Multicluster, mobile, multimedia radio network
Wireless Networks
WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks
Cluster Computing
Design and Performance of a Distributed Dynamic Clustering Algorithm for Ad-Hoc Networks
SS '01 Proceedings of the 34th Annual Simulation Symposium (SS01)
A survey of clustering schemes for mobile ad hoc networks
IEEE Communications Surveys & Tutorials
Adaptive clustering for mobile wireless networks
IEEE Journal on Selected Areas in Communications
A mobility-based framework for adaptive clustering in wireless ad hoc networks
IEEE Journal on Selected Areas in Communications
Scalable routing protocols for mobile ad hoc networks
IEEE Network: The Magazine of Global Internetworking
CASAN: Clustering algorithm for security in ad hoc networks
Computer Communications
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The main objective of clustering in mobile ad-hoc network environments 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. A popular clustering algorithm (LID) suggests CH election based on node IDs (nodes with locally lowest ID value become CHs). Although fast and simple, this method is biased against nodes with low IDs, which are likely to serve as CHs for long periods and are therefore prone to rapid battery exhaustion. Herein, we propose LIDAR, a novel clustering method which represents a major improvement over traditional LID algorithm: 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. 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.