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dc.contributor.authorKhan, Muhidul Islam
dc.identifier.citationMuhidulislam, K. (2016). Resource-aware task scheduling by an adversarial bandit solver method in wireless sensor networks. Eurasip Journal on Wireless Communications and Networking, 1–17.
dc.descriptionThis article was published in the Eurasip Journal on Wireless Communications and Networking [©2016 Springer International Publishing.] and the definite version is available at: The article website is at:
dc.description.abstractA wireless sensor network (WSN) is composed of a large number of tiny sensor nodes. Sensor nodes are very resource-constrained, since nodes are often battery-operated and energy is a scarce resource. In this paper, a resource-aware task scheduling (RATS) method is proposed with better performance/resource consumption trade-off in a WSN. Particularly, RATS exploits an adversarial bandit solver method called exponential weight for exploration and exploitation (Exp3) for target tracking application of WSN. The proposed RATS method is compared and evaluated with the existing scheduling methods exploiting online learning: distributed independent reinforcement learning (DIRL), reinforcement learning (RL), and cooperative reinforcement learning (CRL), in terms of the tracking quality/energy consumption trade-off in a target tracking application. The communication overhead and computational effort of these methods are also computed. Simulation results show that the proposed RATS outperforms the existing methods DIRL and RL in terms of achieved tracking performance. © 2016, Khan.en_US
dc.publisher© 2016 Springer International Publishingen_US
dc.subjectAdversarial bandit solversen_US
dc.subjectCooperative reinforcement learningen_US
dc.subjectIndependent reinforcement learningen_US
dc.subjectTask schedulingen_US
dc.subjectWireless sensor networksen_US
dc.titleResource-aware task scheduling by an adversarial bandit solver method in wireless sensor networksen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University

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