Analysis of Improved Particle Swarm Algorithm in Wireless Sensor Network Localization


  • Yafeng Chen



improved partical swarm algorithm, WSN, backward learning, chaotic search, linear fitting


WSN localization occupies an important position in the practical application of WSN. To complete WSN localization efficiently and accurately, the article constructs the objective function based on the target node location constraints and maximum likelihood function. It avoids premature convergence through the PSO algorithm based on chaos search and backward learning. Based on linear fitting, the node-flipping fuzzy detection method is proposed to perform the judgment of node flipping fuzzy phenomenon. And the detection method is combined with the localization algorithm, and the final WSN localization algorithm is obtained after multi-threshold processing. After analysis, it is found that compared with other PSO algorithms, the MTLFPSO algorithm used in the paper has better performance with the highest accuracy of 83.1%. Different threshold values will affect the favorable and error detection rates of different WSNs. For type 1 WSNs, the positive detection rate of the 3-node network is the highest under the same threshold value, followed by the 4-node network; when the threshold value is 7.5 (3 ), the positive detection rate of the 3-node network is 97.8%. Different numbers of anchor nodes and communication radius will have specific effects on the number of definable nodes and relative localization error, in which the lowest relative localization error of the MTLFPSO algorithm is 3.4% under different numbers of anchor nodes; the lowest relative localization error of MTLFPSO algorithm is 2.5% under different communication radius. The article adopts the method to achieve accurate and efficient localization of WSNs.


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Rajesh L, Mohan H S. Adaptive Group Teaching Based Clustering and Data Aggregation with Routing in Wireless Sensor Network. Wireless personal communications: An Internaional Journal, 2022,122(2):1839-1866.

Dong C, Feng S, Yu F. Performance optimisation of multichannel MAC in large-scale wireless sensor network. International Journal of Sensor Networks, 2022, 38(1):12-24.

Dhinnesh A N, Sabapathi T. Probabilistic neural network based efficient bandwidth allocation in wireless sensor networks. Journal of ambient intelligence and humanized computing, 2022,13(4):2001-2012.

Jiang S, Mashdoor S, Parvin H, Bui Anh Tuan,Kim-Hung Pho. An Adaptive Location-Aware Swarm Intelligence Optimization Algorithm. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2021,29(2): 249-279.

Sakthivel S, Manikandan M, Vivekanandhan V R. Design of efficient location-based multipath self-adaptive balancer router using particle swarm optimization in wireless sensor network. International journal of communication systems, 2022,35(4): e5060.1-e5060.14.

Mann P S, Singh S. Improved metaheuristic-based energy-efficient clustering protocol with optimal base station location in wireless sensor networks. Soft Computing, 2019, 23(3):1021-1037.

Jj A, Gh B, Hao W A, C MG. A survey on location privacy protection in Wireless Sensor Networks- ScienceDirect. Journal of Network and Computer Applications, 2019,125(Jan.):93-114.

Lu X, Zhang Y, Liu J, Yuan F,Cheng L. Mobile target tracking algorithm for wireless camera sensor networks with adjustable monitoring direction of nodes. International Journal of Communication Systems, 2019, 32(10): e3944.1-e3944.14.

Raja M, Koduru T, Datta R. Protecting Source Location Privacy in IoT Enabled Wireless Sensor Networks: The Case of Multiple Assets. IEEE, 2021,1(9):10807 - 10820.

Varshovi H, Kavian Y S, Ansari-Asl K. Design and implementing wireless multimedia sensor network for movement detection using FPGA local co-processing. Multimedia Tools and Applications, 2019, 78(13):17413-17435.

Yu H, Zheng M, Zhang W, Nie W, Bian T. Optimal design of helical flute of irregular tooth end milling cutter based on particle swarm optimization algorithm. Proceedings of the Institution of Mechanical Engineers, Part C. Journal of mechanical engineering science, 2022,236(7):3323-3339.

Purushothaman K E, Nagarajan V. Multiobjective optimization based on selfrganizing Particle Swarm Optimization algorithm for massive MIMO 5G wireless network. International Journal of Communication Systems, 2021,34(4): e4725.1-e4725.15.

Diaz-Ramirez V H, Juarez-Salazar R, Zheng J, Hernandez-Beltran JE,Marquez A. Homography estimation from a single-point correspondence using template matching and particle swarm optimization. Applied optics, 2022,61(7): D63-D74.

Bacar A, Rawhoudine S C. An attractors-based particle swarm optimization for multiobjective capacitated vehicle routing problem. RAIRO - Operations Research, 2021, 55(5):2599-2614.

Jubair A M, Hassan R, Aman A, Sallehudin H. Social class particle swarm optimization for variable-length Wireless Sensor Network Deployment. Applied Soft Computing, 2021,113(Pt.B):107926-1-107926-20.

Lin C, Han G, Qi X, J Du,T Xu,M Martinez-Garcia. Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach. IEEE transactions on industrial informatics, 2021,17(6):4411-4420.

Zali H M, Mahmood M, Pasya I, I Pasya,M Hirose,N Ramli. Narrowband and wideband EMW path loss in underwater wireless sensor network. Sensor Review, 2022,42(1):125-132.

Umashankar M L. An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm. Indian Journal of Science and Technology, 2021, 14(3):270-288.




How to Cite

Chen Y. Analysis of Improved Particle Swarm Algorithm in Wireless Sensor Network Localization. EAI Endorsed Trans Energy Web [Internet]. 2023 Sep. 11 [cited 2023 Sep. 22];10. Available from: