Hierarchically-organized, multihop mobile wireless networks for quality-of-service support
Mobile Networks and Applications - Special issue: mobile multimedia communications
The broadcast storm problem in a mobile ad hoc network
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
A Mobility Based Metric for Clustering in Mobile Ad Hoc Networks
ICDCSW '01 Proceedings of the 21st International Conference on Distributed Computing Systems
Loopy Belief Propagation: Convergence and Effects of Message Errors
The Journal of Machine Learning Research
A binary variable model for affinity propagation
Neural Computation
Index coded repetition-based MAC in vehicular ad-hoc networks
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
Mobility-based clustering in VANETs using affinity propagation
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A Position-Based Clustering Technique for Ad Hoc Intervehicle Communication
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sufficient Conditions for Convergence of the Sum–Product Algorithm
IEEE Transactions on Information Theory
Ad hoc peer-to-peer network architecture for vehicle safety communications
IEEE Communications Magazine
Adaptive clustering for mobile wireless networks
IEEE Journal on Selected Areas in Communications
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The need for an effective clustering algorithm for Vehicular Ad Hoc Networks (VANETs) is motivated by the recent research in cluster-based MAC and routing schemes. VANETs are highly dynamic and have harsh channel conditions, thus a suitable clustering algorithm must be robust to channel error and must consider node mobility during cluster formation. This work presents a novel, mobility-based clustering scheme for Vehicular Ad hoc Networks, which forms clusters using the Affinity Propagation algorithm in a distributed manner. This proposed algorithm considers node mobility during cluster formation and produces clusters with high stability. Cluster performance was measured in terms of average clusterhead duration, average cluster member duration, average rate of clusterhead change, and average number of clusters. The proposed algorithm is also robust to channel error and exhibits reasonable overhead. Simulation results confirm the superior performance, when compared to other mobility-based clustering techniques.