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dc.contributor.authorDyo, Vladimiren_GB
dc.contributor.authorMascolo, Ceciliaen_GB
dc.date.accessioned2013-03-13T13:07:44Z
dc.date.available2013-03-13T13:07:44Z
dc.date.issued2008
dc.identifier.citationDyo, V., Mascolo, C., (2008) 'Efficient Node Discovery in Mobile Wireless Sensor Networks', in DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems: 478-485en_GB
dc.identifier.isbn9783540691693
dc.identifier.doi10.1007/978-3-540-69170-9_33
dc.identifier.urihttp://hdl.handle.net/10547/272052
dc.description.abstractIn this paper we propose an algorithm for energy efficient node discovery in sparsely connected mobile wireless sensor networks. The work takes advantage of the fact that nodes have temporal patterns of encounters and exploits these patterns to drive the duty cycling. Duty cycling is seen as a sampling process and is formulated as an optimization problem. We have used reinforcement learning techniques to detect and dynamically change the times at which a node should be awake as it is likely to encounter other nodes. We have evaluated our work using real human mobility traces, and the paper presents the performance of the protocol in this context.
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://dl.acm.org/citation.cfm?id=1425072en_GB
dc.subjectwireless sensor networksen_GB
dc.titleEfficient node discovery in mobile wireless sensor networksen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity College Londonen_GB
dc.contributor.departmentUniversity of Cambridgeen_GB
html.description.abstractIn this paper we propose an algorithm for energy efficient node discovery in sparsely connected mobile wireless sensor networks. The work takes advantage of the fact that nodes have temporal patterns of encounters and exploits these patterns to drive the duty cycling. Duty cycling is seen as a sampling process and is formulated as an optimization problem. We have used reinforcement learning techniques to detect and dynamically change the times at which a node should be awake as it is likely to encounter other nodes. We have evaluated our work using real human mobility traces, and the paper presents the performance of the protocol in this context.


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  • Centre for Wireless Research (CWR)
    The Centre for Wireless Research brings together expertise in the areas of mobile and wireless sensor networks. The breadth and depth of the expertise make the Centre rich with research and innovation potential.

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