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Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks

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Abstract

Energy conserving of sensor nodes is the most crucial issue in the design of wireless sensor networks (WSNs). In a cluster based routing approach, cluster heads (CHs) cooperate with each other to forward their data to the base station (BS) via multi-hop routing. In this process, CHs closer to the BS are burdened with heavier relay traffic and tend to die prematurely which causes network partition is popularly known as a hot spot problem. To mitigate the hot spot problem, in this paper, we propose unequal clustering and routing algorithms based on novel chemical reaction optimization (nCRO) paradigm, we jointly call these algorithms as novel CRO based unequal clustering and routing algorithms (nCRO-UCRA). In clustering, we partition the network into unequal clusters such that smaller size clusters near to the sink and larger size clusters relatively far away from the sink. For this purpose, we develop the CH selection algorithm based on nCRO paradigm and assign the non-cluster head sensor nodes to the CHs based on derived cost function. Then, a routing algorithm is presented which is also based on nCRO based approach. All these algorithms are developed with the efficient schemes of molecular structure encoding and novel potential energy functions. The nCRO-UCRA is simulated extensively on various scenarios of WSNs and varying number of sensors and the CHs. The results are compared with some existing algorithms and original CRO based algorithm called as CRO-UCRA to show the superiority in terms of various performance metrics like residual energy, network lifetime, number of alive nodes, data packets received by the BS and convergence rate.

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References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14), 2826–2841.

    Article  Google Scholar 

  3. Afsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46, 198–226.

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Liu, X. (2012). A survey on clustering routing protocols in wireless sensor networks. Sensors, 12(8), 11113–11153.

    Article  Google Scholar 

  6. Lam, A. Y. S., & Li, V. (2010). Chemical-reaction-inspired metaheuristic for optimization. IEEE Transactions on Evolutionary Computation, 14(3), 381–399.

    Article  Google Scholar 

  7. Xu, J., Lam, A., & Li, V. O. (2011). Chemical reaction optimization for task scheduling in grid computing. IEEE Transactions on Parallel and Distributed Systems, 22(10), 1624–1631.

    Article  Google Scholar 

  8. Lam, A., Li, V. O., & Yu, J. J. (2013). Power-controlled cognitive radio spectrum allocation with chemical reaction optimization. IEEE Transactions on Wireless Communications, 12(7), 3180–3190.

    Article  Google Scholar 

  9. Lam, A. Y., & Li, V. O. (2012). Chemical reaction optimization: A tutorial. Memetic Computing, 4(1), 3–17.

    Article  Google Scholar 

  10. Atkins, P., & de Paula, J. (2010). Physical chemistry (9th ed.). Oxford, UK: Oxford University Press (Part 1 and Part 3).

    Google Scholar 

  11. Oxtoby, D. W., Gill, H. P., & Campion, A. (2012). Principles of modern chemistry (7th ed.). Boston: Cengage Learning (Unit 3 and Unit 5).

    Google Scholar 

  12. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In 2000 IEEE Proceedings of the 33rd annual Hawaii international conference on system sciences (10 pp).

  13. Liu, Y., Xiong, N., Zhao, Y., Vasilakos, A. V., Gao, J., & Jia, Y. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.

    Article  Google Scholar 

  14. Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In 2011 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON) (pp. 46–54).

  15. Liu, X. Y., Zhu, Y., Kong, L., Liu, C., Gu, Y., Vasilakos, A. V., & Wu, M. Y. (2014). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Sysytems, PP(99), 1.

    Google Scholar 

  16. Xiao, Y., Peng, M., Gibson, J., Xie, G. G., Du, D. Z., & Vasilakos, A. V. (2012). Tight performance bounds of multihop fair access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554.

    Article  Google Scholar 

  17. Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, 2009, 1–9. doi:10.1155/2009/134165.

    Article  Google Scholar 

  18. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power efficient gathering in sensor information systems. In Proceedings of IEEE aerospace conference (Vol. 3, pp. 1125–1130).

  19. Younis, O., & Fahmy, S. (2004). HEED: Hybrid energy efficient distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  20. Yanjun, Y., Qing, C., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE ACM Transactions on Networks, 23(3), 810–823.

    Article  Google Scholar 

  21. Han, K., Luo, J., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.

    Article  Google Scholar 

  22. Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  23. Guo, W., Xiong, N., Vasilakos, A. V., Chen, G., & Cheng, H. (2011). Multi-source temporal data aggregation in wireless sensor networks. Wireless Personal Communications, 56(3), 359–370.

