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Energy Efficient Approach in Wireless Sensor Networks Using Game Theoretic Approach and Ant Colony Optimization

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Abstract

In the cluster based wireless sensor network architecture, an effective way to optimize the energy consumption is to implement an energy efficient scheme amongst the participating nodes for major activities such as construction of the hierarchical structure on the regular interval and the data communication from a node to the base station. This paper proposes an energy efficient approach for a cluster based wireless sensor network architecture by employing the game theory and ant colony optimization technique. Initially, the proposed work forms various clusters within the network and thereafter, the coalitions are formed using the proposed algorithm based on the game theory. The proposed algorithm considers the extent of spatially correlated sensed data that are generated by neighbouring nodes in order to form a coalition within a cluster. The proposed coalition scheme reduces the number of transmissions across the network. It is compared with the competing clustering protocols. The simulation results confirm that the proposed algorithm achieves the increased network lifetime under the specified quality of service specification (QSS). The results of the proposed work are compared with that obtained through the existing low energy adaptive clustering hierarchy (LEACH) and the deterministic stable election protocols (D-SEP). The overall improvement gain achieved by the proposed work is 31% and 10% at specified QSS, when compared with the LEACH and the D-SEP protocols respectively. Thus, the simulation results obtained in the proposed work confirm their superiority over the LEACH and the D-SEP protocols.

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References

  1. Giuseppe, A., Marco, C., Mario, D. F., & Andrea, P. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7(3), 537–568.

    Article  Google Scholar 

  2. 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, 2000 (10 pp). IEEE.

  3. Selvakennedy, S., Sinnappan, S., & Shang, Y. (2006). T-ANT: a nature-inspired data gathering protocol for wireless sensor networks. Journal of Communications, 1(2), 22–29.

    Article  Google Scholar 

  4. Chiasserini, C. F., Chlamtac, I., Monti, P., & Nucci, A. (2002). Energy efficient design of wireless ad hoc networks. In NETWORKING. Networking technologies, services, and protocols; Performance of computer and communication networks; Mobile and wireless communications (pp. 376–386). Berlin: Springer.

  5. Yoon, S., & Shahabi, C. (2005). Exploiting spatial correlation towards an energy efficient clustered aggregation technique (cag) [wireless sensor network applications]. In 2005 IEEE International conference on communications, 2005. ICC 2005 (Vol. 5, pp. 3307–3313). IEEE.

  6. Meka, A., & Singh, A. K. (2006). Distributed spatial clustering in sensor networks. Advances in Database Technology-EDBT 2006 (pp. 980–1000). Berlin: Springer.

    Chapter  Google Scholar 

  7. Youssef, M., Youssef, A., & Younis, M. F. (2009). Overlapping multihop clustering for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 20(12), 1844–1856.

    Article  Google Scholar 

  8. Amis, A. D., Prakash, R., Vuong, T. H., & Huynh, D. T. (2000). Max-min d-cluster formation in wireless ad hoc networks. In INFOCOM 2000. Nineteenth annual joint conference of the IEEE computer and communications societies. Proceedings. IEEE (Vol. 1, pp. 32–41). IEEE.

  9. Foss, S. G., & Zuyev, S. A. (1996). On a Voronoi aggregative process related to a bivariate Poisson process. Advances in Applied Probability, 28(4), 965–981.

    Article  MathSciNet  MATH  Google Scholar 

  10. Voulkidis, A. C., Anastasopoulos, M. P., & Cottis, P. G. (2013). Energy efficiency in wireless sensor networks: a game-theoretic approach based on coalition formation. ACM Transactions on Sensor Networks (TOSN), 9(4), 43.

    Article  Google Scholar 

  11. Schmidt, C. (Ed.). (2003). Game theory and economic analysis: A quiet revolution in economics. London: Routledge.

    Google Scholar 

  12. Osborne, M. J., & Rubinstein, A. (1994). A course in game theory. Cambridge: MIT press.

    MATH  Google Scholar 

  13. Pitchai, K. M., Paramasivan, B., & Bhuvaneswari, M. (2014). Game theoretical computation based energy efficient routing for wireless sensor networks. In 2014 3rd International conference on eco-friendly computing and communication systems (ICECCS) (pp. 99–104). IEEE.

