimization in wireless sensor networks[J].Acta Electronica Sinica,2007,35(11):2039-2042.(in Chinese)
教育部博士点专项基金 (No.20113219110028); 江苏省自然科学基金 (No.BK2012803)
/
| 〈 |
|
〉 |
