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|Title:||Autonomic mobile sensor network with self-coordinated task allocation and execution||Authors:||Low, K.H.
Ang Jr., M.H.
Self-organizing neural networks
|Issue Date:||2006||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||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.||Source Title:||IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews||URI:||http://scholarbank.nus.edu.sg/handle/10635/43063||ISSN:||10946977||DOI:||10.1109/TSMCC.2006.871590|
|Appears in Collections:||Staff Publications|
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