WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks

  • Authors:

  • Mainak Chatterjee;Sajal K. Das;Damla Turgut

  • Affiliations:

  • Center for Research in Wireless Mobility and Networking (CReWMaN), Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019-0015, USA;Center for Research in Wireless Mobility and Networking (CReWMaN), Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019-0015, USA;Center for Research in Wireless Mobility and Networking (CReWMaN), Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019-0015, USA

  • Venue:
  • Cluster Computing
  • Year:
  • 2002

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Abstract

In this paper, we propose an on-demand distributed clustering algorithm for multi-hop packet radio networks. These types of networks, also known as iad hoc networks, are dynamic in nature due to the mobility of nodes. The association and dissociation of nodes to and from iclusters perturb the stability of the network topology, and hence a reconfiguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The iclusterheads, form a idominant set in the network, determine the topology and its stability. The proposed weight-based distributed clustering algorithm takes into consideration the ideal degree, transmission power, mobility, and battery power of mobile nodes. The time required to identify the clusterheads depends on the diameter of the underlying graph. We try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control (MAC) protocol. The non-periodic procedure for clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. The clusterheads, operating in “dual" power mode, connects the clusters which help in routing messages from a node to any other node. We observe a trade-off between the uniformity of the load handled by the clusterheads and the connectivity of the network. Simulation experiments are conducted to evaluate the performance of our algorithm in terms of the number of clusterheads, ireaffiliation frequency, and dominant set updates. Results show that our algorithm performs better than existing ones and is also tunable to different kinds of network conditions.