Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICTAI.2011.139
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dc.titleAnytime heuristic for determining agent paths that minimize maximum latency in a sparse DTN
dc.contributor.authorSivakumar, A.
dc.contributor.authorTan, C.K.-Y.
dc.date.accessioned2013-07-04T08:43:58Z
dc.date.available2013-07-04T08:43:58Z
dc.date.issued2011
dc.identifier.citationSivakumar, A., Tan, C.K.-Y. (2011). Anytime heuristic for determining agent paths that minimize maximum latency in a sparse DTN. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI : 882-883. ScholarBank@NUS Repository. https://doi.org/10.1109/ICTAI.2011.139
dc.identifier.isbn9780769545967
dc.identifier.issn10823409
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42125
dc.description.abstractMobile data relay agents (or ferries) have the potential to build wireless backbones over which sparsely located, unconnected nodes can communicate in a delay-tolerant fashion. Paths taken by agents determine the quality of such delay tolerant networks (DTNs). We consider the problem of determining agent paths that minimize the maximum pairwise latency in the network. We introduce the concept of Bounded Edge-Count Diametric Latency Minimizing Steiner trees (BECDLMST) where each edge represents an agent path and non-leaf nodes represent rendezvous points. Using ideas from particle swarm optimization, we propose an anytime algorithm to generate a near optimal BECDLMST. Beginning with a simple Minimum Diameter Steiner Tree (MDST), we apply an anytime heuristic and iteratively evolve the tree to produce a network structure that minimizes the maximum latency. Network latency results are presented for varying problem sizes in terms of number of nodes and number of agents. © 2011 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICTAI.2011.139
dc.sourceScopus
dc.subjectComputational geometry
dc.subjectDelay tolerant networks
dc.subjectMultiagent systems
dc.subjectSelf-organization
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/ICTAI.2011.139
dc.description.sourcetitleProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
dc.description.page882-883
dc.description.codenPCTIF
dc.identifier.isiut000299009900132
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