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Energy-Efficient Routing Mechanism for Mobile Sink in Wireless Sensor Networks Using Particle Swarm Optimization Algorithm

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Abstract

One of the most effective approaches to increase the lifetime of wireless sensor networks (WSNs), is the use of a mobile sink to collect data from sensor. In WSNs, mobile sinks implicitly help achieving uniform energy-consumption and provide load-balancing. In this approach, some certain points in the sensors field should be visited by the mobile sink. The optimal selection of these points which are also called rendezvous points is a NP-hard problem. Since hierarchical algorithms rely only on their local information to select these points, thus the probability of selecting an optimal node as rendezvous point will be very low. To address this problem, in this paper, a new method called particle swarm optimization based selection (PSOBS) is proposed to select the optimal rendezvous points. By applying PSO, the proposed method is capable of finding optimal or near-optimal rendezvous points to efficient management of network resources. In the proposed method, a weight value is also calculated for each sensor node based on the number of data packets that it receives from other sensor nodes. The proposed method was compared with weighted rendezvous planning based selection (WRPBS) algorithm based on some performance metrics such as throughput, energy consumption, number of rendezvous points and hop count. The simulation results show the superiority of PSOBS as compared with WRPBS, but it increases the packet loss rate in comparison with WRPBS.

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References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40, 102–114.

    Article  Google Scholar 

  2. Nikokheslat, H. D., & Ghaffari, A. (2017). Protocol for controlling congestion in wireless sensor networks. Wireless Personal Communications, 95, 3233–3251.

    Article  Google Scholar 

  3. Ghaffari, A. (2015). Congestion control mechanisms in wireless sensor networks: A survey. Journal of Network and Computer Applications, 52, 101–115.

    Article  Google Scholar 

  4. Ghaffari, A. (2014). An energy efficient routing protocol for wireless sensor networks using A-star algorithm. Journal of Applied Research and Technology, 12, 815–822.

    Article  Google Scholar 

  5. KeyKhosravi, D., Ghaffari, A., Hosseinalipour, A., & Khasragi, B. A. (2010). New clustering protocol to decrease probability failure nodes and increasing the lifetime in WSNs. International Journal of Advanced Computer Technology, 2, 117–121.

    Article  Google Scholar 

  6. Azari, L., & Ghaffari, A. (2015). Proposing a novel method based on network-coding for optimizing error recovery in wireless sensor networks. Indian Journal of Science and Technology, 8, 859–867.

    Article  Google Scholar 

  7. Ghaffari, A., & Takanloo, V. A. (2011). QoS-based routing protocol with load balancing for wireless multimedia sensor networks using genetic algorithm. World Applied Sciences Journal, 15, 1659–1666.

    Google Scholar 

  8. Salarian, H., Chin, K.-W., & Naghdy, F. (2014). An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Transactions on Vehicular Technology, 63, 2407–2419.

    Article  Google Scholar 

  9. Krishnan, A. M., & Kumar, P. G. (2016). An effective clustering approach with data aggregation using multiple mobile sinks for heterogeneous WSN. Wireless Personal Communications, 90, 423–434.

    Article  Google Scholar 

  10. Zhou, Z., Du, C., Shu, L., Hancke, G., Niu, J., & Ning, H. (2016). An energy-balanced heuristic for mobile sink scheduling in hybrid WSNs. IEEE Transactions on Industrial Informatics, 12, 28–40.

    Article  Google Scholar 

  11. Tang, J., Yang, W., Zhu, L., Wang, D., & Feng, X. (2017). An adaptive clustering approach based on minimum travel route planning for wireless sensor networks with a mobile sink. Sensors, 17, 964.

    Article  Google Scholar 

  12. Wang, J., Cao, J., Ji, S., & Park, J. H. (2017). Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks. The Journal of Supercomputing, 73, 3277–3290.

    Article  Google Scholar 

  13. Pedersen, M. E. H., & Chipperfield, A. J. (2010). Simplifying particle swarm optimization. Applied Soft Computing, 10, 618–628.

