An Energy-Efficient and Transmission-Efficient Adaptive Routing Algorithm Using Deep Reinforcement Learning for Wireless Sensor Networks

How to improve energy efficiency (EE) and transmission efficiency for wireless sensor networks (WSNs) through dynamic routing is a challenge. This article introduces MADII, an adaptive routing algorithm integrating the informer model and imitation learning (IL) within a multiagent deep Q-network (DQN) framework. Our method models the routing decision in WSN as a multiagent Markov decision process (MMDP) and employs a novel sensor transmission control mechanism for information via negotiation in centralized-training, centralized-execution and centralized-distribution (STCIN3C) tailored for the centralized training and centralized execution (CTCE) paradigm. This design effectively minimizes redundant energy consumption of sensor nodes (SNs). The reward function of reinforcement learning (RL) is designed by considering transmission quantity, data-receiving success rate, and relative energy consumption. Furthermore, our approach adopts a combination of informer model and IL strategy for balancing exploration and exploitation of RL applications with large-scale action space through deep learning (DL) model and training sample, which can accelerate convergence speed, fit the dynamically changing features of WSN, and obtain better routing scheme. Extensive experiments demonstrate that MADII significantly outperforms existing methods on network lifetime and EE. Furthermore, the ablation experiment emphasizes the effectiveness of informer model and IL strategy in dealing with RL with large-scale action space.