Node location algorithm of artificial fish swarm mixing based on improved particle swarm
Sun Shaowei
Anhui Transport Consulting & Design Institute Co., Ltd/School of Mechanical and Automotive Engineering, Hefei University of Technology
Shen Guodong
Anhui Transport Consulting & Design Institute Co., Ltd
Liu Mingzhou
School of Mechanical and Automotive Engineering, Hefei University of Technology
Zou Jiao
Anhui Transport Consulting & Design Institute Co., Ltd
Wu Yue
School of Computer and Artificial Intelligence, Hefei Normal University
DOI: https://doi.org/10.59429/esta.v12i2.10559
Keywords: Node location; WSN; RWPOS; AFSA; Positioning accuracy
Abstract
Node positioning technology is a hot research area in wireless sensor networks (WSN). In view of the problems of large node localization errors in traditional DV-Hop algorithm, premature convergence and local optimization in PSO algorithm, a node localization algorithm based on the combination of random weight particle swarm optimization (RWPOS) and artificial fish swarm algorithm (AFSA) is proposed.By analyzing the inertia weight, maximum velocity, and learning factor of particles, the particle swarm optimization algorithm was improved and combined with the artificial fish swarm algorithm. Finally, four algorithms were compared and analyzed in three-dimensional space to investigate the impact of different beacon node ratios, different node communication radii, and different node numbers on node positioning error.The MATLAB simulation results show that compared with the improvement of the single algorithm, the hybrid algorithm has strong convergence and effectively improves the node positioning accuracy, thus providing an important basis for the application of this algorithm in practical situations.
References
[1]Zhou Wenbo, Zhang Yong, Sun Liangyi, and Su Jun. Research on an Improved Node Positioning Algorithm for Wireless Sensor Networks[J]. Journal of Marine Electronics Engineering, 2021(05):53-57
[2]Hatami, K.Pahlavan, M.Heidari, F.Akgul.On RSSI and TOA Based Indoor Geolocation-A Comparative Performance Evaluation[J]. IEEE Wireless Communications and Networking Conference, 2006: 2267-2272.
[3]X.Cheng, T. A, D.Chen.TPS:a time-based positioning scheme for outdoor wireless sensor networks[C]. International Conference on Computer Communications. IEEE, 2004: 2685-2696.
[4]D.Niculescu, B.Nath.Ad Hoc Positioning System(APS) Using AOA[C]. IEEE INFOCOM, San Francisco, 2003: 1734-1743.
[5]Wang J, Urriza P, Han Y, et al. Weighted Centroid Localization Algorithm: Theoretical Analys is and Distributed Implementation[J]. IEEE Transactions on Wireless Communications, 2011, 10(10):3403-3413.
[6]Kumar S, Lobiyal D K. An Advanced DV-Hop Localization Algorithm for Wireless Sensor Networks[M]. ICIEA, Kluwer Academic Publishers, Wireless Personal Communications, 2013:1557-1561.
[7]Xiu-wu Y U, Hao Y U, Yong L, et al. APIT Location Algorithm Based on Weighted Hybrid Filtering and Center of Gravity Method[J]. Journal of beijing university of posts and telecommunications, 2019, 42(04), 32-37.
[8]CHEN Min, WANG Bo, LI Jun-hua. Wireless Sensor Network Theory and Practice [M]. Beijing: Chemical Industry Press, 2011. [9]Liu Chuanzhou, Zhang Linghua. Optimization of positioning accuracy in wireless sensor networks based on DV-Hop algorithm [J]. Progress in Laser and Optoelectronics, 2021, 58(22): 498-504.
[10]Liang Yuming, Pei Xinghuan. Particle Swarm Optimization Artificial Fish Swarm Algorithm [J]. Computer Simulation, 2016, 33(06): 213-217+281.
[11] Localization in 3D Sensor Networks Using Stochastic Particle Swarm Optimization[J]. Wuhan University Journal of Natural Sciences, 2012, 17(06):544-548.
[12]Lou Guohong, Zhang Jianping Wireless sensor network node localization using particle swarm optimization for ranging correction [J] Journal of Jilin University (Science Edition), 2018, 56(03):650-656.
[13]Gao Yun Research and improvement of DV-Hop-based wireless sensor network localization algorithm [D]. Xi'an University of Electronic Science and Technology, 2020.
[14]Wang Lianguo, Shi Qiuhong, Hong Yi PSO AFSA hybrid optimization algorithm [J] Computer Engineering, 2010, 36(5): 176-178.
[15]Ren Xiaokui, Li Feng, Cheng Lin. An improved centroid localization algorithm based on dynamic loss factor and weight [J]. Computer Application, 2019(03): 824-828.