    Article  Google Scholar 

  24. Rao, P. C. S., Banka, H., & Jana, P. K. (2015). PSO-based multiple-sink placement algorithm for protracting the lifetime of wireless sensor networks. In Proceedings of the second international conference on computer and communication technologies (pp. 605–616). Springer.

  25. Li, M., Li, Z., & Vasilakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.

    Article  Google Scholar 

  26. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3), 45.

    Article  Google Scholar 

  27. Yao, Y., Cao, Q., & Vasilakos, A. V. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In 2013 IEEE 10th international conference on mobile ad-hoc and sensor systems (MASS) (pp. 182–190).

  28. Dvir, A., & Vasilakos, A. V. (2011). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 41(4), 405–406.

    Google Scholar 

  29. Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501.

    Article  Google Scholar 

  30. Yan, Z., Zhang, P., & Vasilakos, A. V. (2014). A survey on trust management for internet of things. Journal of Network and Computer Applications, 42, 120–134.

    Article  Google Scholar 

  31. Liu, T., Li, Q., & Liang, P. (2012). An energy-balancing clustering approach for gradient-based routing in wireless sensor networks. Computer Communications, 35(17), 2150–2161.

    Article  Google Scholar 

  32. Afsar, M. M., & Younis, M. (2014). An energy-and proximity-based unequal clustering algorithm for wireless sensor networks. In IEEE 39th conference on local computer networks (LCN) (pp. 262–269).

  33. 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 

  34. Lin, J., Xiong, N., Vasilakos, A. V., Chen, G., & Guo, W. (2011). Evolutionary game-based data aggregation model for wireless sensor networks. IET Communications, 5(12), 1691–1697.

    Article  MathSciNet  Google Scholar 

  35. Guo, W., Xiong, N., Vasilakos, A. V., Chen, G., & Yu, C. (2012). Distributed k–connected fault–tolerant topology control algorithms with PSO in future autonomic sensor systems. International Journal of Sensor Networks, 12(1), 53–62.

    Article  Google Scholar 

  36. Guo, W., Park, J. H., Yang, L. T., Vasilakos, A. V., Xiong, N., & Chen, G. (2011). Design and analysis of a MST-based topology control scheme with PSO for wireless sensor networks. In 2011 IEEE Asia–Pacific services computing conference (APSCC) (pp. 360–367).

  37. Sengupta, S., Das, S., Nasir, M., Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1093–1102.

    Article  Google Scholar 

  38. Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  Google Scholar 

  39. Phan, D. H., Suzuki, J., Omura, S., Oba, K., & Vasilakos, A. (2014). Multiobjective communication optimization for cloud-integrated body sensor networks. In 2014 14th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid) (pp. 685–693).

  40. Latiff, N. M. A., Tsemenidis, C. C., & Sheriff, B. S. (2007). Energy-aware clustering for wireless sensor networks using particle swarm optimization. In Proceedings of 18th annual IEEE international symposium on personal, indoor and mobile radio communications (pp. 1–5).

  41. Singh, B., & Lobiyal, D. K. (2012). A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-centric Computing and Information Sciences, 2(1), 2–13.

    Article  Google Scholar 

  42. 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 

  43. Jiang, C. J., Shi, W. R., & Tang, X. L. (2010). Energy-balanced unequal clustering protocol for wireless sensor networks. The Journal of China Universities of Posts and Telecommunications, 17(4), 94–99.

    Article  Google Scholar 

  44. Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749.

    Article  Google Scholar 

  45. Logambigai, R., & Kannan, A. (2015). Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Networks. doi:10.1007/s11276-015-1013-1.

    Google Scholar 

  46. Xu, J., Liu, W., Lang, F., Zhang, Y., & Wang, C. (2010). Distance measurement model based on RSSI in WSN. Wireless Sensor Networks, 2(8), 606–611.

    Article  Google Scholar 

  47. Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks, 5(1), 1–38.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian School of Mines, Dhanbad, 826004, India

    P. C. Srinivasa Rao & Haider Banka

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  1. P. C. Srinivasa Rao
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Correspondence to P. C. Srinivasa Rao.

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Srinivasa Rao, P.C., Banka, H. Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks. Wireless Netw 23, 759–778 (2017). https://doi.org/10.1007/s11276-015-1148-0

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