A Bi-objective Virtual-force Local Search PSO Algorithm for Improving Sensing Deployment in Wireless Sensor Network

Kiani, Vahid; Imanparast, Mahdi

doi:10.22044/jadm.2023.11917.2339

Document Type : Original/Review Paper

Authors

Vahid Kiani ¹
Mahdi Imanparast ²

¹ Department of Computer Engineering, University of Bojnord, Bojnord, Iran.

² Department of Computer Science, University of Bojnord, Bojnord, Iran.

https://doi.org/10.22044/jadm.2023.11917.2339

Abstract

In this paper, we present a bi-objective virtual-force local search particle swarm optimization (BVFPSO) algorithm to improve the placement of sensors in wireless sensor networks while it simultaneously increases the coverage rate and preserves the battery energy of the sensors. Mostly, sensor nodes in a wireless sensor network are first randomly deployed in the target area, and their deployment should be then modified such that some objective functions are obtained. In the proposed BVFPSO algorithm, PSO is used as the basic meta-heuristic algorithm and the virtual-force operator is used as the local search. As far as we know, this is the first time that a bi-objective PSO algorithm has been combined with a virtual force operator to improve the coverage rate of sensors while preserving their battery energy. The results of the simulations on some initial random deployments with the different numbers of sensors show that the BVFPSO algorithm by combining two objectives and using virtual-force local search is enabled to achieve a more efficient deployment in comparison to the competitive algorithms PSO, GA, FRED and VFA with providing simultaneously maximum coverage rate and the minimum energy consumption.

Keywords

20.1001.1.23225211.2023.11.1.1.6

References

[1] R. Priyadarshi, B. Gupta, and A. Anurag, “Deployment techniques in wireless sensor networks: a survey, classification, challenges, and future research issues,” The Journal of Supercomputing, Vol. 76, No. 9, pp. 7333–7373,2020.

[2] D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, “Applications of Wireless Sensor Networks: An Up-to-Date Survey,” Applied System Innovation, Vol. 3, No. 1, 2020.

[3] A. Sangwan and R. P. Singh, “Survey on Coverage Problems in Wireless Sensor Networks,” Wireless Personal Communications, Vol. 80, No. 4, pp. 1475–1500, 2015.

[4] H. I. Sweidan and T. C. Havens, “Coverage optimization in a terrain-aware wireless sensor network,” in 2016 IEEE Congress on Evolutionary Computation (CEC), Jul. 2016, pp. 3687–3694.

[5] Y. Zou and K. Chakrabarty, “Sensor Deployment and Target Localization in Distributed Sensor Networks,” ACM Transactions on Embedded Computing Systems, Vol. 3, No. 1, pp. 61–91, Feb. 2004.

[6] M. Abo-Zahhad, S. M. Ahmed, N. Sabor, and S. Sasaki, “Rearrangement of Mobile Wireless Sensor Nodes for Coverage Maximization Based on Immune Node Deployment Algorithm,” Computers & Electrical Engineering, Vol. 43, No. C, pp. 76–89, Apr. 2015.

[7] B. Wang, “Coverage Problems in Sensor Networks: A Survey,” ACM Computing Surveys, Vol. 43, No. 4, 2011.

[8] D. Liang, H. Shen, and L. Chen, “Maximum Target Coverage Problem in Mobile Wireless Sensor Networks,” Sensors, Vol. 21, No. 1, p. 184, 2020.

[9] H. ZainEldin, M. Badawy, M. Elhosseini, H. Arafat, and A. Abraham, “An improved dynamic deployment technique based-on genetic algorithm (IDDT-GA) for maximizing coverage in wireless sensor networks,” Journal of Ambient Intelligence and Humanized Computing, Vol. 11, No. 10, pp. 4177–4194, 2020.

[10] H. T. T. Binh, N. T. Hanh, L. V. Quan, N. D. Nghia, and N. Dey, “Metaheuristics for maximization of obstacles constrained area coverage in heterogeneous wireless sensor networks,” Applied Soft Computing, Vol. 86, p. 105939, 2020.

[11] K. Tarnaris, I. Preka, D. Kandris, and A. Alexandridis, “Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study,” Electronics, Vol. 9, No. 4, Apr. 2020.

