Abstract
In this paper, we investigate how to efficiently utilize channel bandwidth in heterogeneous hybrid optical and acoustic underwater sensor networks, where sensor nodes adopt different Media Access Control (MAC) protocols to transmit data packets to a common relay node on optical or acoustic channels. We propose a new MAC protocol based on deep reinforcement learning (DRL), referred to as optical and acoustic dual-channel deep-reinforcement learning multiple access (OA-DLMA), in which the sensor nodes utilizing the OA-DLMA protocol are called agents, and the remainder are non-agents. The agents can learn the transmission patterns of coexisting non-agents and find an optimal channel access strategy without any prior information. Moreover, in order to further enhance network performance, we develop a differentiated reward policy that rewards specific actions over optical and acoustic channels differently, with priority compensation being given to the optical channel to achieve greater data transmission. Furthermore, we have derived the optimal short-term sum throughput and channel utilization analytically and conducted extensive simulations to evaluate the OA-DLMA protocol. Simulation results show that our protocol performs with near-optimal performance and significantly outperforms other existing protocols in terms of short-term sum throughput and channel utilization.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The heterogeneous Underwater Sensor Networks (UWSNs) architecture with hybrid optical and acoustic dual channels considered in this paper is illustrated in Figure 2, which is mainly formed from underwater sensor nodes, underwater uplink acoustic and optical channels, and several water surface bacons
We proposed an optical and acoustic dual-channel deep-reinforcement learning multiple access protocol for heterogeneous underwater sensor networks, referred to as OA-Deep Reinforcement Learning Multiple Access (DLMA)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Though the improved optical and acoustic MAC protocols have promoted the network performance, a common precondition of the series is that the global environmental information (the propagation delay, the transmission methods, including channel reservation, data forwarding, etc.) is supposed to be known among nodes [8,16]. Motivated by the aforementioned considerations, in this paper we propose a new MAC protocol for heterogeneous hybrid optical and acoustic underwater sensor networks, referred to as optical and acoustic dual-channel deep-reinforcement learning multiple access (OA-DLMA). The agents can interact with the environment and learn the optimal transmission strategy when coexisting with non-agent nodes from a series of observations and actions to achieve the goal of performance optimization In this way, the agents can make full use of the available time slots on both the acoustic and optical channels.
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