Localization Method Based on Modified Cuckoo Difference Optimization for Wireless Sensor Networks

doi:10.16182/j.issn1004731x.joss.201704012

Abstract

Abstract: To solve the sensitive and accumulative ranging error issue in the trilateration method and the maximum likelihood estimation method for the DV-Hop localization algorithm, an algorithm based on the Cuckoo difference optimization was proposed. The proposed algorithm essentially turned the positioning calculation problem into a group optimization problem. Using the Cuckoo algorithm and the differential evolution algorithm for parallel optimization with double populations, the proposed algorithm fused the advantages of the two kinds of intelligent optimization algorithms, which dynamically rectified the abandoned factor and the scaled variation factor randomly at the same time. The Cuckoo differential evolution algorithm's global search ability was enhanced to maintain the population diversity, which made the estimated coordinates closer to the real values. Without any increase in communication overhead, the positioning precision was improved effectively.

Key words: DV-Hop, cuckoo optimization, differential evolution, WSN

CLC Number:

TP393

Liu Dengfeng, Zhang Li, Bing Xiaoying, Shao Yuqian, Xu Baoguo. Localization Method Based on Modified Cuckoo Difference Optimization for Wireless Sensor Networks[J]. Journal of System Simulation, 2017, 29(4): 791-797.

References

[1] Chu Y-L, Tzeng J-R, Cheng Y-P, et al.Density-adaptive range-free localization in large-scale sensor networks[C]// 41st International Conference on Parallel Processing Workshops (ICPPW), 2012. USA: IEEE, 2012: 488-495.
[2] Maung N, Kawai M.Experimental evaluations of RSS threshold-based optimised DV-HOP localisation for wireless ad-hoc networks[J]. Electronics Letters (S1350-911X), 50(17): 1246-1248.
[3] Gayan S, Dias D.Improved DV-Hop algorithm through anchor position re-estimation[C]// 2014 IEEE Asia Pacific Conference on Wireless and Mobile, Bali, Indonesia, 2014. USA: IEEE, 2014: 126-131.
[4] Niculescu D, Nath B.DV Based Positioning in Ad Hoc Networks[J]. Telecommunication Systems (S1018-4864), 2003, 22(1/4): 267-280.
[5] 黄艳, 臧传治, 于海斌. 基于改进粒子群优化的无线传感器网络定位算法[J]. 控制与决策, 2012, 27(1): 156-160.
(Huang Y, Zang C, Yu H.Localization method based on modified particle swarm optimization for wireless sensor networks[J]. Control and Decision, 2012, 27(1): 156-160.)
[6] 林雯, 张烈平, 王守峰. 基于差分进化算法的无线传感器网络节点定位方法研究[J]. 计算机测量与控制(S1671-4598), 2013, 21(7): 2023-2026.
(Lin W, Zhang L, Wang S.Research on nodes localization method for wireless sensor networks based on differential evolution algorithm[J]. Computer Measurement & Control (S1671-4598), 2013, 21(7): 2023-2026.)
[7] 刘彦隆, 吕显朋, 王相国. 混合遗传算法在WSNs定位中的应用[J]. 传感器与微系统2014, 33(2): 150-153.
(Liu Y, Lv X, Wang X.Application of hybrid genetic algorithm in WSNs localization[J]. Transducer and Microsystem Technologies. 2014, 33(2): 150-153.)
[8] 李牧东, 熊伟, 梁青. 基于人工蜂群改进算法的无线传感器网络定位算法[J]. 传感技术学报, 2013, 26(2): 241-245.
(Li M, Xiong W, Liang Q.Wireless Sensor Networks Node Localization Algorithm Based on Improved ABC Algorithm[J]. Chinese Journal of Sensors and Actuators, 2013, 26(2): 241-245.)
[9] Yang X-S, Deb S.Cuckoo search via Lévy flights[C]// World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) Coimbatore, India 2009. USA: IEEE, 2009: 210-214.
[10] Walton S, Hassan O, Morgan K, et al.Modified cuckoo search: A new gradient free optimisation algorithm[J]. Chaos, Solitons & Fractals (S0960-0779), 2011, 44(9): 710-718.
[11] Yang X-S, Deb S.Engineering optimisation by cuckoo search[J]. International Journal of Mathematical Modelling and Numerical Optimisation (S2040-3607), 2010, 1(4): 330-343.
[12] Rajabioun R.Cuckoo optimization algorithm[J]. Applied Soft Computing (S1568-4946), 2011, 11(8): 5508-5518.
[13] Mantegna RN.Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes[J]. Physical Review E (S1539-3755), 1994, 49(5): 4677.
[14] Yang X-S.Nature-inspired metaheuristic algorithms [M]. UK: Luniver Press, 2010.
[15] 郑洪清, 周永权. 一种自适应步长布谷鸟搜索算法[J]. 计算机工程与应用, 2013, 49(10): 68-71.
(Zheng H, Zhou Y.Self-adaptive step cuckoo search algorithm[J]. Computer Engineering and Applications, 2013, 49(10): 68-71.)
[16] Storn R, Price K.Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces[M]. USA: ICSI Berkeley, 1995.
[17] 乔俊飞, 傅嗣鹏, 韩红桂. 基于混合变异策略的改进差分进化算法及函数优化[J]. 控制工程, 2013, 20(5): 943-947.
(Qiao J, Fu S, Han H.A Modified Differential Evolution Algorithm Based on Hybrid Mutation Strategy for Function Optimization[J]. Control Engineering of China (S1671-7848), 2013, 20(5): 943-947.)
[18] 贺淼, 阮奇, 郑晓桂, 等. 自适应布谷鸟搜索算法[J]. 计算机与应用化学, 2014, 31(8): 961-968.
(He M, Ruan Q, Zheng X, et al.Adaptive cuckoo search algorithm[J]. Computers and Applied Chemistry, 2014, 31(8): 961-968.)
[19] Kim H-K, Chong J-K, Park K-Y, et al.Differential evolution strategy for constrained global optimization and application to practical engineering problems[J]. IEEE Transactions on Magnetics (S0018-9464), 2007, 43(4): 1565-1568.
[20] Das S, Suganthan P N.Differential evolution: a survey of the state-of-the-art[J]. IEEE Transactions on Evolutionary Computation (S1089-778X), 2011, 15(1): 4-31.