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A PSO Based Routing with Novel Fitness Function for Improving Lifetime of WSNs

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Abstract

Wireless sensor networks (WSNs) consist of spatially distributed low power sensor nodes and gateways along with base station to monitor physical or environmental conditions. In cluster-based WSNs, the cluster head is treated as the gateway. The gateways perform the multiple activities, such as data gathering, aggregation, and transmission etc. The collected data is transmitted from gateways to the base station using routing information. Routing is a key challenge in WSNs design as gateways are constrained by energy, processing power, and memory. Moreover, heavily loaded gateways die in early stages and cause changes in network topology. It is necessary to conserve gateways energy for prolonging the WSNs lifetime. To address this problem, particle swarm optimization (PSO)-based routing is proposed in this paper. Also, a novel fitness function is designed by considering the number of relay nodes, the distance between the gateway to base station and relay load factor of the network. The proposed algorithm is validated under two different scenarios. The experimental results show that the proposed PSO-based routing algorithm prolonged WSNs lifetime when compared to other bio-inspired approaches.

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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(8), 102–114.

    Article  Google Scholar 

  2. Amirthalingam, K., et al. (2016). Improved leach: A modified leach for wireless sensor network. In IEEE international conference on advances in computer applications (ICACA) (pp. 255–258). IEEE (2016).

  3. Ammari, H. M., & Das, S. K. (2008). A trade-off between energy and delay in data dissemination for wireless sensor networks using transmission range slicing. Computer Communications, 31(9), 1687–1704.

    Article  Google Scholar 

  4. Bari, A., Wazed, S., Jaekel, A., & Bandyopadhyay, S. (2009). A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks. Ad Hoc Networks, 7(4), 665–676.

    Article  Google Scholar 

  5. Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science. MHS’95 (pp. 39–43). IEEE.

  6. Gupta, G., & Younis, M. (2003). Load-balanced clustering of wireless sensor networks. In IEEE international conference on communications. ICC’03 (Vol. 3, pp. 1848–1852). IEEE.

  7. Heinzelman, W. B. (2000). Application-specific protocol architectures for wireless networks. Ph.D. thesis, Massachusetts Institute of Technology.

  8. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  9. Kaur, R., & Singh, K. P. (2015). An efficient multipath dynamic routing protocol for mobile wsns. Procedia Computer Science, 46, 1032–1040.

    Article  Google Scholar 

  10. Kuila, P., & Jana, P. K. (2011). Improved load balanced clustering algorithm for wireless sensor networks. In International conference on advanced computing, networking and security (pp. 399–404). Springer.

  11. Kuila, P., & Jana, P. K. (2012). Energy efficient load-balanced clustering algorithm for wireless sensor networks. Procedia Technology, 6, 771–777.

    Article  Google Scholar 

  12. Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.

    Article  Google Scholar 

  13. Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.

    Article  Google Scholar 

  14. Kumar, N., & Kaur, J. (2011). Improved leach protocol for wireless sensor networks. In 2011 7th international conference on wireless communications, networking and mobile computing (WiCOM) (pp. 1–5). IEEE.

  15. Lai, C. C., Ting, C. K., & Ko, R. S. (2007). An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications. In IEEE congress on evolutionary computation. CEC 2007 (pp. 3531–3538). IEEE.

  16. Lim, W. H., & Isa, N. A. M. (2014). Particle swarm optimization with increasing topology connectivity. Engineering Applications of Artificial Intelligence, 27, 80–102.

    Article  Google Scholar 

  17. Low, C. P., Fang, C., Ng, J. M., & Ang, Y. H. (2008). Efficient load-balanced clustering algorithms for wireless sensor networks. Computer Communications, 31(4), 750–759.

    Article  Google Scholar 

  18. Mann, P. S., Singh, S., & Kumar, A. (2016). Computational intelligence based metaheuristic for energy-efficient routing in wireless sensor networks. In 2016 IEEE congress on evolutionary computation (CEC) (pp. 4460–4467). IEEE.

  19. Tang, J., Hao, B., & Sen, A. (2006). Relay node placement in large scale wireless sensor networks. Computer Communications, 29(4), 490–501.

    Article  Google Scholar 

  20. Yu, S., Wang, R., Xu, H., Wan, W., Gao, Y., & Jin, Y. (2011). WSN nodes deployment based on artificial fish school algorithm for traffic monitoring system. In IET international conference on smart and sustainable city. ICSSC 2011, Shanghai, China (pp. 201–205).

  21. Zhao, H., Zhang, Q., Zhang, L., & Wang, Y. (2015). A novel sensor deployment approach using fruit fly optimization algorithm in wireless sensor networks. In 2015 IEEE Trustcom/BigDataSE/ISPA (Vol. 1, pp. 1292–1297). IEEE.

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Correspondence to Damodar Reddy Edla.

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Edla, D.R., Kongara, M.C. & Cheruku, R. A PSO Based Routing with Novel Fitness Function for Improving Lifetime of WSNs. Wireless Pers Commun 104, 73–89 (2019). https://doi.org/10.1007/s11277-018-6009-6

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