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NSGA-II with ENLU inspired clustering for wireless sensor networks

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Abstract

Wireless sensor networks (WSNs) have a large number of existing applications and is continuously increasing. Thus it is envisioned that WSN will become an integral part of our life in the near future. Direct propagation, chain formation, cluster creation are various techniques by which data is communicated by sensor nodes to the sink. It has been proved that Clustering is an efficient and scalable method to utilize the energy of sensor nodes efficiently. Optimal election of cluster heads is an NP (non deterministic polynomial time)-Hard problem. In our proposed work, a multi-objective optimization algorithm, non dominated sorting genetic algorithm-II based clustering in wireless sensor networks has been proposed. Energy conservation, network lifetime, coverage and load balancing are the four conflicting objective functions used. Our proposed algorithm handles all of these multiple objectives simultaneously. To reduce the computational complexity of the algorithm, efficient non-dominated level update mechanism for sorting has been used, which eliminates the need of applying non dominated sorting from scratch every time. The algorithm returns a solution set consisting of multiple non dominated solutions, wherein every solution is a best solution according to some objective function, in a single run, from which any solution can be chosen based on user preferences. According to our simulation carried on MATLAB, the proposed approach outperforms the established clustering algorithms in terms of network characteristics such as network lifetime, energy consumption and number of packets received.

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References

  1. Arampatzis, T., Lygeros, J., & Manesis, S. (2005). A survey of applications of wireless sensors and wireless sensor networks. In Proceedings of the 2005 IEEE international symposium on intelligent control, Mediterrean conference on control and automation. IEEE.

  2. Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Wireless Communications, 11(6), 6–28.

    Article  Google Scholar 

  3. Puccinelli, D., & Haenggi, M. (2005). Wireless sensor networks: Applications and challenges of ubiquitous sensing. IEEE Circuits and Systems Magazine, 5(3), 19–31.

    Article  Google Scholar 

  4. Younis, M., & Akkaya, K. (2008). Strategies and techniques for node placement in wireless sensor networks: A survey. Ad Hoc Networks, 6(4), 621–655.

    Article  Google Scholar 

  5. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Article  Google Scholar 

  6. 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. IEEE.

  7. Tripathi, R. K., Singh, Y. N., & Verma, N. K. (2012). N-LEACH, a balanced cost cluster-heads selection algorithm for Wireless Sensor Network, 2012. In National conference on communications (NCC) (pp. 1–5). Kharagpur.

  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. Aju, O. G. (2015). A survey of zigbee wireless sensor network technology: Topology, applications and challenges. International Journal of Computer Applications, 130(9), 47–55.

    Article  Google Scholar 

  10. Gaddour, O., et al. (2010). Z-Cast: A multicast routing mechanism in ZigBee cluster-tree wireless sensor networks. In 2010 IEEE 30th international conference on distributed computing systems workshops. IEEE

  11. Koubâa, A., et al. (2008). TDBS: a time division beacon scheduling mechanism for ZigBee cluster-tree wireless sensor networks. Real-Time Systems, 40(3), 321–354.

    Article  MATH  Google Scholar 

  12. Tsai, C.-H., & Tseng, Y.-C. (2012). A path-connected-cluster wireless sensor network and its formation, addressing, and routing protocols. IEEE Sensors Journal, 12(6), 2135–2144.

    Article  Google Scholar 

  13. Menzel, K., et al. (2008). Towards a wireless sensor platform for energy efficient building operation. Tsinghua Science and Technology, 13(S1), 381–386.

    Article  Google Scholar 

  14. Pei, Z., et al. (2008). Application-oriented wireless sensor network communication protocols and hardware platforms: A survey. In 2008 IEEE international conference on industrial technology. IEEE.

  15. Mainetti, L., Patrono, L., & Vilei, A. (2011). Evolution of wireless sensor networks towards the internet of things: A survey. In 19th international conference on software, telecommunications and computer networks SoftCOM. IEEE.

  16. Kumar, T., Mane, P. B., & ZigBee Topology. (2016). A survey, 2016. In International conference on control, instrumentation, communication and computational technologies (ICCICCT) (pp. 164–166). Kumaracoil.

  17. Cuomo, F., et al. (2008). Topology formation in IEEE 802.15. 4: Cluster-tree characterization. In Sixth annual IEEE international conference on pervasive computing and communications (PerCom). IEEE

  18. Jaichandran, R., & Irudhayaraj, A. A. (2010). Effective strategies and optimal solutions for hot spot problem in wireless sensor networks (WSN). In 10th International conference on information science, signal processing and their applications (ISSPA 2010). IEEE

  19. Loscri, V., Morabito, G., & Marano, S. (2005). A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). In IEEE 62nd vehicular technology conference VTC-2005-Fall (vol. 3). IEEE.

