Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Energy-efficient collision-free medium access control for wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
The number of neighbors needed for connectivity of wireless networks
Wireless Networks
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Panel on mobility in sensor networks
Proceedings of the 6th international conference on Mobile data management
A community based mobility model for ad hoc network research
REALMAN '06 Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality
On accurate measurement of link quality in multi-hop wireless mesh networks
Proceedings of the 12th annual international conference on Mobile computing and networking
Context as autonomic intelligence in a ubiquitous computing environment
International Journal of Internet Protocol Technology
Scalable and Efficient Graph Colouring in 3 Dimensions Using Emergence Engineering Principles
SASO '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Beaconing in wireless mobile networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Mobility-based communication in wireless sensor networks
IEEE Communications Magazine
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Wireless sensor networks and ubiquitous computing are rapidly increasing in popularity and diversity. For many applications of these systems the mobility status of devices forms part of the operating context on which self-organisation is based. This paper describes a novel technique by which wireless devices such as sensor nodes can deduce their own mobility status, based on analysis of patterns in their local neighbourhood. The Self-Detection of Mobility Status algorithm (SDMS) uses a reinforcement learning inspired mechanism to combine the indications from five mobility metrics. For many systems in which a neighbour table is maintained through regular status messages or other interaction, the technique incurs no additional communication overhead. The technique does not require that nodes have additional information such as absolute or relative locations, or neighbourhood node density. The work considers systems with heterogeneous time-variant mobility models, in which a subset of nodes follows a random walk mobility model, another subset follows a random waypoint mobility model (i.e. has intermittent movement), some nodes have group mobility and there is a static subset. We simulate these heterogeneous mobility systems and evaluate the performance of SDMS against a number of metrics and in a wide variety of system configurations.