Advertisement

Energy efficient clustering algorithm for the mobility support in an IEEE 802.15.4 based wireless sensor network

  • Jin-Woo KimEmail author
  • Jae-Wan Kim
Article
  • 17 Downloads

Abstract

The traditional clustering algorithm is an advanced routing protocol for enhancing an energy efficiency, which selects a cluster head and transmits the aggregated data arriving from the sensor nodes in the cluster to a gateway. However, the existing literature works were not suitable for an IEEE 802.15.4 beacon enabled mode and did not provide the combined solution for an energy efficient scheduling and handover of the sensor nodes. To address these problems, in this paper, we propose an energy efficient clustering algorithm for the mobility support in IEEE 802.15.4 networks. The simulation results show that the proposed scheme reduces the energy consumption and the packet loss, thus enhancing the performance.

Keywords

Cluster tree routing Handover Wireless sensor network IEEE 802.15.4 Internet of things 

Notes

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

References

  1. 1.
    Stojmenovic, I., Seddigh, M., & Zunic, J. (2002). Dominating sets and neighbor elimination-based broadcasting algorithms in wireless networks. IEEE Transactions on Parallel and Distributed Systems, 13(1), 14–25.CrossRefGoogle Scholar
  2. 2.
    Mišić, J., Shafi, S., & Mišić, V. B. (2006). Cross-layer activity management in a 802.15.4 sensor network. IEEE Communications Magazine, 44(1), 131–136.CrossRefGoogle Scholar
  3. 3.
    Chamberland, J.-F., & Veeravalli, V. V. (2007). Wireless sensors in distributed detection applications. IEEE Signal Processing Magazine, 24(3), 16–25.CrossRefGoogle Scholar
  4. 4.
    Boyinbode, O., Le, H., Mbogho, A., Takizawa, M., Poliah, R. (2010). A survey on clustering algorithms for wireless sensor networks. In 13th International Conference Network-Based Information Systems, Takayama, Gifu, Japan (pp. 358–364).Google Scholar
  5. 5.
    Li, X., Fletcher, G., Nayak, A., & Stojmenovic, I. (2013). Randomized carrier-based sensor relocation in wireless sensor and robot networks. Ad Hoc Networks, 11(7), 1951–1962.CrossRefGoogle Scholar
  6. 6.
    IEEE P802.15 Working Group. (2006). Wireless medium access control and physical layer specications for low-rate wireless personal area networks. IEEE Standard, 802.15.4-2006. ISBN: 0-7381-4997-7.Google Scholar
  7. 7.
    Luo, H., Ye, F., Cheng, J., Lu, S., & Zhang, L. (2005). TTDD: Two-tier data dissemination in large-scale wireless sensor networks. Wireless Networks, 11(1), 161–175.CrossRefGoogle Scholar
  8. 8.
    Awad, F. (2012). Energy-efficient and coverage-aware clustering in wireless sensor networks. Wireless Engineering and Technology, 3(3), 142–151.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Azizi, N., Karimpour, J., & Seifi, F. (2012). HCTE: Hierarchical clustering based routing algorithm with applying the two cluster heads in each cluster for energy balancing in WSN. IJCSI International Journal of Computer Science, 9(1), 57–61.Google Scholar
  10. 10.
    Ghelichi, M., Jahanbakhsh, S., Sanaei, E. (2008). RCCT: Robust clustering with cooperative transmission for energy efficient wireless sensor networks. In 5th international conference information technology: New generations (ITNG 2008), Las Vegas, NV (pp. 761–766).Google Scholar
  11. 11.
    Naeimi, S., Ghafghazi, H., Chow, C.-O., & Ishii, H. (2012). A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors, 12(6), 7350–7409.CrossRefGoogle Scholar
  12. 12.
    Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.CrossRefGoogle Scholar
  13. 13.
    Kim, J., Lee, J., & Rim, K. (2009). 3DE: Selective cluster head selection scheme for energy efficiency in wireless sensor networks. In: Proceedings of the 2nd international conference on PErvasive technologies related to assistive environments, PETRA’09, New York, NY, USA, 1-7.Google Scholar
  14. 14.
    Huang, Y.-K., Pang, A.-C., Hsiu, P.-C., Zhuang, W., & Liuy, P. (2012). Distributed throughput optimization for ZigBee clustertree networks. IEEE Transactions on Parallel and Distributed Systems, 23(3), 513–520.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 16.
    Tang, F., You, I., Guo, S., Guo, M., & Ma, Y. (2010). A chain-cluster based routing algorithm for wireless sensor networks. Journal of Intelligent Manufacturing, 23(4), 1305–1313.CrossRefGoogle Scholar
  17. 17.
    Ding, P., Holliday, J., & Celik, A. (2005). Distributed energy-efficient hierarchical clustering for wireless sensor networks. In Proceedings of the first IEEE international conference on distributed computing in sensor systems (pp. 322–339).Google Scholar
  18. 18.
    Fan, Z., & Jin, Z. (2012). A multi-weight based clustering algorithm for wireless sensor networks. Guangzhou: College of Computer Science & Educational Software Guangzhou University.Google Scholar
  19. 19.
