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Wireless Networks

, Volume 20, Issue 6, pp 1515–1525 | Cite as

IBLEACH: intra-balanced LEACH protocol for wireless sensor networks

  • Ahmed Salim
  • Walid Osamy
  • Ahmed M. KhedrEmail author
Article

Abstract

Wireless sensor networks (WSNs) are composed of many low cost, low power devices with sensing, local processing and wireless communication capabilities. Recent advances in wireless networks have led to many new protocols specifically designed for WSNs where energy awareness is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. Minimizing energy dissipation and maximizing network lifetime are important issues in the design of routing protocols for WSNs. In this paper, the low-energy adaptive clustering hierarchy (LEACH) routing protocol is considered and improved. We propose a clustering routing protocol named intra-balanced LEACH (IBLEACH), which extends LEACH protocol by balancing the energy consumption in the network. The simulation results show that IBLEACH outperforms LEACH and the existing improvements of LEACH in terms of network lifetime and energy consumption minimization.

Keywords

Wireless sensor networks LEACH Intra-balanced Cluster-based routing protocols Hierarchical clustering Base station 

References

  1. 1.
    Khedr, A.M., & Osamy, W. (2011). Effective target tracking mechanism in a self-organizing wireless sensor network. Journal of Parallel an Distributed Computing, 71, 1318–1326.CrossRefGoogle Scholar
  2. 2.
    Khedr, A.M., & Osamy, W. (2011). Minimum perimeter coverage of query regions in heterogeneous wireless sensor networks. Information Sciences, 181, 3130–3142.CrossRefGoogle Scholar
  3. 3.
    Khedr, A. M., & Osamy, W. (2006). A topology discovery algorithm for sensor network using smart antennas. Computer Communications Journal, 29, 2261–2268.CrossRefGoogle Scholar
  4. 4.
    Khedr, A. M., Osamy, W., & Agrawal, D. P. (2009). Perimeter discovery in wireless sensor networks. Journal of Parallel and Distributed Computing , 69, 922–929.CrossRefGoogle Scholar
  5. 5.
    Khedr, A. M., & Osamy, W. (2007). Target tracking mechanism for cluster based sensor networks. Applied Mathematics and Information Science Journal, 1(3), 287–303.zbMATHGoogle Scholar
  6. 6.
    Khedr, A. M. (2006). Tracking mobile targets using grid sensor networks. GESJ: Computer Science and Telecommunications, 3(10), 66–84.Google Scholar
  7. 7.
    Cerpa, A., Elson, J., Estrin, D., Girod, L., Hamilton, M., & Zhao, J. (2001). Habitat monitoring: Application driver for wireless communications technology. In Workshop on data communication, Latin America and the Caribbean (pp. 20–41), Costa Rica.Google Scholar
  8. 8.
    Estrin, D., Govindan, R., Heidemann, J., & Kumar, S. (1999). Next century challenges: Scalable coordination in sensor networks. In Proceedings of the 5th annual ACM/IEEE international conference on mobile computing and networking (pp. 263–270). Seattle, Washington, USA.Google Scholar
  9. 9.
    Yang, H., & Sikdar, B. (2007). Optimal CH selection in the LEACH architecture. In IEEE international conference on performance, computing, and communications (pp. 93–100).Google Scholar
  10. 10.
    Latiff, N. M. A., Tsimenidis, C. C., & Sharif, B. S. (2007). Performance comparison of optimization algorithm for clustering in wireless sensor networks. In IEEE International conference on mobile adhoc and sensor systems (pp. 1–4).Google Scholar
  11. 11.
    Zhang, Z., & Zhang, X. (2009). Research of improved clustering routing algorithm based on load balance in wireless sensor networks. IET International communication conference on wireless mobile and computing (pp. 661–664).Google Scholar
  12. 12.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences (pp. 1–10).Google Scholar
  13. 13.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2002). An applocation-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 662–666.CrossRefGoogle Scholar
  14. 14.
    Ok, C., Lee, S., Mitra, P., & Kumara, S. (2010). Distributed routing in wireless sensor networks using energy welfare metric. Information Sciences, 80(9), 1656–1670.CrossRefGoogle Scholar
  15. 15.
    Saleem, M., Di-Caro, GA., & Farooq, M. (2011). Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions. Information Sciences, 181(20), 4597–4624.CrossRefGoogle Scholar
  16. 16.
    Srgio, S. P., Aurlio, S. S., & Perkusich, A. (2010). Broadcast routing in wireless sensor networks with dynamic power management and multi-coverage backbones. Information Sciences, 180(5), 653–663.CrossRefGoogle Scholar
  17. 17.
    Rabaey, J. M., Ammer, J., da Silva, J. L. Jr, Patel, D., & Roundy, S. (2000). PicoRadio supports ad hoc ultra low power wireless networking. IEEE Computer, 33(7), 42–48.CrossRefGoogle Scholar
