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Traffic Aware Channel Access Algorithm for Cluster Based Wireless Sensor Networks

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Abstract

Partition of nodes into clusters is one of the most accepted method for achieving energy efficiency and scalability in wireless sensor networks. In this paper, we have modified the Fuzzy C-Means algorithm to partition the network into clusters such as to ensure that the resulted clusters are both spatially efficient and are sharing equal data transmission load. Further in this paper, we have re-defined the medium access protocol for cluster heads. The proposed medium access protocol is dependent upon the data traffic at the Cluster heads. Cluster heads with high traffic are given preference to access the channel and cluster head(s) having low traffic are made to wait for comparatively higher back-off time. By giving more time to cluster heads with lower initial data to collect more data, energy efficiency of the system is increased and contention losses are decreased due to reduction in number of transmissions between cluster heads and sink. The proposed method has been simulated and compared with LEACH protocol, a FCM based clustering protocol and Zonal based Deterministic Energy Efficient Clustering Protocol. The simulation results show that our proposed method performs better in terms of network performance parameters viz. network lifetime, energy dissipation, throughput and packet delivery ratio.

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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. Reinisch, C., Kastner, W., Neugschwandtner, G., & Granzer, W. (2007). Wireless technologies in home and building automation. In 2007 5th IEEE international conference on industrial informatics (Vol. 1, pp. 93–98). IEEE.

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

    Article  Google Scholar 

  4. Troubleyn, E., Moerman, I., & Demeester, P. (2013). QoS challenges in wireless sensor networked robotics. Wireless Personal Communications, 70(3), 1059–1075.

    Article  Google Scholar 

  5. Manap, Z., Ali, B. M., Ng, C. K., Noordin, N. K., & Sali, A. (2013). A review on hierarchical routing protocols for wireless sensor networks. Wireless Personal Communications, 72(2), 1077–1104.

    Article  Google Scholar 

  6. Lam, S. S. (1980). A carrier sense multiple access protocol for local networks. Computer Networks (1976), 4(1), 21–32.

    Article  Google Scholar 

  7. 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 

  8. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203.

    Article  Google Scholar 

  9. Dasgupta, S., & Dutta, P. (2011). An improved Leach approach for head selection strategy in a fuzzy-C means induced clustering of a wireless sensor network. In Proceedings of the IEMCON Organised by IEM in Collaboration with IEEE (pp. 203–208).

  10. Hoang, D. C., Kumar, R., & Panda, S. K. (2010). Fuzzy C-means clustering protocol for wireless sensor networks. In 2010 IEEE international symposium on industrial electronics (ISIE) (pp. 3477–3482). IEEE.

  11. Kaur, P., & Singh, R. (2015). Zonal based deterministic energy efficient clustering protocol for WSNs. International Journal of Computer Applications, 109(10), 1–5.

  12. Mhatre, V., & Rosenberg, C. (2004). Homogeneous vs heterogeneous clustered sensor networks: A comparative study. In 2004 IEEE international conference on communications (Vol. 6, pp. 3646–3651). IEEE.

  13. Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Ipdps (vol. 1, p. 189).

  14. Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In Ipdps (Vol. 2, p. 48).

  15. 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 

  16. 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 (p. 10). IEEE.

  17. Handy, M. J., Haase M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In Proceedings of the international workshop on mobile wireless communication networks (pp. 368–372).

  18. Ferng, H. W., Tendean, R., & Kurniawan, A. (2012). Energy-efficient routing protocol for wireless sensor networks with static clustering and dynamic structure. Wireless Personal Communications, 65(2), 347–367.

    Article  Google Scholar 

  19. Baker, D. J., & Ephremides, A. (1981). The architectural organization of a mobile radio network via a distributed algorithm. IEEE Transactions on Communications, 29(11), 1694–1701.

    Article  Google Scholar 

  20. Chatterjee, M., Das, S. K., & Turgut, D. (2002). WCA: A weighted clustering algorithm for mobile ad hoc networks. Journal of Cluster Computing, Special Issue on Mobile Ad hoc Networking, 5, 193–204.

    Google Scholar 

  21. Cheng, H., Yang, S., & Cao, J. (2013). Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc networks. Expert Systems with Applications, 40, 1381–1392.

    Article  Google Scholar 

  22. Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications. Upper Saddle River, NJ: Prentice Hall.

    MATH  Google Scholar 

  23. Ross, T. (2004). Fuzzy logic with engineering applications (2nd ed.). Chichester: Wiley.

    MATH  Google Scholar 

  24. Gupta, I., Riordan, D., & Sampalli, S. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In Proceedings of the annual conference on communication network and services research (pp. 255–260).

  25. Kim, J., Park, S., Han, Y., & Chung, T. (2008): CHEF. Cluster head election mechanism using fuzzy logic in wireless sensor networks. In Proceedings of the international conference on advanced communication technology (pp. 654–659).

