Dominating Sets and Neighbor Elimination-Based Broadcasting Algorithms in Wireless Networks
IEEE Transactions on Parallel and Distributed Systems
Smart-Its Friends: A Technique for Users to Easily Establish Connections between Smart Artefacts
UbiComp '01 Proceedings of the 3rd international conference on Ubiquitous Computing
Distributed Clustering for Ad Hoc Networks
ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
Sensor Networks for Emergency Response: Challenges and Opportunities
IEEE Pervasive Computing
Lightweight detection and classification for wireless sensor networks in realistic environments
Proceedings of the 3rd international conference on Embedded networked sensor systems
Shake well before use: authentication based on accelerometer data
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Movement-based group awareness with wireless sensor networks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Towards mood based mobile services and applications
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
D-FLER: a distributed fuzzy logic engine for rule-based wireless sensor networks
UCS'07 Proceedings of the 4th international conference on Ubiquitous computing systems
Modeling service-oriented context processing in dynamic body area networks
IEEE Journal on Selected Areas in Communications - Special issue on body area networking: Technology and applications
WSNs clustering based on semantic neighborhood relationships
Computer Networks: The International Journal of Computer and Telecommunications Networking
A study on automatic recognition of object use exploiting motion correlation of wireless sensors
Personal and Ubiquitous Computing
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Wireless sensor nodes attached to everyday objects and worn by people are able to collaborate and actively assist users in their activities. We propose a method through which wireless sensor nodes organize spontaneously into clusters based on a common context. Provided that the confidence of sharing a common context varies in time, the algorithm takes into account a window-based history of believes. We approximate the behaviour of the algorithm using a Markov chain model and we analyse theoretically the cluster stability. We compare the theoretical approximation with simulations, by making use of experimental results reported from field tests. We show the tradeoff between the time history necessary to achieve a certain stability and the responsiveness of the clustering algorithm.