基于增强蚁群算法的传感网移动sink路径规划

doi:10.16182/j.issn1004731x.joss.19-0298

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (11): 2543-2552.doi: 10.16182/j.issn1004731x.joss.19-0298

基于增强蚁群算法的传感网移动sink路径规划

吉珊珊

东莞职业技术学院,广东东莞 523808

收稿日期:2019-07-10 修回日期:2019-09-12 出版日期:2019-11-10 发布日期:2019-12-13
作者简介:吉珊珊(1985-),女,广东五华,硕士,工程师,讲师,研究方向为计算机智能信息处理与控制,计算机仿真,计算机教育。
基金资助:
2018东莞职业技术学院政校行企合作项目(政2018019),东莞职业技术学院技艺能手项目(Y17040321)

Path Planning for Mobile Sink Based on Enhanced Ant Colony Optimization Algorithm in Wireless Sensor Networks

Ji Shanshan

Department of Computer Engineering, Donggguan Polytechnic, Dongguan 523808, China

Received:2019-07-10 Revised:2019-09-12 Online:2019-11-10 Published:2019-12-13

摘要/Abstract

摘要： 为同时降低移动sink无线传感器网络的能耗与sink移动距离,提出了一种基于增强蚁群算法的传感网络移动sink路径规划算法。为人工蚁群算法引入了遗传算子,避免人工蚁群算法早熟收敛。将数据量不均匀作为网络的约束条件,将网络生命期与sink的移动距离作为问题的2个优化目标,采用增强的人工蚁群算法选择汇集点的帕累托次优集。多组仿真实验的结果表明,该算法有效地降低了网络平均能耗,提高了网络能耗的均衡性。

关键词: 无线传感器网络, 路径规划, 遗传算法, 人工蚁群优化, 有向图生成树, 网络生命期

Abstract: To reduce the energy consumption and sink mobile distance of mobile sink wireless sensor networks simultaneously, a path planning algorithm for mobile sink based on enhanced ant colony optimization algorithm in wireless sensor networks is proposed. Genetic operators are introduced to ant colony optimization algorithm in order to prevent ant colony optimization premature. The non-uniform of data distribution is considered as the constraint condition, the network lifetime and sink mobile distance are considered as a multi-objective problem, and the enhanced ant colony optimization is adopted to search the Pareto sub-optimal sets of rendezvous points. The simulation results show that the proposed algorithm reduces the network average energy consumption effectively and improves the energy consumption balance.

Key words: wireless sensor network, path planning, genetic algorithm, artificial ant colony optimization, directed graph spanning tree, network lifetime

中图分类号:

TP391

吉珊珊. 基于增强蚁群算法的传感网移动sink路径规划[J]. 系统仿真学报, 2019, 31(11): 2543-2552.

Ji Shanshan. Path Planning for Mobile Sink Based on Enhanced Ant Colony Optimization Algorithm in Wireless Sensor Networks[J]. Journal of System Simulation, 2019, 31(11): 2543-2552.

参考文献

[1] 董玮, 陈共龙, 曹晨红, 等. 面向软件定义架构的无线传感器网络[J]. 计算机学报, 2017, 40(8): 1779-1797.
Dong Wei, Chen Gonglong, Cao Chenhong, et al.Towards a Software-Defined Architecture for Wireless Sensor Networks[J]. Chinese Journal of Computers, 2017, 40(8): 1779-1797.
[2] 蒋文贤, 缪海星, 王田, 等. 无线传感器网络中移动式覆盖控制研究综述[J]. 小型微型计算机系统, 2017, 38(3): 417-424.
Jiang Wenxian, Miao Haixing, Wang Tian, et al.Survey on Coverage Control with Mobile Elements in Wireless Sensor Networks[J]. Journal of Chinese Computer Systems, 2017, 38(3): 417-424.
[3] Zhou Z B, Chu D, Lei S, et al.An Energy-Balanced Heuristic for Mobile Sink Scheduling in Hybrid WSNs[J]. IEEE Transactions on Industrial Informatics (S1536-1241), 2016, 12(1): 28-40.
[4] Nitesh K, Azharuddin M, Jana P.Minimum spanning tree-based delay-aware mobile sink traversal in wireless sensor networks: Delay-Aware Mobile Sink Traversal in WSN[J]. Internsational Journal of Communication Systems (S1074-5351), 2017, 30(13): e3270.
[5] 莫文杰, 郑霖. 优化网络生命周期和最短化路径的WSN移动sink路径规划算法[J]. 计算机应用, 2017, 37(8): 2150-2156.
Mo Wenjie, Zheng Lin.Path planning algorithm for mobile sink with optimized network lifetime and shortest path in wireless sensor network[J]. Journal of Computer Applications, 2017, 37(8): 2150-2156.
[6] 惠晓威, 刘彦每. WSN数据收集中移动Sink的路径规划和簇头节点选取问题的综合研究[J]. 传感技术学报, 2014, 1(1): 118-122.
Hui Xiaowei, Liu Yanmei.Comprehensive Study on the Problem of Mobile Sink Path Planning and the Cluster Head Node Selecting in WSN Data Collection[J]. Chinese Journal of Sensors and Actuators, 2014, 1(1): 118-122.
[7] 万成, 常捷, 张灵. 针对单区域突发流量的移动Sink路径规划[J]. 计算机科学, 2017, 44(5):61-65.
Wan Cheng, Chang Jie, Zhang Ling.Dynamic Path Planning Design of Mobile Sink for Single Region Bursty Traffic[J]. Computer Science, 2017, 44(5): 61-65.
[8] Yang J Y, Ding R F, Zhang Y, et al.An improved ant colony optimization (I-ACO) method for the quasi travelling salesman problem (Quasi-TSP)[J]. International Journal of Geographical Information Science (S1365-8816), 2015, 29(9): 1534-1551.
[9] 周冬梅, 孙俊. 遗传规划中遗传算子对种群多样性的影响[J]. 计算机工程与应用, 2016, 52(20): 39-45.
Zhou Dongmei, Sun Jun.Influences of genetic operators on population diversity in genetic programming[J]. CEA, 2016, 52(20): 39-45.
[10] Amgoth T, Jana P K.Energy-aware routing algorithm for wireless sensor networks[J]. Computers and Electrical Engineering (S0045-7906), 2015, 41: 357-367.
[11] Kaswan A, Nitesh K, Jana P K.Energy Efficient Path Selection for Mobile Sink and Data Gathering in Wireless Sensor Networks[J]. AEUE - International Journal of Electronics and Communications (S1434-8411), 2017, 73: 110-118.
[12] Wen W, Zhao S, Shang C, et al.EAPC: Energy-Aware Path Construction for Data Collection using Mobile Sink in Wireless Sensor Networks[J]. IEEE Sensors Journal (S1530-437X), 2017, 1(99): 1.
[13] Liu W, Fan J, Zhang S, et al.Relay Hop Constrained Rendezvous Algorithm for Mobile Data Gathering in Wireless Sensor Networks[C]. Network and Parallel Computing, 2013.
[14] 余思琴, 闫秋艳, 闫欣鸣. 基于最佳u-shapelets的时间序列聚类算法[J]. 计算机应用, 2017, 37(8): 2349-2356.
Yu Siqin, Yan Qiuyan, Yan Xinming.Clustering algorithm of time series with optimal u-shapelets[J]. Journal of Computer Applications, 2017, 37(8): 2349-2356.

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基于增强蚁群算法的传感网移动sink路径规划

Path Planning for Mobile Sink Based on Enhanced Ant Colony Optimization Algorithm in Wireless Sensor Networks

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