A Multi-Objective Particle Swarm Optimization for Wireless Sensor Network Deployment

  • Authors

    • Mohammed s. Ibrahem
    • mohd zakree ahmad nazri
    • zalinda othman
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.36.23734
  • Genetic Algorithm, Multi-Objective, Pareto-based, Particle Swarm Optimization, Region of Interest, Wireless Sensor Network.

  • The use of wireless sensor networks nowadays is imperative for different domain of interests. One of the challenging task in deploying such networks lies on the efficient deployment that guarantees least number of sensors while assuring the connectivity and the coverage among these sensors. This would significantly contribute toward longer lifetime of the network. Several studies have addressed this problem by proposing various meta-heuristic approaches. One of these approaches is the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) which has been extensively used for WSN deployment. However, such approach suffers of the inaccurate fitness values provided for criteria in the same front. Therefore, this paper aims to propose an alternative approach which is called Multi-Objective Particle Swarm Optimization (MOPSO). The proposed method has been compared against the NSGA-II and the results showed that the proposed method has superior performance.

     

     

     

  • References

    1. [1] I. Khan, F. Belqasmi, R. Glitho, N. Crespi, M. Morrow, and P. Polakos, "Wireless sensor network virtualization: A survey," IEEE Communications Surveys & Tutorials, vol. 18, pp. 553-576, 2016.

      [2] E. Fadel, V. C. Gungor, L. Nassef, N. Akkari, M. A. Malik, S. Almasri, and I. F. Akyildiz, "A survey on wireless sensor networks for smart grid," Computer Communications, vol. 71, pp. 22-33, 2015.

      [3] W. Li, F. C. Delicato, P. F. Pires, Y. C. Lee, A. Y. Zomaya, C. Miceli, and L. Pirmez, "Efficient allocation of resources in multiple heterogeneous Wireless Sensor Networks," Journal of Parallel and Distributed Computing, vol. 74, pp. 1775-1788, 2014.

      [4] T. Rault, A. Bouabdallah, and Y. Challal, "Energy efficiency in wireless sensor networks: A top-down survey," Computer Networks, vol. 67, pp. 104-122, 2014.

      [5] P. Kuila and P. K. Jana, "Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach," Engineering Applications of Artificial Intelligence, vol. 33, pp. 127-140, 2014.

      [6] M. Khalesian and M. R. Delavar, "Wireless sensors deployment optimization using a constrained Pareto-based multi-objective evolutionary approach," Engineering Applications of Artificial Intelligence, vol. 53, pp. 126-139, 2016/08/01/ 2016.doi:https://doi.org/10.1016/j.engappai.2016.03.004 http://www.sciencedirect.com/science/article/pii/S095219761630063X.

      [7] Mohammed,M.A, Mohd Khanapi Abd Ghani, Salama A. Mostafa and Dheyaa Ahmed Ibrahim, 2017. Using Scatter Search Algorithm in Implementing Examination Timetabling Problem. Journal of Engineering and Applied Sciences, 12: 4792-4800..

      [8] J. OuYang, F. Yang, S. W. Yang, and Z. P. Nie, "The Improved NSGA-II Approach," Journal of Electromagnetic Waves and Applications, vol. 22, pp. 163-172, 2008/01/01 2008.doi:10.1163/156939308784160703 https://doi.org/10.1163/156939308784160703.

      [9] Y. Yoon and Y. Kim, "An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks," IEEE Transactions on Cybernetics, vol. 43, pp. 1473-1483, 2013.doi:10.1109/TCYB.2013.2250955.

      [10] O. Banimelhem, M. Mowafi, and W. Aljoby, "Genetic algorithm based node deployment in hybrid wireless sensor networks," Communications and Network, vol. 5, p. 273, 2013.

      [11] Mostafa, S.A., Mustapha, A., Hazeem, A.A., Khaleefah, S.H. and Mohammed, M.A., 2018. An Agent-Based Inference Engine for Efficient and Reliable Automated Car Failure Diagnosis Assistance. IEEE Access, 6, pp.8322-8331.

      [12] Z. Lin, S. Zhang, and G. Yan, "An incremental deployment algorithm for wireless sensor networks using one or multiple autonomous agents," Ad Hoc Networks, vol. 11, pp. 355-367, 2013/01/01/ 2013.doi:https://doi.org/10.1016/j.adhoc.2012.06.009 http://www.sciencedirect.com/science/article/pii/S1570870512001175.

      [13] S. M. Jameii, K. Faez, and M. Dehghan, "Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks," International Journal of Distributed Sensor Networks, vol. 11, p. 363815, 2015.doi:10.1155/2015/363815 http://journals.sagepub.com/doi/abs/10.1155/2015/363815.

      [14] M. Abo-Zahhad, N. Sabor, S. Sasaki, and S. M. Ahmed, "A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks," Information Fusion, vol. 30, pp. 36-51, 2016/07/01/ 2016.doi:https://doi.org/10.1016/j.inffus.2015.11.005 http://www.sciencedirect.com/science/article/pii/S1566253515001037.

      [15] M. R. Senouci, A. Mellouk, N. Aitsaadi, and L. Oukhellou, "Fusion-based surveillance WSN deployment using Dempster–Shafer theory," Journal of Network and Computer Applications, vol. 64, pp. 154-166, 2016/04/01/ 2016.doi:https://doi.org/10.1016/j.jnca.2015.12.014 http://www.sciencedirect.com/science/article/pii/S1084804516000576.

      [16] Mohammed, M.A., Ghani, M.K.A., Arunkumar, N., Obaid, O.I., Mostafa, S.A., Jaber, M.M., Burhanuddin, M.A., Matar, B.M. and Ibrahim, D.A., 2018. Genetic case-based reasoning for improved mobile phone faults diagnosis. Computers & Electrical Engineering, 71, pp.212-222.

      [17] Mostafa, S.A., Mustapha, A., Mohammed, M.A., Ahmad, M.S. and Mahmoud, M.A., 2018. A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application. International journal of medical informatics, 112, pp.173-184.

      [18] Ghani, M.K.A., Mohammed, M.A., Ibrahim, M.S., Mostafa, S.A. And Ibrahim, D.A., 2017. Implementing An Efficient Expert System For Services Center Management By Fuzzy Logic Controller. Journal of Theoretical & Applied Information Technology, 95(13).

      [19] KHANAPI ABD GHANI, Mohd et al. The Design of Flexible Telemedicine Framework for Healthcare Big Data. International Journal of Engineering & Technology, v. 7, n. 3.20, p. 461-468, doi:http://dx.doi.org/10.14419/ijet.v7i3.20.20590.

      [20] Mostafa, S.A., Ahmad, M.S., Mustapha, A. and Mohammed, M.A., 2017. Formulating layered adjustable autonomy for unmanned aerial vehicles. International Journal of Intelligent Computing and Cybernetics, 10(4), pp.430-450.

      [21] Mutlag, A.A., Ghani, M.K.A., Arunkumar, N., Mohamed, M.A. and Mohd, O., 2019. Enabling technologies for fog computing in healthcare IoT systems. Future Generation Computer Systems, 90, pp.62-78.

  • Downloads

  • How to Cite

    s. Ibrahem, M., zakree ahmad nazri, mohd, & othman, zalinda. (2018). A Multi-Objective Particle Swarm Optimization for Wireless Sensor Network Deployment. International Journal of Engineering & Technology, 7(4.36), 140-146. https://doi.org/10.14419/ijet.v7i4.36.23734