Deep Reinforcement Learning-Based Multi-Hop State-Aware Routing Strategy for Wireless Sensor Networks

Abstract

With the development of wireless sensor network technology, the routing strategy has important significance in the Internet of Things. An efficient routing strategy is one of the fundamental technologies to ensure the correct and fast transmission of wireless sensor networks. In this paper, we study how to combine deep learning technology with routing technology to propose an efficient routing strategy to cope with network topology changes. First, we use the recurrent neural network combined with the deep deterministic policy gradient method to predict the network traffic distribution. Second, the multi-hop node state is considered as the input of a double deep Q network. Therefore, the nodes can make routing decisions according to the current state of the network. Multi-hop state-aware routing strategy based on traffic flow forecasting (MHSA-TFF) is proposed. Simulation results show that the MHSA-TFF can improve transmission delay, average routing length, and energy efficiency.

Situation-Aware Survivable Network Design for Tactical Environments

“…Unlike a single-hop data transmission, the multi-hop ad hoc network has several challenges to construct reliable end-to-end paths or select the best routing path for improving network performance. Many approaches have been proposed to handle those issues [7,8]. A tactical ad hoc network requires a reliable routing protocol to transmit mission-critical data such as command and control (C2) and surveillance information.…”