  18. 18.
    Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. In Proceedings of IEEE Infocom, New York.Google Scholar
  19. 19.
    Ye, F., Luo, H., Cheng, J., Lu, S., & Zhang, L. (2002). A two-tier data dissemination model for large- scale wireless sensor networks. In Proceedings of Mob-com02, Atlanta, GA.Google Scholar
  20. 20.
    Shih, E., Cho, S., Ickes, N., Min, R., Sinha, A., Wang, A., & Chandrakasan, A. (2001). Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks. In Proceedings of the 7th annual ACM/IEEE international conference on mobile computing and networking (Mo-bicom01), Rome, Italy.Google Scholar
  21. 21.
    Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2002). TAG: A tiny aggregation service for ad-hoc sensor networks. In ACM SIGOPS Operating Systems Review - OSDI ’02: Proceedings of the 5th symposium on Operating systems design and implementation (Vol. 36, pp. 131–146).Google Scholar
  22. 22.
    Pottle, G. J., & Kaiser, W. J. (2000). Embedding the internet: Wireless integrated network sensors. Communications of the ACM, 43(5), 51–58.Google Scholar
  23. 23.
    Wang, L., & Xiao, Y. (2006). A survey of energy- efficient scheduling mechanisms in sensor network. Journal Mobile Networks and Applications archive, 11(5), 723–740.CrossRefGoogle Scholar
  24. 24.
    Tong, M., & Tang, M. (2010). LEACH-B: an improved LEACH protocol for wireless sensor network. In 6th international conference on wireless communications networking and mobile computing (WiCOM) (pp. 1–4).Google Scholar
  25. 25.
    Hong, J., Kook, J., Lee, S., Kwon, D., & Yi, S. (2009). T-LEACH: The method of threshold-based CH replacement for wireless sensor networks. Information Systems, 11(5), 513–521.Google Scholar
  26. 26.
    Zytoune, O., El aroussi, M., Rziza, M., & Aboutajdine, D. (2008). Stochastic low energy adaptive clustering hierarchy. ICGST-CNIR, 8(1), 47–51.Google Scholar
  27. 27.
    Zytoune, O., Fakhri, Y., & Aboutajdine, D. (2009). A balanced cost cluster-heads selection algorithm for wireless sensor networks. International Journal of Computer Science, 4(1), 21–24.Google Scholar
  28. 28.
    Junping, H., Yuhui, J., & Liang, D. (2008). A time-based cluster-head selection algorithm for LEACH. In Proceedings of IEEE Symposium on computers and communications, Morocco (ISCC 2008).Google Scholar
  29. 29.
    Zhiyong, P., & Xiaojuan, L. (2010). The improvement and simulation of LEACH protocol for WSNs. In Software engineering and service sciences (ICSESS), IEEE international conference.Google Scholar
  30. 30.
    Handy, M. J., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In Mobile and wireless communications network, 2002. 4th international workshop, pp. 368–372.Google Scholar
  31. 31.
    AbuBakr, B., & Lilien, L. (2011). Extending wireless sensor network lifetime in the LEACH-SM protocol by spare selection. In Proceedings of the fifth international conference on innovative mobile and internet services in ubiquitous computing (IMIS ’11) (pp. 277–282). Washington, DC, USA: IEEE Computer Society.Google Scholar
  32. 32.
    The Network Simulator ns-2, http://www.isi.edu/nsnam/ns/.
  33. 33.
    Intel Berkely Reseach Lab (IBRL) dataset, (2004). http://db.csail.mit.edu/labdata/labdata.html.
  34. 34.
    Li, G., He, J., & Fu, Y. (2008). Group-based intrusion detection system in wireless sensor networks. Computer Communications, 31, 4324–4332.CrossRefGoogle Scholar
  35. 35.
    Moshtaghi, M., Havens, T. C., Bezdek, J. C., Park, L., Leckie, C., Rajasegarar, S., Keller, J. M., Palaniswami, M.et al. (2011). Clustering ellipses for anomaly detection. Pattern Recognition, 44, 55–69.CrossRefzbMATHGoogle Scholar
  36. 36.
    Moshtaghi, M., Rajasegarar, S., Leckie, C., & Karunasekera, S. (2009). Anomaly detection by clustering ellipsoids in wireless sensor networks. In 5th international conference on intelligent sensors, sensor networks and information processing (ISSNIP) (pp. 331–336).Google Scholar
  37. 37.
    Rajasegarar, S., Bezdek, J. C., Leckie, C., & Palaniswami, M. (2007). Analysis of anomalies in IBRL data from a wireless sensor network deployment. In International conference on sensor technologies and applications (SensorComm) (pp. 158–163).Google Scholar
  38. 38.
    Branch, J., Szymanski, B., Giannella, C., Ran, W., & Kargupta, H. (2006). In-network outlier detection in wireless sensor networks. In 26th IEEE international conference on distributed computing systems (ICDCS) (pp. 51–58).Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Mathematics DepartmentZagazig UniversityZagazigEgypt
  2. 2.Computer Science DepartmentBanha UniversityBanhaEgypt
  3. 3.Computer Science DepartmentUniversity of SharjahSharjahUAE

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