[12] A. Osmani, M. Dehghan, H. Pourakbar, and P. Emdadi, “Fuzzy-Based Movement-Assisted Sensor Deployment Method in Wireless Sensor Networks,” in 2009 First International Conference on Computational Intelligence, Communication Systems and Networks, pp. 90–95, 2009.

[13] S. Liu, R. Zhang, and Y. Shi, “Design of coverage algorithm for mobile sensor networks based on virtual molecular force,” Computer Communications, Vol. 150, pp. 269–277, 2020.

[14] S. Wang, X. Yang, X. Wang, and Z. Qian, “A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization,” Sensors, Vol. 19, No. 12, 2019.

[15] J. Xie, D. Wei, S. Huang, and X. Bu, “A Sensor Deployment Approach Using Improved Virtual Force Algorithm Based on Area Intensity for Multisensor Networks,” Mathematical Problems in Engineering, Vol. 2019, p. 8015309, 2019.

[16] X. Deng, Z. Yu, R. Tang, X. Qian, K. Yuan, and S. Liu, “An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications,” Sensors, Vol. 19, No. 8, p. 1817, 2019.

[17] M. Song, L. Yang, W. Li, and T. A. Gulliver, “Improving wireless sensor network coverage using the VF-BBO algorithm,” in 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), Aug. 2013, pp. 318–321.

[18] A. More and V. Raisinghani, “A survey on energy efficient coverage protocols in wireless sensor networks,” Journal of King Saud University-Computer and Information Sciences, Vol. 29, No. 4, pp. 428–448, 2017.

[19] Q. Zhang and M. P. Fok, “A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks,” Sensors, Vol. 17, No. 1, 2017.

[20] N. A. A. Aziz, A. W. Mohemmed, M. Y. Alias, K. A. Aziz, and S. Syahali, “Coverage Maximization and Energy Conservation for Mobile Wireless Sensor Networks: A Two Phase Particle Swarm Optimization Algorithm,” International Journal of Natural Computing Research (IJNCR), Vol. 3, No. 2, pp. 43–63, 2012.

[21] Y. Qu and S. V. Georgakopoulos, “Relocation of wireless sensor network nodes using a genetic algorithm,” in WAMICON 2011 Conference Proceedings, Apr. 2011, pp. 1–5.

[22] E. Tuba, M. Tuba and M. Beko, “Mobile wireless sensor networks coverage maximization by firefly algorithm". In Proceedings of 27^th Conference Radioelektronika, pp. 1–5, 2017.

[23] X. Bai, S. Li, and J. Xu, “Mobile Sensor Deployment Optimization for k-Coverage in Wireless Sensor Networks with a Limited Mobility Model,” IETE Technical Review, Vol. 27, No. 2, pp. 124–137, 2010.

[24] N. Heo and P. K. Varshney, “Energy-efficient deployment of Intelligent Mobile sensor networks,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol. 35, No. 1, pp. 78–92, 2005.

[25] J. Pournazari, M. Alaei, and F. Yazdanpanah, “An Energy Efficient Autonomous Method for Coverage Optimization in Wireless Multimedia Sensor Networks,” Wireless Personal Communications, Vol. 99, No. 2, pp. 717–736, 2018.

[26] N. Qin, and J. Chen, “An area coverage algorithm for wireless sensor networks based on differential evolution,” International Journal of Distributed Sensor Networks, Vol. 14, No. 8, 2018.

[27] H. Sheikhi, W. Barkhoda, “Solving the k- Coverage and m-Connected Problem in Wireless Sensor Networks through the Imperialist Competitive Algorithm,” Journal of Interconnec-tion Networks, Vol. 20, No. 1, p. 2050002, 2020.

[28] G. P. Gupta, and S. Jha, “Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks,” Wireless Networks, Vol. 25, No. 6, pp. 3167–3177, 2019.

[29] S. M. Hosseinirad, “Multi-layer Clustering Topology Design in Densely Deployed Wireless Sensor Network using Evolutionary Algorithms,” Journal of AI and Data Mining, Vol. 6, No. 2, pp. 297-311, 2018.

[30] R. Liu, C. Ma, W. Ma, and Y. Li, “A Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling,” The Scientific World Journal, Vol. 2013, p. 387194, 2013.

A Bi-objective Virtual-force Local Search PSO Algorithm for Improving Sensing Deployment in Wireless Sensor Network

References

References

Volume 11, Issue 1January 2023Pages 1-12

Volume 11, Issue 1
January 2023
Pages 1-12