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2-D coverage optimization in obstacle-based FOI in WSN using modified PSO

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Abstract

Wireless sensor network found immense uses in the daily life. Also, the random deployment of nodes is a preferable option in many applications such as earthquake observation, military applications, and forest fire detection. It is expected that deployed nodes should be able to monitor the field of interest (FoI) with the optimum capacity. In order to maximize the coverage of area, each node should be repositioned to an optimal position inside the FoI. A modified particle swarm optimization (PSO) algorithm has been proposed to achieve optimize coverage while keeping the number of nodes minimum. It introduces the concept of negative velocity in order to avoid premature convergence of the algorithm. This paper describes a way to tackle the two dimensional obstacles present inside the FoI. The simulated results show a significant improvement in the performance with compared to the standard PSO in presence of obstacles.

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References

  1. Elhabyan R, Shi W, St-Hilaire M (2019) Coverage protocols for wireless sensor networks: review and future directions. J Commun Networks 21(1):45–60

    Article  Google Scholar 

  2. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Networks 52(12):2292–2330

    Article  Google Scholar 

  3. Farsi M, Elhosseini MA, Badawy M, Arafat Ali H, Zain Eldin H (2019) Deployment techniques in wireless sensor networks coverage and connectivity: a survey. In IEEE Access 7:28940–28954

    Article  Google Scholar 

  4. Ammari HM (2010) Coverage in wireless sensor networks: a survey. Netw Protoc Algorithms 2(2):27–53

    Google Scholar 

  5. Chang YJ, Chen CH, Lin LF, Han RP, Huang WT, Lee GC (2012) Wireless sensor networks for vital signs monitoring: application in a nursing home. Int J Distrib Sens Networks 8(11):685107

    Article  Google Scholar 

  6. Zheng J, Jamalipour A (2009) Wireless sensor networks: a networking perspective. John Wiley & Sons

    Book  MATH  Google Scholar 

  7. A. Boubrima, W. Bechkit, and H. Rivano, “Optimal WSN Deployment Models for Air Pollution Monitoring,” IEEE Trans. Wirel. Commun., 2017.

  8. H. Wang, J. Wang, and M. Huang, “Building a smart home system with WSN and service robot,” in Proceedings - 2013 5th Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2013, 2013.

  9. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey”. Comput Networks 38(4):393–442

    Article  Google Scholar 

  10. Ababnah A and Natarajan B (2011) Optimal control-based strategy for sensor deployment, IEEE Trans Syst Man Cybern Part A Systems Humans.

  11. Tsai CW, Tsai PW, Pan JS, Chao HC (2015) Metaheuristics for the deployment problem of WSN: a review. Microprocess Microsyst 39(8):1305–1317

    Article  Google Scholar 

  12. Gupta SK, Kuila P and Jana PK (2016) Genetic algorithm for k-connected relay node placement in wireless sensor networks,” In: Advances in Intelligent Systems and Computing.

  13. Eberhart R and Kennedy J (1995) A new optimizer using particle swarm theory, In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp 39–43.

  14. Fan Z and Zhao W (2011) Network coverage optimization strategy in wireless sensor networks based on particle swarm optimization.

  15. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, Tech. Rep. TR06, Erciyes Univ.

  16. Öztürk C, Karaboǧa D, Görkemli B (2012) Artificial bee colony algorithm for dynamic deployment of wireless sensor networks. Turkish J Electr Eng Comput Sci 20(2):255–262

    Google Scholar 

  17. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278

    Article  MathSciNet  MATH  Google Scholar 

  18. Liao WH, Kao Y, Wu RT (2011) Ant colony optimization based sensor deployment protocol for wireless sensor networks. Expert Syst Appl 38(6):6599–6605

    Article  Google Scholar 

  19. Du KL and Swamy MNS (2016) Search and optimization by metaheuristics: Techniques and algorithms inspired by nature.

  20. El Khamlichi Y, Tahiri A, Abtoy A, Medina-Bulo I, Palomo-Lozano F (2017) A hybrid algorithm for optimal wireless sensor network deployment with the minimum number of sensor nodes. Algorithms 10(3):80

    Article  MathSciNet  MATH  Google Scholar 

  21. Cao B, Zhao J, Lv Z, Liu X, Kang X, Yang S (2018) Deployment optimization for 3D industrial wireless sensor networks based on particle swarm optimizers with distributed parallelism. J Netw Comput Appl 103:225–238

    Article  Google Scholar 

  22. Aziz NABA, Mohemmed AW and Alias MY (2009) A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram, In: 2009 International Conference on Networking, Sensing and Control, pp 602–607

  23. Metiaf A and Wu Q (2019) Particle swarm optimization based deployment for WSN with the existence of obstacles. In: 2019 5th International Conference on Control, Automation and Robotics (ICCAR) pp 614-618. IEEE

  24. Siqueira IG, Ruiz LB, Loureiro AAF, Nogueira JM (2007) Coverage area management for wireless sensor networks. Int. J. Netw. Manag. 17(1):17–31

    Article  Google Scholar 

  25. Zou Y and Chakrabarty K (2003) Sensor deployment and target localization based on virtual forces, In: Proceedings—IEEE INFOCOM

  26. Mahboubi H, Aghdam AG, Sayrafian-Pour K (2015) Area coverage in a fixed-obstacle environment using mobile sensor networks. Control and Systems Engineering. Springer International Publishing, Cham, pp 135–151. https://doi.org/10.1007/978-3-319-14636-2_7

    Chapter  Google Scholar 

  27. Rout M, Roy R (2017) Optimal wireless sensor network information coverage using particle swarm optimisation method. Int J Electron Lett 5(4):491–499

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Patna, Bihar, India

    Rahul Priyadarshi & Bharat Gupta

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  1. Rahul Priyadarshi
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  2. Bharat Gupta
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Correspondence to Rahul Priyadarshi.

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The authors Rahul Priyadarshi and Bharat Gupta declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Priyadarshi, R., Gupta, B. 2-D coverage optimization in obstacle-based FOI in WSN using modified PSO. J Supercomput 79, 4847–4869 (2023). https://doi.org/10.1007/s11227-022-04832-6

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  • DOI: https://doi.org/10.1007/s11227-022-04832-6

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