Skip to main content

Advertisement

Log in

LIDAR: a protocol for stable and energy-efficient clustering of ad-hoc multihop networks

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Clustering has been proposed as a promising method for simplifying the routing process in mobile ad hoc networks (MANETs). The main objective in clustering is to identify suitable node representatives, i.e. cluster heads (CHs) to store routing and topology information; CHs should be elected so as to maximize clusters stability, that is to prevent frequent cluster re-structuring. Since CHs are engaged on packet forwarding they are prone to rapidly drop their energy supplies, hence, another important objective of clustering is to prevent such node failures. Recently proposed clustering algorithms either suggest CH election based on node IDs (nodes with locally lowest ID value become CHs) or take into account additional metrics (such as energy and mobility) and optimize initial clustering. Yet, the former method is biased against nodes with low IDs (which are likely to serve as CHs for long periods and therefore run the risk of rapid battery exhaustion). Similarly, in the latter method, in many situations (e.g. in relatively static topologies) re-clustering procedure is hardly ever invoked; hence initially elected CHs soon suffer from energy drainage. Herein, we propose LIDAR, a novel clustering method which represents a major improvement over alternative clustering algorithms: node IDs are periodically re-assigned so that nodes with low mobility rate and high energy capacity are assigned low ID values and, therefore, are likely to serve as CHs. Therefore, LIDAR achieves stable cluster formations and balanced distribution of energy consumption over mobile nodes. Our protocol also greatly reduces control traffic volume of existing algorithms during clustering maintenance phase, while not risking the energy availability of CHs. Simulation results demonstrate the efficiency, scalability and stability of our protocol against alternative approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. An, B., & Papavassiliou, S. (2001). A mobility-based clustering approach to support mobility management and multicast routing in mobile ad-hoc wireless networks. International Journal of Network Management, 11(6), 387–395.

    Article  Google Scholar 

  2. Baker, D. J., Ephremides, A., & Flyn, J. A. (1984). The design and simulation of a mobile radio network with distributed control. IEEE Journal on Selected Areas in Communications, 2(1), 226–237.

    Article  Google Scholar 

  3. Basagni, S. (1999). Distributed and mobility-adaptive clustering for multimedia support in multi-hop wireless networks. In Proceedings of the 50th IEEE vehicular technology conference (pp. 889–893).

  4. Belding-Royer, E. M. (2002). Hierarchical routing in ad hoc mobile networks. Wireless Communications and Mobile Computing, 2(5), 515–532.

    Article  Google Scholar 

  5. Chatterjee, M., Das, S. K., & Turgut, T. (2002). WCA: a weighted clustering algorithm for mobile ad hoc networks. Cluster Computing, 5, 193–204.

    Article  Google Scholar 

  6. Chen, Y. P., Liestman, A. L., & Liu, J. (2004). Clustering algorithms for ad hoc wireless networks. In Y. Pan & Y. Xiao (Eds.), Ad hoc and sensor networks. Nova Science Publishers.

  7. Chiang, C. C., Wu, H. K., Liu, W., & Gerla, M. (1997). Routing in clustered multihop, mobile wireless networks with fading channel. In Proceedings of the IEEE Singapore international conference on networks (SICON’97) (pp. 197–211).

  8. Cisco (2004). Cisco aironet 1230AG series 802.11a/b/g access point data sheet. Cisco Systems.

  9. Ephremides, A., Wieselthier, J. E., & Baker, D. J. (1987). A design concept for reliable mobile radio networks with frequency hopping signalling. In Proceedings of the IEEE, 75 (pp. 56–73).

  10. Gavalas, D., Pantziou, G., Konstantopoulos, C., & Mamalis, B. (2006). Lowest-ID with adaptive ID reassignment: A novel mobile ad-hoc network clustering algorithm. In Proceedings of the 1st IEEE international symposium on wireless pervasive computing.

  11. Gavalas, D., Pantziou, G., Konstantopoulos, C., & Mamalis, B. (2006). Clustering of mobile ad hoc networks: An adaptive broadcast period approach. In Proceedings of the IEEE international conference on communications (pp. 4034–4039).

  12. Gavalas, D., Pantziou, G., Konstantopoulos, C., & Mamalis, B. (2006). Stable and energy efficient clustering of wireless ad-hoc networks with LIDAR algorithm. In Lecture notes in computer science: Vol. 4217. Proceedings of the 11th IFIP International Conference on Personal Wireless Communications (PWC’2006) (pp. 100–110).

  13. Gavalas, D., Pantziou, G., Konstantopoulos, C., & Mamalis, B. (in press). ABP: A low-cost, energy-efficient clustering algorithm for relatively static and quasi-static MANETs. International Journal of Sensor Networks.

  14. Guptar, P., & Kumar, P. R. (2000). The capacity of wireless networks. IEEE Transactions on Information Theory, IT-46(2), 388–404.

    Article  Google Scholar 

  15. Gerla, M., & Tsai, J. T. C. (1995). Multicluster, mobile, multimedia radio network. Wireless Networks, 1(3), 255–265.

    Article  Google Scholar 

  16. Hong, X., Xu, K., & Gerla, M. (2002). Scalable routing protocols for mobile ad hoc networks. IEEE Network, 16(4), 11–21.

    Article  Google Scholar 

  17. Li, C. R., & Gerla, M. (1997). Adaptive clustering for mobile wireless networks. IEEE Journal of Selected Areas in Communications, 15(7), 1265–1275.

    Article  Google Scholar 

  18. Li, F., Zhang, S., Wang, X., Xue, X., & Shen, H. (2004). Vote-based clustering algorithm in mobile ad hoc networks. In Lecture notes in computer science: Vol. 3090. Proceedings of International Conference on Networking Technologies for Broadband and Mobile Networks (pp. 13–23).

  19. McDonald, B., & Znati, F. (1999). A mobility-based framework for adaptive clustering in wireless ad hoc networks. IEEE Journal on Selected Areas in Communications, 17, 1466–1487.

    Article  Google Scholar 

  20. McDonald, B., & Znati, F. (2001). Design and performance of a distributed dynamic clustering algorithm for ad-hoc networks. In Proceedings of the 34th annual simulation symposium (pp. 27–35).

  21. Perkins, C. (2001). Ad hoc networking. Addison-Wesley.

  22. Sivavakeesar, S., Pavlou, G., & Liotta, A. (2004). Stable clustering through mobility prediction for large-scale multihop ad hoc networks. In Proceedings of the IEEE wireless communications and networking conference.

  23. Yu, J., & Chong, P. (2005). A survey of clustering schemes for mobile ad hoc networks. IEEE Communications Surveys, 7(1), 32–48.

    Article  Google Scholar 

  24. Xu, K. X., Hong, X. Y., & Gerla, M. (2002). An ad hoc network with mobile backbones. In Proceedings of the IEEE international conference on communications (pp. 3138–3143).

  25. Zheng, R., & Kravets, R. (2003). On-demand power management for ad hoc networks. In Proceedings of the IEEE Infocom’2003 (pp. 481–491).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Damianos Gavalas.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gavalas, D., Pantziou, G., Konstantopoulos, C. et al. LIDAR: a protocol for stable and energy-efficient clustering of ad-hoc multihop networks. Telecommun Syst 36, 13–25 (2007). https://doi.org/10.1007/s11235-007-9053-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-007-9053-1

Keywords

Navigation