    Article  Google Scholar 

  14. Amiri, B., Fathian, M., & Maroosi, A. (2009). Application of shuffled frog-leaping algorithm on clustering. The International Journal of Advanced Manufacturing Technology, 45, 199–209.

    Article  Google Scholar 

  15. Vericat, F., Stoico, C. O., Carlevaro, C. M., & Renzi, D. G. (2011). Genetic algorithm for the pair distribution function of the electron gas. Interdisciplinary Sciences: Computational Life Sciences, 3, 283–289.

    Google Scholar 

  16. Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11, 5508–5518.

    Article  Google Scholar 

  17. Lee, E., Park, S., Yu, F., & Kim, S.-H. (2010). Communication model and protocol based on multiple static sinks for supporting mobile users in wireless sensor networks. IEEE Transactions on Consumer Electronics, 56, 1652–1660.

    Article  Google Scholar 

  18. Lee, E., Park, S., Lee, J., Oh, S., & Kim, S.-H. (2011). Novel service protocol for supporting remote and mobile users in wireless sensor networks with multiple static sinks. Wireless Networks, 17, 861–875.

    Article  Google Scholar 

  19. Vincze, Z., Vida, R., & Vidacs, A. (2007). Deploying multiple sinks in multi-hop wireless sensor networks. In IEEE international conference on pervasive services (pp. 55–63).

  20. Bagheri, T., & Ghaffari, A. (2011) Recm: Reliable and energy effective clustering based multi-path routing algorithm for wireless sensor networks. In: World Congress on Information and Communication Technologies (WICT) (pp. 1340–1345).

  21. Alizadeh, S., & Ghaffari, A. (2010). An energy-efficient hirerchical clustering protocole in wireless sensor networks. In: 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT) (pp. 413–418).

  22. Khabiri, M., & Ghaffari, A. (2018). Energy-aware clustering-based routing in wireless sensor networks using cuckoo optimization algorithm. Wireless Personal Communications, 98, 2473–2495.

    Article  Google Scholar 

  23. Gu, Y., Ren, F., Ji, Y., & Li, J. (2016). The evolution of sink mobility management in wireless sensor networks: A survey. IEEE Communications Surveys and Tutorials, 18, 507–524.

    Article  Google Scholar 

  24. Wang, J., Cao, Y., Li, B., Kim, H.-J., & Lee, S. (2017). Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Generation Computer Systems, 76, 452–457.

    Article  Google Scholar 

  25. Zhang, J., Tang, J., Wang, T., & Chen, F. (2017). Energy-efficient data-gathering rendezvous algorithms with mobile sinks for wireless sensor networks. International Journal of Sensor Networks, 23, 248–257.

    Article  Google Scholar 

  26. Chang, J.-Y., & Shen, T.-H. (2016). An efficient tree-based power saving scheme for wireless sensor networks with mobile sink. IEEE Sensors Journal, 16, 7545–7557.

    Article  Google Scholar 

  27. Kaswan, A., Nitesh, K., & Jana, P. K. (2017). Energy efficient path selection for mobile sink and data gathering in wireless sensor networks. AEU-International Journal of Electronics and Communications, 73, 110–118.

    Article  Google Scholar 

  28. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (Vol. 2, p. 10).

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Authors and Affiliations

  1. Department of Computer Engineering, Sardroud Branch, Islamic Azad University, Tabriz, Iran

    Shamineh Tabibi

  2. Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Ali Ghaffari

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  1. Shamineh Tabibi
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  2. Ali Ghaffari
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Correspondence to Ali Ghaffari.

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Tabibi, S., Ghaffari, A. Energy-Efficient Routing Mechanism for Mobile Sink in Wireless Sensor Networks Using Particle Swarm Optimization Algorithm. Wireless Pers Commun 104, 199–216 (2019). https://doi.org/10.1007/s11277-018-6015-8

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  • DOI: https://doi.org/10.1007/s11277-018-6015-8

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