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Analysis of Existing Clustering Algorithms for Wireless Sensor Networks

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System Performance and Management Analytics

Part of the book series: Asset Analytics ((ASAN))

Abstract

With the recent advancement in MEMS technology, researchers in academics as well as in industry are showing their immense interest in Wireless Sensor Networks (WSNs) since the past decade. WSNs are the networks composed of uniformly or randomly distributed autonomous low-cost nodes used for reliable monitoring of environmental parameters. These resource-constrained sensor nodes work in a synergetic manner to perform a sensing process. Wireless Sensor Networks have a significant role in different areas like habitat monitoring, health monitoring, intelligent and adaptive traffic management, military surveillance, target tracking, aircraft control, forest fire detection, air pollution monitoring, etc. These networks face some critical energy challenges while doing data aggregation, node deployment, localization, and clustering. This chapter presents the analysis of different clustering algorithms proposed so far to lengthen the network lifetime and to increase the network scalability.

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References

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

    Article  Google Scholar 

  2. Kumar, S. N. (2014). A new approach for traffic management in wireless multimedia sensor network. International Transaction of Electrical and Computer Engineers System, 2(5), 128–134.

    Google Scholar 

  3. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2000). Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000 (Vol. 2, pp. 3005–3014).

    Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. Khediri, S. E., Nasri, N., Wei, A., & Kachouri, A. (2014). A new approach for clustering in wireless sensors networks based on LEACH international workshop on wireless networks and energy saving techniques (WNTEST). Procedia Computer Science, 32(2014), 1180–1185.

    Article  Google Scholar 

  6. Bandyopadhyay, S., & Coyle, E. J. (2003, April). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies (Vol. 3, pp. 1713–1723). IEEE.

    Google Scholar 

  7. 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 

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

    Google Scholar 

  9. Khanna, R., Liu, H., & Chen, H. H. (2006). Self-organisation of sensor networks using genetic algorithms. International Journal of Sensor Networks, 1(3–4), 241–252.

    Article  Google Scholar 

  10. Hussain, S., Matin, A. W., & Islam, O. (2007, April). Genetic algorithm for energy efficient clusters in wireless sensor networks. In ITNG ‘07. Fourth International Conference on information Technology, 2007 (pp. 147–154). IEEE.

    Google Scholar 

  11. Heidari, E., & Movaghar, A. (2011, March). An efficient method based on genetic algorithms to solve sensor network optimization problem. International Journal on Applications of Graph Theory in Wireless Ad Hoc Networks and Sensor Networks (GRAPH-HOC), 3(1).

    Google Scholar 

  12. Bayraklı, S., & Erdogan, S. Z. (2012). Genetic algorithm based energy efficient clusters (gabeec) in wireless sensor networks. Procedia Computer Science, 10, 247–254, Conference on Ambient Systems, Networks and Technologies (ANT).

    Google Scholar 

  13. Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56.

    Article  Google Scholar 

  14. Barekatain, B., Dehghani, S., & Pourzaferani, M. (2015). An energy-aware routing protocol for wireless sensor networks based on new combination of genetic algorithm & k-means. Procedia Computer Science, 72, 552–560.

    Article  Google Scholar 

  15. Latiff, N. A., Tsimenidis, C. C., & Sharif, B. S. (2007, September). Energy-aware clustering for wireless sensor networks using particle swarm optimization. In PIMRC 2007. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, 2007 (pp. 1–5). IEEE.

    Google Scholar 

  16. 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), 13.

    Article  Google Scholar 

  17. Azharuddin, M., & Jana, P. K. (2016). Particle swarm optimization for maximizing lifetime of wireless sensor networks. Computers & Electrical Engineering, 51, 26–42.

    Article  Google Scholar 

  18. Solaiman, B. (2016). Energy optimization in wireless sensor networks using a hybrid k-means PSO clustering algorithm. Turkish Journal of Electrical Engineering & Computer Sciences, 24(4), 2679–2695.

    Article  Google Scholar 

  19. 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 

  20. Sarma, N. V. S. N., & Gopi, M. (2014). Implementation of energy efficient clustering using firefly algorithm in wireless sensor networks. International Proceedings of Computer Science and Information Technology, 59, 1.

    Google Scholar 

  21. Nadeem, A., Shankar, T., Sharma, R. K., & Roy, S. K. (2016). An application of firefly algorithm for clustering in wireless sensor networks. In Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing (pp. 869–878). Springer India.

    Google Scholar 

  22. Sahoo, R. R., Singh, M., Sahoo, B. M., Majumder, K., Ray, S., & Sarkar, S. K. (2013). A light weight trust based secure and energy efficient clustering in wireless sensor network: Honey bee mating intelligence approach. Procedia Technology, 10, 515–523.

    Article  Google Scholar 

  23. Potthuri, S., Shankar, T., & Rajesh, A. (2016). Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing (DESA). Ain Shams Engineering Journal, 6 March 2016.

    Google Scholar 

  24. Gaur, A., & Kumar, T. (2016). Switching-differential evolution (S-DE) for cluster head election in wireless sensor network, IJARIIE-ISSN(O)-2395-4396 (Vol. 2 Issue 5).

    Google Scholar 

  25. Shokrollahi, A., & Mazloom-Nezhad Maybodi, B. (2017). An energy-efficient clustering algorithm using fuzzy C-means and genetic fuzzy system for wireless sensor network. Journal of Circuits, Systems and Computers, 26(01), 1750004.

    Article  Google Scholar 

  26. Zhang, J., Lin, Y., Zhou, C., & Ouyang, J. (2008, December). Optimal model for energy-efficient clustering in wireless sensor networks using global simulated annealing genetic algorithm. In IITAW ‘08. International Symposium on intelligent information technology application workshops, 2008 (pp. 656–660). IEEE.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Amity University, Noida, Uttar Pradesh, India

    Richa Sharma, Vasudha Vashisht & Sushil Kumar

  2. Middle East College, Muscat, United Arab Emirates

    Ajay Vikram Singh

Authors
  1. Richa Sharma
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  2. Vasudha Vashisht
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  3. Ajay Vikram Singh
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  4. Sushil Kumar
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Corresponding author

Correspondence to Richa Sharma .

Editor information

Editors and Affiliations

  1. Center for Interdisciplinary Research, Amity University, New Delhi, India

    P. K. Kapur

  2. Department of Economics and Management in Machine Building, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia

    Yury Klochkov

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Ajit Kumar Verma

  4. Amity University, Noida, Uttar Pradesh, India

    Gurinder Singh

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Sharma, R., Vashisht, V., Singh, A.V., Kumar, S. (2019). Analysis of Existing Clustering Algorithms for Wireless Sensor Networks. In: Kapur, P., Klochkov, Y., Verma, A., Singh, G. (eds) System Performance and Management Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7323-6_22

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