Multicluster, mobile, multimedia radio network
Wireless Networks
A group mobility model for ad hoc wireless networks
MSWiM '99 Proceedings of the 2nd ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
A Mobility Based Metric for Clustering in Mobile Ad Hoc Networks
ICDCSW '01 Proceedings of the 21st International Conference on Distributed Computing Systems
Design and Performance of a Distributed Dynamic Clustering Algorithm for Ad-Hoc Networks
SS '01 Proceedings of the 34th Annual Simulation Symposium (SS01)
Probabilistic routing in intermittently connected networks
ACM SIGMOBILE Mobile Computing and Communications Review
A message ferrying approach for data delivery in sparse mobile ad hoc networks
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
Mobility Modeling of Outdoor Scenarios for MANETs
ANSS '05 Proceedings of the 38th annual Symposium on Simulation
DTN routing in a mobility pattern space
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
DTN routing strategies using optimal search patterns
Proceedings of the third ACM workshop on Challenged networks
Mobility entropy and message routing in community-structured delay tolerant networks
Proceedings of the 4th Asian Conference on Internet Engineering
Energy and path aware clustering algorithm (EPAC) for mobile ad hoc networks
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part IV
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Non-uniform distributions of mobile nodes are the norm for a mobile network. Often, there can be concentration areas or grouping of nodes. Early work has explored these features to help message disseminations. However, a mobile network application can generate complex mixing mobility patterns that render these work less effective and efficient. In addition, many applications run with in a sparse mode, namely, the network may not be connected all the time. In this paper, we propose two entropy based metrics to identify the nodes with different mobility patterns and further use the metrics to accomplish clustering. Aiming at low-end devices which have no inputs of velocity and location, we employ neighbor information through hello messages and draw speed implication through neighbor change rates. The entropy based metrics are used in a clustering algorithm to find stable nodes as cluster heads. According to the the simulation results, two metrics, namely, speed entropy and relation entropy can be applied to distinguish active nodes from stable nodes in different group mixing configurations. The simulations also show that our new metric-based clustering algorithm generates more stable clusters.