TSV-MAC: Time Slot Variable MAC Protocol Based on Deep Reinforcement Learning for UASNs

Abstract

AbstractWith the increasing variety and number of ocean applications, the underwater transmission of heterogeneous ocean data has become a hot spot in the research field of underwater acoustic sensor networks (UASNs). However, due to lack of flexibility in time slot allocation, the existing multiple access control (MAC) protocols for UASNs cannot be effectively applied to the transmission of heterogeneous ocean data. In order to solve the above problem in UASNs with heterogeneous ocean data, we propose a time slot variable MAC protocol (TSV-MAC) based on deep reinforcement learning. In TSV-MAC, the long short term memory (LSTM) deep learning model is constructed and is trained by considering the usage efficiency of time slots and the data collection condition of underwater nodes. Then, the trained LSTM model is applied to predict the generation and transmission of data from each underwater node and a Q-learning model is adopted to allocate a suitable number of time slots to underwater nodes. The TSV-MAC protocol periodically updates the time slot allocation table, to enable UASNs to adapt the different data packets which are dynamically generated. Finally, the effectiveness of the protocol is verified by extensive simulation results.KeywordsUnderwater acoustic sensor networksMultiple access control protocolDeep reinforcement learningTime slot