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EEPMS: Energy Efficient Path Planning for Mobile Sink in Wireless Sensor Networks: A Genetic Algorithm-Based Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1086))

Abstract

The area of wireless sensor networks (WSNs)  has been highly explored due to its vast application in various domains. The main constraint of WSN is the energy of the sensor nodes. The use of mobile sink (MS) is one of the prominent methods to preserve the energy of sensor nodes. Moreover, use of mobile sink is also solving the hot-spot problem of wireless sensor network. In the paper, we propose a genetic algorithm-based approach to plan the path for mobile sink. All the basic intermediate operations of genetic algorithms, i.e., chromosome representation, crossover and mutation are well explained with suitable examples. The proposed algorithm is shown its efficacy over the randomly generated path.

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References

  1. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)

    Google Scholar 

  2. G. Anastasi, M. Conti, M. Di Francesco, A. Passarella, Energy conservation in wireless sensor networks: a survey. Ad Hoc Netw. 7(3), 537–568 (2009)

    Article  Google Scholar 

  3. A. Gagarin, S. Hussain, L.T. Yang, Distributed hierarchical search for balanced energy consumption routing spanning trees in wireless sensor networks. J. Parallel Distrib. Comput. 70(9), 975–982 (2010)

    Google Scholar 

  4. T. Arampatzis, J. Lygeros, S. Manesis, A survey of applications of wireless sensors and wireless sensor networks, in Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterranean Conference on Control and Automation (IEEE, 2005), pp. 719–724

    Google Scholar 

  5. S.K. Gupta, P. Kuila, P.K. Jana, GAR: an energy efficient GA-based routing for wireless sensor networks, in International Conference on Distributed Computing and Internet Technology (Springer, 2013), pp. 267–277

    Google Scholar 

  6. S.K. Gupta, P.K. Jana, Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wireless Pers. Commun. 83(3), 2403–2423 (2015)

    Google Scholar 

  7. K. Akkaya, M. Younis, A survey on routing protocols for wireless sensor networks. Ad Hoc Netw. 3(3), 325–349 (2005)

    Article  Google Scholar 

  8. S.K. Gupta, P. Kuila, P.K. Jana, Delay constraint energy efficient routing using multi-objective genetic algorithm in wireless sensor networks, in International Conference on Eco-friendly Computing and Communication Systems, TMH (2013), pp. 50–59

    Google Scholar 

  9. S.K. Gupta, P. Kuila, P.K. Jana, GA based energy efficient and balanced routing in k-connected wireless sensor networks, in Proceedings of the First International Conference on Intelligent Computing and Communication (Springer, 2017), pp. 679–686

    Google Scholar 

  10. R.N. Shukla, A.S. Chandel, S.K. Gupta, J. Jain, A. Bhansali, GAE\(^3\)BR: genetic algorithm based energy efficient and energy balanced routing algorithm for wireless sensor networks, in 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (IEEE, 2015), pp. 942–947

    Google Scholar 

  11. S.K. Gupta, P. Kuila, P.K. Jana, E\(^3\)BFT: energy efficient and energy balanced fault tolerance clustering in wireless sensor networks, in 2014 International Conference on Contemporary Computing and Informatics (IC3I) (IEEE, 2014), pp. 714–719

    Google Scholar 

  12. Y.S. Yun, Ye Xia, Maximizing the lifetime of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Trans. Mob. Comput. 9(9), 1308–1318 (2010)

    Article  Google Scholar 

  13. M.I. Khan, W.N. Gansterer, G. Haring, Static vs. mobile sink: the influence of basic parameters on energy efficiency in wireless sensor networks. Comput. Commun. 36(9), 965–978 (2013)

    Google Scholar 

  14. I. Papadimitriou, L. Georgiadis, Energy-aware routing to maximize lifetime in wireless sensor networks with mobile sink. J. Commun. Softw. Syst. (2006)

    Google Scholar 

  15. G. Xing, T. Wang, Z. Xie, W. Jia, Rendezvous planning in wireless sensor networks with mobile elements. IEEE Trans. Mob. Comput. 7(12), 1430–1443 (2008)

    Article  Google Scholar 

  16. D.J. Jezewski, J.P. Brazzel Jr., E.E. Prust, B.G. Brown, T.A. Mulder, D.B. Wissinger, A survey of rendezvous trajectory planning. Astrodynamics 1991, 1373–1396 (1992)

    Google Scholar 

  17. T. Camp, J. Boleng, V. Davies, A survey of mobility models for ad hoc network research. Wireless Commun. Mob. Comput. 2(5), 483–502 (2002)

    Article  Google Scholar 

  18. G. Yu, Y. Ji, J. Li, F. Ren, Baohua Zhao, EMS: efficient mobile sink scheduling in wireless sensor networks. Ad Hoc Netw. 11(5), 1556–1570 (2013)

    Article  Google Scholar 

  19. T.-S. Chen, H.-W. Tsai, Y.-H. Chang, T.-C. Chen, Geographic convergecast using mobile sink in wireless sensor networks. Comput. Commun. 36(4), 445–458 (2013)

    Article  Google Scholar 

  20. S. Ghafoor, M.H. Rehmani, S. Cho, S.-H. Park, An efficient trajectory design for mobile sink in a wireless sensor network. Comput. Electr. Eng. 40(7), 2089–2100 (2014)

    Google Scholar 

  21. H. Salarian, K.-W. Chin, F. Naghdy, An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Trans. Veh. Technol. 63(5), 2407–2419 (2014)

    Article  Google Scholar 

  22. P. Kuila, S.K. Gupta, P.K. Jana, A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol. Comput. 12, 48–56 (2013)

    Google Scholar 

  23. S.K. Gupta, P. Kuila, P.K. Jana, Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Comput. Electr. Eng. (2015)

    Google Scholar 

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Correspondence to Akhilesh Kumar Srivastava .

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Srivastava, A.K., Sinha, A., Mishra, R., Gupta, S.K. (2021). EEPMS: Energy Efficient Path Planning for Mobile Sink in Wireless Sensor Networks: A Genetic Algorithm-Based Approach. In: Gao, XZ., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Computational Intelligence and Communication Technology. Advances in Intelligent Systems and Computing, vol 1086. Springer, Singapore. https://doi.org/10.1007/978-981-15-1275-9_9

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