Please use this identifier to cite or link to this item: https://doi.org/10.1007/11669463_20
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dc.titleSift: A MAC protocol for event-driven wireless sensor networks
dc.contributor.authorJamieson, K.
dc.contributor.authorBalakrishnan, H.
dc.contributor.authorTay, Y.C.
dc.date.accessioned2014-10-28T02:51:45Z
dc.date.available2014-10-28T02:51:45Z
dc.date.issued2006
dc.identifier.citationJamieson, K.,Balakrishnan, H.,Tay, Y.C. (2006). Sift: A MAC protocol for event-driven wireless sensor networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3868 LNCS : 260-275. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/11669463_20" target="_blank">https://doi.org/10.1007/11669463_20</a>
dc.identifier.isbn3540321586
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104626
dc.description.abstractNodes in sensor networks often encounter spatially-correlated contention, where multiple nodes in the same neighborhood all sense an event they need to transmit information about. Furthermore, in many sensor network applications, it is sufficient if a subset of the nodes that observe the same event report it. We show that traditional carrier-sense multiple access (CSMA) protocols for sensor networks do not handle the first constraint adequately, and do not take advantage of the second property, leading to degraded latency as the network scales in size. We present Sift, a medium access control (MAC) protocol for wireless sensor networks designed with the above observations in mind. We show using simulations that as the size of the sensor network scales up to 500 nodes, Sift can offer up to a 7-fold latency reduction compared to other protocols, while maintaining competitive throughput. © Springer-Verlag Berlin Heidelberg 2006.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/11669463_20
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentMATHEMATICS
dc.description.doi10.1007/11669463_20
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume3868 LNCS
dc.description.page260-275
dc.identifier.isiutNOT_IN_WOS
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