Resource Allocation Strategy for Wireless Sensor Networks Based on Reinforcement Learning

In the realm of wireless sensor networks for transmission line monitoring, the proliferation of sensor devices inevitably results in significant potential interference, thereby exacerbating the complexity of resource management. To ad- dress this challenge, this paper introduces a deep reinforcement learning-based resource management strategy known as D3QN. Initially, a conflict graph model is constructed using graph theory to delineate the interference patterns among devices within the network. Subsequently, the resource allocation issue is reformulated as a graph vertex coloring problem. This problem is then cast as a Markov decision process, which is adeptly tackled by the D3QN algorithm to achieve conflict-free resource allocation. The simulation outcomes demonstrate that the proposed algorithm outperforms existing methods in the context of wireless sensor networks.