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A Multi-Objective Optimization for Remote Monitoring Cost Minimization in Wireless Sensor Networks

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Abstract

The network based on wireless sensors are said to as Wireless sensor networks (WSNs) and these networks are a group of inexpensive and smaller battery-powered nodes, mainly used to monitor and gather data from various environment remotely. Wireless links available on these types of networks are mostly wouldn't be reliable, focus to build the routing trees to make gateway to the sink and transmit the gathered data for providing the required throughput. The most of the existing works are focused to enhance the lifetime of wireless network. The work presented here focused to reduce the service cost of remote monitoring system while transmitting the collected data. Reducing the service cost while maintaining the required throughput, the proposed multi objective Adam hybridized flower pollination optimization needs to identify a best path to transmit data to sink which satisfies all the objectives. It can easily optimize the service cost and maintain the required throughput. The multi objective considered here is throughput, service cost and energy consumption. Finally, the proposed work is evaluated and compared with two existing approaches, modLEACH and FACER.

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References

  1. Musa, A., Gonzalez, V., & Barragan, D. (2018). A new strategy to optimize the sensors placement in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 10, 1389–1399.

    Article  Google Scholar 

  2. ZainEldin, H., Badawy, M., Elhosseini, M., Arafat, H., & Abraham, A. (2020). 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, 11, 4177–4194.

    Article  Google Scholar 

  3. Zhao, M., Gong, D., & Yang, Y. (2015). Network cost minimization for mobile data gathering in wireless sensor networks. IEEE Transactions on Communications, 63(11), 4418–4432.

    Article  Google Scholar 

  4. Mehta, D., & Saxena, S. (2020). MCH-EOR: Multi-objective cluster head based energy-aware optimized routing algorithm in wireless sensor networks. Sustainable Computing: Informatics and Systems, 28, 100406.

    Google Scholar 

  5. Mahdi, O. A., Wahab, A. W. A., Idris, M. Y. I., Znaid, A. A., Al-Mayouf, Y. R. B., & Khan, S. (2016). WDARS: A weighted data aggregation routing strategy with minimum link cost in event-driven WSNs. Journal of Sensors, 2016, 1–12.

    Article  Google Scholar 

  6. Hodon, M., Chovanec, M., Cechovic, L., Hudik, M., Milanova, J., Kochlan, M., Jurecka, M., Kapitulik, J., & Sevcik, P. (2015). Maximizing performance of low-power WSN node on the basis of event-driven-programming approach: Minimization of operational energy costs of WSN node control unit. IEEE Symposium on Computers and Communication (ISCC), 2015, 204–209.

    Google Scholar 

  7. Gong, P., Chen, T. M., & Xu, Q. (2015). ETARP: An energy efficient trust-aware routing protocol for wireless sensor networks. Journal of Sensors, 2015, 1–10.

    Article  Google Scholar 

  8. Zhang, Y., He, S., & Chen, J. (2016). Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks. IEEE/ACM Transactions on Networking, 24(3), 1632–1646.

    Article  Google Scholar 

  9. Liu, X., Cao, J., Song, W.-Z., Guo, P., & He, Z. (2015). Distributed sensing for high-quality structural health monitoring using WSNs. IEEE Transactions on Parallel and Distributed Systems, 26(3), 738–747.

    Article  Google Scholar 

  10. Shaikh, F. K., & Zeadally, S. (2016). Energy harvesting in wireless sensor networks: A comprehensive review. Renewable and Sustainable Energy Reviews, 55, 1041–1054.

    Article  Google Scholar 

  11. Zhu, C., Wang, H., Liu, X., Shu, L., Yang, L. T., & Leung, V. C. M. (2016). A novel sensory data processing framework to integrate sensor networks with mobile cloud. IEEE Systems Journal, 10(3), 1125–1136.

    Article  Google Scholar 

  12. Prathima, E. G., Prakash, T. S., Venugopal, K. R., Iyengar, S. S., & Patnaik, L. M. (2016). SDAMQ: Secure data aggregation for multiple queries in wireless sensor networks. Procedia Computer Science, 89, 283–292.

    Article  Google Scholar 

  13. Faheem, M., & Gungor, V. C. (2018). Energy efficient and QoS-aware routing protocol for wireless sensor network-based smart grid applications in the context of industry 4.0. Applied Soft Computing, 68, 910–922.

    Article  Google Scholar 

  14. Zhu, J., Lung, C.-H., & Srivastava, V. (2015). A hybrid clustering technique using quantitative and qualitative data for wireless sensor networks. Ad Hoc Networks, 25, 38–53.