  20. Zhao, L., Shaocheng, Q., & Yi, Y. (2018). A modified cluster-head selection algorithm in wireless sensor networks based on LEACH. EURASIP Journal on Wireless Communications and Networking, 2018(1), 287.

    Article  Google Scholar 

  21. Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399.

    Article  Google Scholar 

  22. Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings 15th international parallel and distributed processing symposium (IPDPS) (pp. 2009–2015). San Francisco, CA.

  23. Manjeshwar, A., & Agrawal, D. P., APTEEN. (2002). A hybrid protocol for efficient routing and comprehensive information retrieval in wireless. In Proceedings 16th international parallel and distributed processing symposium (p. 8). Ft. Lauderdale, FL.

  24. Gunjan, M., P., & Sharma, A. K. (2018). Modified TEEN for handling inconsistent cluster size problem in WSN, In 2018 international conference on wireless communications, signal processing and networking (WiSPNET), Chennai (pp. 1–6).

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

    Article  Google Scholar 

  26. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In IEEE Aerospace conference proceedings (vol. 3). IEEE.

  27. Li, C., et al. (2011). A survey on routing protocols for large-scale wireless sensor networks. Sensors, 11(4), 3498–3526.

    Article  Google Scholar 

  28. Geetha, V. A., Kallapur, P. V., & Tellajeera, S. (2012). Clustering in wireless sensor networks: Performance comparison of LEACH and LEACH-C protocols using NS2. Procedia Technology, 4, 163–170.

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Ferentinos, K. P., & Tsiligiridis, T. A. (2007). Adaptive design optimization of wireless sensor networks using genetic algorithms. Computer Networks, 51(4), 1031–1051.

    Article  MATH  Google Scholar 

  31. Hu, X.-M., et al. (2010). Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks. IEEE Transactions on Evolutionary Computation, 14(5), 766–781.

    Article  Google Scholar 

  32. Yoon, Y., & Kim, Y.-H. (2013). An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Transactions on Cybernetics, 43(5), 1473–1483.

    Article  Google Scholar 

  33. Gupta, S. K., Kuila, P., & Jana, P. K. (2013). GAR: an energy efficient GA-based routing for wireless sensor networks. In International conference on distributed computing and internet technology. Berlin: Springer.

  34. Rathee, M., & Kumar, S. (2016). Quantum inspired genetic algorithm for multi-hop energy balanced unequal clustering in wireless sensor networks. In 2016 Ninth international conference on contemporary computing (IC3). IEEE.

  35. Deb, K., et al. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  36. Li, K., et al. (2014). Efficient non-domination level update approach for steady-state evolutionary multiobjective optimization. Department of Electtrical and Computer Engineering, Michigan State University, East Lansing, USA, Technical Report COIN Report 2014014.

  37. Hu, S., & Li, G. (2018). Fault-tolerant clustering topology evolution mechanism of wireless sensor networks. IEEE Access, 6, 28085–28096.

    Article  Google Scholar 

  38. Mehmood, A., et al. (2015). Improvement of the wireless sensor network lifetime using LEACH with vice-cluster head. Ad Hoc & Sensor Wireless Networks, 28(1–2), 1–17.

    Google Scholar 

  39. Gawade, R. D., & Nalbalwar, S. L. (2016). A centralized energy efficient distance based routing protocol for wireless sensor networks. Journal of Sensors, 2016, 8313986.

    Article  Google Scholar 

  40. Guravaiah, K., & Velusamy, R. L. (2018). BEACH: Balanced energy and adaptive cluster head selection algorithm for wireless sensor networks. Adhoc & Sensor Wireless Networks, 42(3–4), 199–225.

    Google Scholar 

  41. Mittal, N., Singh, U., & Sohi, B. S. (2017). A stable energy efficient clustering protocol for wireless sensor networks. Wireless Networks, 23(6), 1809–1821.

    Article  Google Scholar 

  42. Sindjoung, M. L. F., et al. (2018). ISCP: An instantaneous and secure clustering protocol for wireless sensor networks. Network Protocols & Algorithms, 10(1), 65–82.

    Article  Google Scholar 

  43. Mehmood, A., et al. (2017). Secure knowledge and cluster-based intrusion detection mechanism for smart wireless sensor networks. IEEE Access, 6, 5688–5694.

