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• W2336709801 abstract "In recent years, the developments of underwater acoustic wireless sensor networks (UA-WSNs) have attracted considerable research interest due to their capabilities to support underwater missions. Underwater acoustic communication channels are featured with large attenuation, long propagation delay and constrained bandwidth, which limit communications between sensors and make the system intermittent. As a result, delay tolerance is one of the major design concerns for supporting UA-WSNs to carry out tasks in harsh subsea environments. Although a number of delay tolerant schemes have been proposed for terrestrial wireless sensor networks, the fundamental differences between underwater acoustic channels and radio frequency channels make those schemes perform poorly in subsea environments. Therefore, it is desirable to develop a feasible, reliable and robust protocol for high-speed underwater acoustic wireless communications. In this dissertation, we present a family of delay tolerant protocols for UA-WSNs, which employ reinforcement learning algorithms. In the proposed system, a UA-WSN consisting of a number of static sensors and a mobile ferry is modeled as a single agent reinforcement learning system. The sensors are assumed to be sparsely-distributed, energy-constrained, stationary, and consequently are not capable of peer to peer sensor communications. For the purpose of conserving energy, each sensor periodically transits between the active state and the sleep state. A ferry is employed to travel around the deployment field to collect, carry and deliver data packets between sensors. A specific position where the ferry contacts with a specific sensor is known as a way-point which is fixed at the position of its host sensor. The ferry acts as the intelligent agent of the employed reinforcement" @default.
• W2336709801 created "2016-06-24" @default.
• W2336709801 creator A5079909649 @default.
• W2336709801 date "2014-01-01" @default.
• W2336709801 modified "2023-09-27" @default.
• W2336709801 title "Machine learning based delay tolerant protocols for underwater acoustic wireless sensor networks" @default.
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