Routing algorithm for railway monitoring linear WSN based on improved PSO

被引：0
|
作者：
Li C. [1 ]
Wang X. [1 ]
Xie J. [1 ]
Lyu A. [1 ]
机构：
[1] School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou
来源：
Tongxin Xuebao/Journal on Communications | 2022年 / 43卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Breadth first search; Energy consumption-balanced; Linear wireless sensor network; Particle swarm optimization; Railway environment monitoring;
D O I：
10.11959/j.issn.1000-436x.2022109
中图分类号：
学科分类号：
摘要：
To solve the problems of short network lifetime and large data transmission delay, caused by unbalanced node energy consumption of linear wireless sensor network in railway monitoring scenario, a routing algorithm based on particle swarm optimization theory and breadth first search was proposed. The fitness function was constructed based on the relative energy consumption, spacing and load of candidate cluster heads. The local search ability of particle swarm algorithm was enhanced by adjusting the inertia weight coefficient to set up the cluster head optimal set. Meanwhile, a path cost function driven by energy consumption and delay was built up, and the optimal main path from the source node to the sink node was obtained by breadth first search. Lastly, a Q-learning alternative path updating and route maintenance mechanism based on discrete Markov decision process (MDP) was designed. Simulation results show that the proposed algorithm can balance the node energy consumption effectively, and has also advantages in prolonging the network lifetime and reducing the data transmission delay. © 2022, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：155 / 165
页数：10
相关论文
共 31 条
  • [1] KUMAR S A A, OVSTHUS K, KRISTENSEN L M., An industrial perspective on wireless sensor networks-a survey of requirements, protocols, and challenges, IEEE Communications Surveys & Tutorials, 16, 3, pp. 1391-1412, (2014)
  • [2] HU C J, YUAN S J., An adaptive data collection method of energy efficiency and energy consumption balance in WSN for coal mines, Journal of Beijing University of Posts and Telecommunications, 41, 2, pp. 86-91, (2018)
  • [3] REN J, ZHANG Y X, ZHANG K, Et al., Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks, IEEE Transactions on Industrial Informatics, 12, 2, pp. 788-800, (2016)
  • [4] LV X J, LI J, SHI T Y, Et al., Topology analysis based on linear wireless sensor networks in monitoring of high-speed railways, Proceedings of 2016 Chinese Control and Decision Conference (CCDC), pp. 1797-1802, (2016)
  • [5] DIAO P F, WANG Y J., Coverage-preserving clustering algorithm for underwater sensor networks based on the sleeping mechanism, Journal of Electronics & Information Technology, 40, 5, pp. 1101-1107, (2018)
  • [6] ZHU B T, BEDEER E, NGUYEN H H, Et al., UAV trajectory planning in wireless sensor networks for energy consumption minimization by deep reinforcement learning, IEEE Transactions on Vehicular Technology, 70, 9, pp. 9540-9554, (2021)
  • [7] AHMED S, GUPTA S, SURI A, Et al., Adaptive energy efficient fuzzy: an adaptive and energy efficient fuzzy clustering algorithm for wireless sensor network-based landslide detection system, IET Networks, 10, 1, pp. 1-12, (2021)
  • [8] RATHEE M, KUMAR S, GANDOMI A H, Et al., Ant colony optimization based quality of service aware energy balancing secure routing algorithm for wireless sensor networks, IEEE Transactions on Engineering Management, 68, 1, pp. 170-182, (2021)
  • [9] POONGUZHALI P K, ANANTHAMOORTHY N P., Improved energy efficient WSN using ACO based HSA for optimal cluster head selection, Peer-to-Peer Networking and Applications, 13, 4, pp. 1102-1108, (2020)
  • [10] PAVANI M, TRINATHA RAO P., Adaptive PSO with optimised firefly algorithms for secure cluster-based routing in wireless sensor networks, IET Wireless Sensor Systems, 9, 5, pp. 274-283, (2019)
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