    Article  Google Scholar 

  15. Xie, K., Ning, X., Wang, X., He, S., Ning, Z., Liu, X., Wen, J., & Qin, Z. (2017). An efficient privacy-preserving compressive data gathering scheme in WSNs. Information Sciences, 390, 82–94.

    Article  Google Scholar 

  16. Ahmed, A., Bakar, K. A., Channa, M. I., Haseeb, K., & Khan, A. W. (2015). A trust aware routing protocol for energy constrained wireless sensor network. Telecommunication Systems, 61(1), 123–140.

    Article  Google Scholar 

  17. Raychaudhuri, A., & De, D. (2020). Bio-inspired algorithm for multi-objective optimization in wireless sensor network. Nature inspired computing for wireless sensor networks (pp. 279–301). Singapore: Springer.

    Chapter  Google Scholar 

  18. Kanwar, V., & Kumar, A. (2020). Range free localization for three dimensional wireless sensor networks using multi objective particle swarm optimization. Wireless Personal Communications, 117, 901–921.

    Article  Google Scholar 

  19. Malakar, M. (2020). TLBO based cluster-head selection for multi-objective optimization in wireless sensor networks. Nature inspired computing for wireless sensor networks (pp. 303–319). Singapore: Springer.

    Chapter  Google Scholar 

  20. Thekkil, T. M., & Prabakaran, N. (2021). Optimization based multi-objective weighted clustering for remote monitoring system in WSN. Wireless Personal Communications, 117, 387–404.

    Article  Google Scholar 

  21. Alia, O. M., & Al-Ajouri, A. (2017). Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm. IEEE Sensors Journal, 17(3), 882–896.

    Article  Google Scholar 

  22. Huang, Y., Yu, W., Osewold, C., & Garcia-Ortiz, A. (2016). Analysis of PKF: A communication cost reduction scheme for wireless sensor networks. IEEE Transactions on Wireless Communications, 15(2), 843–856.

    Article  Google Scholar 

  23. Kuo, T.-W., & Tsai, M.-J. (2012). On the construction of data aggregation tree with minimum energy cost in wireless sensor networks: NP-completeness and approximation algorithms. Proceedings IEEE INFOCOM, 2012, 2591–2595.

    Google Scholar 

  24. Jin, X., Xia, C., Xu, H., Wang, J., & Zeng, P. (2016). Mixed criticality scheduling for industrial wireless sensor networks. Sensors, 16(9), 1376.

    Article  Google Scholar 

  25. Benatia, M. A., Sahnoun, M., Baudry, D., Louis, A., El-Hami, A., & Mazari, B. (2017). Multi-objective WSN deployment using genetic algorithms under cost, coverage, and connectivity constraints. Wireless Personal Communications, 94(4), 2739–2768.

    Article  Google Scholar 

  26. Xu, X., Liang, W., Jia, X., & Xu, W. (2015). Network throughput maximization in unreliable wireless sensor networks with minimal remote data transfer cost. Wireless Communications and Mobile Computing, 16(10), 1176–1191.

    Article  Google Scholar 

  27. Liu, Y., Ota, K., Zhang, K., Ma, M., Xiong, N., Liu, A., & Long, J. (2018). QTSAC: An energy-efficient mac protocol for delay minimization in wireless sensor networks. IEEE Access, 6, 8273–8291.

    Article  Google Scholar 

  28. Yang, X.-S. (2012). Flower pollination algorithm for global optimization, lecture notes in computer science (pp. 240–249). Berlin: Springer.

    Google Scholar 

  29. Ku-Mahamud, K.R. (2015). Hybrid ant colony system and flower pollination algorithms for global optimization. In 2015 9th international conference on IT in Asia (CITA), IEEE, 1–9.

  30. Chen, P., Zhang, Y., Dai, W. (2018). LEACH protocol based on clustering and multi-leader selecting in wireless sensor network. In 2018 37th Chinese control conference (CCC), pp 7298–7303.

  31. Nanda, A., Kumar, A. (2018). Fuzzy A-star based cost effective routing (FACER) in WSNs, Advances in Intelligent Syst Rath, Tems and Computing, pp 557–563.

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

    Tibin Mathew Thekkil

  2. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram Guntur, Andhra Pradesh, India

    N. Prabakaran

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  1. Tibin Mathew Thekkil
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  2. N. Prabakaran
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Correspondence to Tibin Mathew Thekkil.

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Tibin Mathew Thekkil & Dr. N. Prabakaran have declare that there is no conflict of interest.

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Thekkil, T.M., Prabakaran, N. A Multi-Objective Optimization for Remote Monitoring Cost Minimization in Wireless Sensor Networks. Wireless Pers Commun 121, 1049–1065 (2021). https://doi.org/10.1007/s11277-021-08671-1

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