  26. Pires, A., Silva, C., Cerqueira, E., Monteiro, D., & Viegas, R. (2011). CHEATS: A cluster-head election algorithm for WSN using a Takagi-Sugeno fuzzy system. In 2011 IEEE Latin-American conference communications (LATINCOM) (pp. 1–6).

  27. Lee, J. S., Member, S., & Cheng, W. L. (2012). Fuzzy-logic-based clustering approach for energy predication. IEEE Sensors Journal, 12(9), 2891–2897.

    Article  Google Scholar 

  28. Jain, A., & Reddy, B. R. (2015). A novel method of modeling wireless sensor network using fuzzy graph and energy efficient fuzzy based k-hop clustering algorithm. Wireless Personal Communications, 82(1), 157–181.

    Article  Google Scholar 

  29. Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2011). A deterministic energy-efficient clustering protocol for wireless sensor networks. In 2011 Seventh international conference on intelligent sensors, sensor networks and information processing (ISSNIP) (pp. 341–346). IEEE.

  30. Bandyopadhyay, S., & Coyle, E. J. (2003). 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.

  31. Dasgupta, S., & Dutta, P. (2011). An improved Leach approach for head selection strategy in a fuzzy-C means induced clustering of a wireless sensor network. In Proceedings of the IEMCON organised by IEM in collaboration with IEEE (pp. 203–208).

  32. Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461.

    Article  Google Scholar 

  33. Singh, A. K., Goutele, S., Verma, S., & Purohit, N. (2012). An energy efficient approach for clustering in WSN using fuzzy logic. International Journal of Computer Applications, 44(18), 8–12.

    Article  Google Scholar 

  34. Hoang, D. C., Kumar, R., & Panda, S. K. (2013). Realisation of a cluster-based protocol using fuzzy C-means algorithm for wireless sensor networks. IET Wireless Sensor Systems, 3(3), 163–171.

    Article  Google Scholar 

  35. Kone, C. T., David, M., & Lepage, F. (2010). Cluster-based multi-channel system for improving performance of large-scale wireless multi-sink sensor networks. In 2010 2nd international conference on future computer and communication (ICFCC) (Vol. 3, pp. V3–163). IEEE.

  36. Park, Y. K., Lee, M. G., Jung, K. K., Yoo, J. J., Lee, S. H., & Kim, H. S. (2011). Optimum sensor nodes deployment using fuzzy c-means algorithm. In 2011 international symposium on computer science and society (ISCCS) (pp. 389–392). IEEE.

  37. Raghuvanshi, A. S., Tiwari, S., Tripathi, R., & Kishor, N. (2012). Optimal number of clusters in wireless sensor networks: A FCM approach. International Journal of Sensor Networks, 12(1), 16–24.

    Article  Google Scholar 

  38. Fouad, M. R., Fahmy, S., & Pandurangan, G. (2005). Latency-sensitive power control for wireless ad-hoc networks. In Proceedings of the 1st ACM international workshop on quality of service and security in wireless and mobile networks (pp. 31–38). ACM.

  39. Wang, X., & Berger, T. (2008). Spatial channel reuse in wireless sensor networks. Wireless Networks, 14(2), 133–146.

    Article  Google Scholar 

  40. Jain, A., & Reddy, B. R. (2015). Eigenvector centrality based cluster size control in randomly deployed wireless sensor networks. Expert Systems with Applications, 42(5), 2657–2669.

    Article  Google Scholar 

  41. Dutta, R., Gupta, S., & Das, M. K. (2014). Low-energy adaptive unequal clustering protocol using fuzzy c-means in wireless sensor networks. Wireless Personal Communications, 79(2), 1187–1209.

    Article  Google Scholar 

  42. Förster, A., Förster, A., & Murphy, A. L. (2009). Optimal cluster sizes for wireless sensor networks: An experimental analysis. In International conference on ad hoc networks (pp. 49–63). Berlin: Springer.

  43. Ho, T. S., & Chen, K. C. (1996). Performance analysis of IEEE 802.11 CSMA/CA medium access control protocol. In Proceedings of the PIMRC (Vol. 96, pp. 407–411).

  44. Lu, G., Krishnamachari, B., & Raghavendra, C. S. (2004). Performance evaluation of the IEEE 802.15. 4 MAC for low-rate low-power wireless networks. In 2004 IEEE international conference on performance, computing, and communications (pp. 701–706). IEEE.

  45. Heinzelman, W. B. (2000). Application-specific protocol architectures for wireless networks. Doctoral dissertation, Massachusetts Institute of Technology.

  46. Karl, H., & Willig, A. (2005). Protocols and architectures for wireless sensor networks (pp. 36–44). Chichester: Wiley.

    Book  Google Scholar 

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  1. Department of ECE, Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India

    Aarti Jain

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  1. Aarti Jain
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Jain, A. Traffic Aware Channel Access Algorithm for Cluster Based Wireless Sensor Networks. Wireless Pers Commun 96, 1595–1612 (2017). https://doi.org/10.1007/s11277-017-4258-4

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