    Azizi, N., Karimpour, J., & Seifi, F. (2012). HCTE: Hierarchical Clustering based routing algorithm with applying the Two cluster heads in each cluster for Energy balancing in WSN. IJCSI International Journal of Computer Science, 9(1), 57–61.Google Scholar
  20. 20.
    Delavar, A. G., Shamsi, S., Mirkazemi, N., & Artin, J. (2012). SLGC: A new cluster routing algorithm in wireless sensor network for decrease energy consumption. International Journal of Computer Science, Engineering and Application, 2(3), 39–51.CrossRefGoogle Scholar
  21. 21.
    Hanzalek, Z., & Jurcik, P. (2010). Energy efficient scheduling for cluster-tree Wireless Sensor Networks with time-bounded data flows: Application to IEEE 802.15.4/ZigBee. IEEE Transactions on Industrial Informatics, 6(3), 438–450.CrossRefGoogle Scholar
  22. 22.
    Saleh, A. B., Sibley, M., Mather, P. (2014). Energy efficient cluster scheduling and interference mitigation for IEEE 802.15.4 network. In Computer science and engineering conference (ICSEC), Khon Kaen, Thailand (pp. 244–250).  https://doi.org/10.1109/icsec.2014.6978202.
  23. 23.
    Shih, Y.-Y., Chung, W.-H., Hsiu, P.-C., & Pang, A.-C. (2013). A mobility-aware node deployment and tree construction framework for ZigBee wireless networks. IEEE Transactions on Vehecular Technology, 62(6), 2763–2779.CrossRefGoogle Scholar
  24. 24.
    Prinslin, L, Janani, V. (2014). Efficient data delivery in mobility aware ZigBee wireless networks. In International conference on information communication and embedded systems (ICICES), Cairo, Egypt (pp. 1–5).Google Scholar
  25. 25.
    Ayoub, Z. T., & Ouni, S. (2014). New re-association procedures for reliable handover in IEEE 802.15. 4 wireless sensor networks. In: Ad Hoc networks lecture notes of the institute for computer sciences, social informatics and telecommunications engineering (Vol. 129, pp. 3–14).Google Scholar
  26. 26.
    Javed, M., Zen, K., Bin Lenando, H., & Zen, H. (2013). Fast association process (FAP) of beacon enabled for IEEE 802.15.4 in strong mobility. In: International conference on information technology in Asia (CITA), Kota Samarahan, Malaysia (pp. 1–8).Google Scholar
  27. 27.
    Wang, J., Chalhoub, G., and Misson, M. (2017). Mobility support enhancement for RPL. In International conference on performance evaluation and modeling in wired and wireless networks (PEMWN), Paris, France (pp. 1–6).Google Scholar
  28. 28.
    Nepali, S., & Shin, J. (2017). Neighbour aware fast association scheme over IEEE 802.15.4. In International conference on frontiers of sensors technologies (ICFST), Shenzhen, China (pp. 294–298).Google Scholar
  29. 29.
    Wang, P., Li, C., & Zheng, J. (2007). Distributed data aggregation using clustered slepian-wolf coding in wireless sensor networks. In IEEE international conference on communications (ICC), Glasgow, UK (pp. 3616–3622).Google Scholar
  30. 30.
    Zheng, J., Wang, P., Li, C., & Mouftah, H. T. (2008). An efficient fault- prevention clustering protocol for robust underwater sensor networks. In IEEE international conference on communications (ICC), Beijing, China (pp. 2802–2807).Google Scholar
  31. 31.
    Tong, H., & Zheng, J. (2010). An energy and distance based clustering protocol for wireless sensor networks. In IEEE international conference on communication technology (ICCT’11), Jinan, China (pp. 666–670).Google Scholar
  32. 32.
    Tavakoli, H., Miic, J., Naderi, M., & Miic, V. B. (2013). Energy-efficient clustering in IEEE 802.15. 4 wireless sensor networks. In 2013 IEEE 33rd international conference on distributed computing systems workshops, Philadelphia, PA, USA (pp. 262–267).Google Scholar
  33. 33.
    OMNeT++ Network Simulation Framework. http://www.omnetpp.org.
  34. 34.
    Chen, F., & Dressler, F. (2007). A Simulation Model of IEEE 802.15.4 in OMNeT++. In Proceedings of 6. GI/ITG KuVS Fachgespräch Drahtlose Sensornetze, Aachen, Germany (pp. 35–38).Google Scholar
  35. 35.
    Hurtado-López, J., Casilari, E., Ariza, A. (2009). Enabling IEEE 802.15.4 cluster-tree topologies in OMNeT++. In: Proceedings of the 2nd international conference on simulation tools and techniques, Brussels (pp. 1–5).Google Scholar
  36. 36.
    IEEE Std 802.16-2012. (2012). IEEE standard for local and metropolitan area networks part 16: Air interface for broadband wireless access systems. IEEE Std 802.16-2012 (revision of IEEE Std 802.16-2009).Google Scholar
  37. 37.
    Borman, C. et al. (2001). RObust header compression (ROHC): Framework and four profiles: RTP, UDP, ESP, and uncompressed, IETF. https://tools.ietf.org/html/rfc3095.
  38. 38.
    Montenegro, G. et al. (2007). Transmission of IPv6 packets over IEEE 802.15.4 networks, IETF. https://tools.ietf.org/html/rfc4944.
  39. 39.
    Hui, J., & Thubert, P. (2011). Compression format for IPv6 datagrams over IEEE 802.15.4-based networks, IETF. https://tools.ietf.org/html/rfc6282.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The School of SoftwareSoongsil UniversitySeoulKorea
  2. 2.The Division of Electronics & Info-Communication EngineeringYeungjin UniversityDaeguKorea

Personalised recommendations