Multicluster, mobile, multimedia radio network
Wireless Networks
Distributed Clustering for Ad Hoc Networks
ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
A peer-to-peer zone-based two-level link state routing for mobile ad hoc networks
IEEE Journal on Selected Areas in Communications
Simple approximation algorithms and PTASs for various problems in wireless ad hoc networks
Journal of Parallel and Distributed Computing - Special issue: Algorithms for wireless and ad-hoc networks
Energy efficiency analysis of a chain-based scheme via intra-grid for wireless sensor networks
Computer Communications
Real-Time coordination and routing in wireless sensor and actor networks
NEW2AN'06 Proceedings of the 6th international conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking
A distributed backbone formation algorithm for mobile ad hoc networks
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
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In this paper, we propose a distributed clustering algorithm for a multi-hop packet radio network. These types of networks, also known as ad hoc networks, are dynamic in nature due to the mobility of the nodes. The association and dissociation of nodes to and from clusters 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 clusterheads, which form a dominant set in the network, determine the topology and its stability. Our weight based distributed clustering algorithm takes into consideration the ideal degree, transmission power, mobility and battery power of a mobile node. 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 gradually improves the load balance factor (LBF) which is a measure of the load distribution among the clusterheads. For lowering the computation and communication costs, the clustering algorithm is invoked on-demand which aims to maintain the connectivity of the network at the cost of load imbalance. Simulation experiments are conducted to evaluate the performance of our algorithm in terms of the number of clusterheads, reaffiliation frequency and dominant set updates. Results show that the our algorithm performs better than the existing algorithms and is also tunable to different types of ad hoc networks.