Please use this identifier to cite or link to this item:
https://doi.org/10.1109/TSMCC.2006.871590
DC Field | Value | |
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dc.title | Autonomic mobile sensor network with self-coordinated task allocation and execution | |
dc.contributor.author | Low, K.H. | |
dc.contributor.author | Leow, W.K. | |
dc.contributor.author | Ang Jr., M.H. | |
dc.date.accessioned | 2013-07-23T09:23:57Z | |
dc.date.available | 2013-07-23T09:23:57Z | |
dc.date.issued | 2006 | |
dc.identifier.citation | Low, K.H., Leow, W.K., Ang Jr., M.H. (2006). Autonomic mobile sensor network with self-coordinated task allocation and execution. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 36 (3) : 315-327. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCC.2006.871590 | |
dc.identifier.issn | 10946977 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/43063 | |
dc.description.abstract | This paper describes a distributed layered architecture for resource-constrained multirobot cooperation, which is utilized in autonomic mobile sensor network coverage. In the upper layer, a dynamic task allocation scheme self-organizes the robot coalitions to track efficiently across regions. It uses concepts of ant behavior to self-regulate the regional distributions of robots in proportion to that of the moving targets to be tracked in a nonstationary environment. As a result, the adverse effects of task interference between robots are minimized and network coverage is improved. In the lower task execution layer, the robots use self-organizing neural networks to coordinate their target tracking within a region. Both layers employ self-organization techniques, which exhibit autonomic properties such as self-configuring, self-optimizing, self-healing, and self-protecting. Quantitative comparisons with other tracking strategies such as static sensor placements, potential fields, and auction-based negotiation show that our layered approach can provide better coverage, greater robustness to sensor failures, and greater flexibility to respond to environmental changes. © 2006 IEEE. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSMCC.2006.871590 | |
dc.source | Scopus | |
dc.subject | Motion control | |
dc.subject | Multirobot architecture | |
dc.subject | Self-organizing neural networks | |
dc.subject | Swarm intelligence | |
dc.subject | Task allocation | |
dc.type | Article | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.description.doi | 10.1109/TSMCC.2006.871590 | |
dc.description.sourcetitle | IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews | |
dc.description.volume | 36 | |
dc.description.issue | 3 | |
dc.description.page | 315-327 | |
dc.description.coden | ITCRF | |
dc.identifier.isiut | 000237580400005 | |
Appears in Collections: | Staff Publications |
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