  14. Xu, Z., Yin, Y., Chen, X., & Wang, J. (2013). A game-theory based clustering approach for wireless sensor networks. In NGCIT 2013, ASTL (pp. 58–66).

  15. Hanappi, H. (2013). The Neumann–Morgenstern project-game theory as a formal language for the social sciences. Croatia: INTECH Open Access Publisher.

    Book  Google Scholar 

  16. Apt, K. R., & Witzel, A. (2009). A generic approach to coalition formation. International Game Theory Review, 11(03), 347–367.

    Article  MathSciNet  MATH  Google Scholar 

  17. Wu, D., Cai, Y., Zhou, L., & Wang, J. (2012). A cooperative communication scheme based on coalition formation game in clustered wireless sensor networks. IEEE Transactions on Wireless Communications, 11(3), 1190–1200.

    Article  Google Scholar 

  18. Jha, V., Khetarpal, K., & Sharma, M. (2011). A survey of nature inspired routing algorithms for MANETs. In 2011 3rd international conference on electronics computer technology (ICECT) (Vol. 6, pp. 16–24). IEEE.

  19. Dorigo, M., & Birattari, M. (2010). Ant colony optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 36–39). Springer: US.

  20. Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2(4), 353–373.

    Article  Google Scholar 

  21. Dorigo, M., Birattari, M., & Sttzle, T. (2006). Ant colony optimization. Computational Intelligence Magazine, IEEE, 1(4), 28–39.

    Article  Google Scholar 

  22. Zungeru, A. M., Ang, L. M., & Seng, K. P. (2012). Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison. Journal of Network and Computer Applications, 35(5), 1508–1536.

    Article  Google Scholar 

  23. Obiniyi, A. A. (2015). Multi-agent based patient scheduling using ant colony optimization. African Journal of Computing & ICT, 8(2), 91–96.

    Google Scholar 

  24. 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 

  25. Tripathi, R. K., Singh, Y. N., & Verma, N. K. (2012). N-leach, a balanced cost cluster-heads selection algorithm for wireless sensor network. In 2012 National conference on communications (NCC) (pp. 1–5). IEEE.

  26. Khan, B. M., & Bilal, R. (2014). High quality of service and energy efficient MAC protocols for wireless sensor networks. Inter-cooperative collective intelligence: techniques and applications (pp. 315–348). Berlin: Springer.

    Chapter  Google Scholar 

  27. Lee, J. W., Choi, B. S., & Lee, J. J. (2011). Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones. IEEE Transactions on Industrial Informatics, 7(3), 419–427.

    Article  Google Scholar 

  28. Bala, M., & Awasthi, L. (2012). Proficient D-SEP protocol with heterogeneity for maximizing the lifetime of wireless sensor networks. International Journal of Intelligent Systems and Applications (IJISA), 4(7), 1.

    Article  Google Scholar 

  29. Mohanty, S. & Patra, S. K. (2010). Quality of service analysis in IEEE 802.15.4 mesh networks using MANET routing. In F. Xhafa & N. Bessis (Eds.), Second international conference on computing, communication and networking technologies, Karur (pp. 1–7).

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Authors and Affiliations

  1. National Institute of Technology, Delhi, India

    Richa Mishra, Rajeev K. Tripathi & Ajay K. Sharma

  2. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Vivekanand Jha

Authors
  1. Richa Mishra
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  2. Vivekanand Jha
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  3. Rajeev K. Tripathi
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  4. Ajay K. Sharma
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Correspondence to Richa Mishra.

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Mishra, R., Jha, V., Tripathi, R.K. et al. Energy Efficient Approach in Wireless Sensor Networks Using Game Theoretic Approach and Ant Colony Optimization. Wireless Pers Commun 95, 3333–3355 (2017). https://doi.org/10.1007/s11277-017-4000-2

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