    Article  Google Scholar 

  44. Mezrag, F., Bitam, S., & Mellouk, A. (2017). Secure routing in cluster-based wireless sensor networks. In IEEE global communications conference GLOBECOM 2017–2017. IEEE.

  45. Zhou, H., et al. (2016). A security mechanism for cluster-based WSN against selective forwarding. Sensors, 16(9), 1537.

    Article  Google Scholar 

  46. Fang, W., et al. (2019). CSDA: A novel cluster-based secure data aggregation scheme for WSNs. Cluster Computing, 22(3), 5233–5244.

    Article  Google Scholar 

  47. Peiravi, A., Mashhadi, H. R., & Javadi, S. H. (2013). An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems, 26(1), 114–126.

    Article  Google Scholar 

  48. Cheng, C.-T., & Leung, H. (2012). A multi-objective optimization framework for cluster-based wireless sensor networks. In 2012 International conference on cyber-enabled distributed computing and knowledge discovery (CyberC). IEEE.

  49. Ozdemir, S., Bara’a, A. A., & Khalil, Ö. A. (2013). Multi-objective evolutionary algorithm based on decomposition for energy efficient coverage in wireless sensor networks. Wireless Personal Communications, 71(1), 195–215.

    Article  Google Scholar 

  50. Iqbal, M., et al. (2015). Wireless sensor network optimization: multi-objective paradigm. Sensors, 15(7), 17572–17620.

    Article  Google Scholar 

  51. Sert, S. A., Bagci, H., & Yazici, A. (2015). MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 30, 151–165.

    Article  Google Scholar 

  52. Hacioglu, G., Kand, V. F. A., & Sesli, E. (2016). Multi objective clustering for wireless sensor networks. Expert Systems with Applications, 59, 86–100.

    Article  Google Scholar 

  53. Jameii, S. M., Faez, K., & Dehghan, M. (2016). AMOF: Adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks. Telecommunication Systems, 61(3), 515–530.

    Article  Google Scholar 

  54. Mazumdar, N., & Om, H. (2017). A distributed fault-tolerant multi-objective clustering algorithm for wireless sensor networks. In Proceedings of the international conference on nano-electronics, circuits communication systems. Singapore: Springer.

  55. Gamwarige, S., & Kulasekere, C. (2006). Application of the EDCR algorithm in a cluster based multi-hop wireless sensor network. In 2006 International symposium on communications and information technologies. IEEE.

  56. Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4(2), 65–85.

    Article  Google Scholar 

  57. Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for hierarchical wireless sensor networks. JNW, 2(5), 87–97.

    Article  Google Scholar 

  58. Nguyen, T. G., So-In, C., Nguyen, N. G. (2014). Two energy-efficient cluster head selection techniques based on distance for wireless sensor networks. In 2014 International computer science and engineering conference (ICSEC). IEEE.

  59. Xie, W.-X., et al. (2015). A clustering routing protocol for WSN based on type-2 fuzzy logic and ant colony optimization. Wireless Personal Communications, 84(2), 1165–1196.

    Article  Google Scholar 

  60. Barati, H., Movaghar, A., & Rahmani, A. M. (2015). EACHP: Energy aware clustering hierarchy protocol for large scale wireless sensor networks. Wireless Personal Communications, 85(3), 765–789.

    Article  Google Scholar 

  61. Halgamuge, M. N., et al. (2009). An estimation of sensor energy consumption. Progress in Electromagnetics Research, 12, 259–295.

    Article  Google Scholar 

  62. Abo-Zahhad, M., Amin, O., Farrag, M., & Ali, A. (2014). Survey on energy consumption models in wireless sensor networks. Open Transactions on Wireless Sensor Networks, 1(1).

  63. Wang, Q., Hempstead, M., & Yang, W. (2006). A realistic power consumption model for wireless sensor network devices. In 2006 3rd annual IEEE communications society on sensor and ad hoc communications and networks (SECON) (Vol. 1, pp. 286–295). Reston, VA: IEEE.

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Authors and Affiliations

  1. Computer Science and Engineering, National Institute of Technology, Delhi, India

    Gunjan & Karan Verma

  2. I.K.Gujral Punjab Technical University, Kapurthala, India

    Ajay K Sharma

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  1. Gunjan
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Gunjan, Sharma, A.K. & Verma, K. NSGA-II with ENLU inspired clustering for wireless sensor networks. Wireless Netw 26, 3637–3655 (2020). https://doi.org/10.1007/s11276-020-02281-8

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