Related papers: HGFF: A Deep Reinforcement Learning Framework for Lifetime Maximization in Wireless Sensor Networks

HGFF: A Deep Reinforcement Learning Framework for Lifetime Maximization in Wireless Sensor Networks

URL: http://arxiv.org/abs/2407.07747v1
Date: Thu, 11 Apr 2024 13:09:11 GMT
Title: HGFF: A Deep Reinforcement Learning Framework for Lifetime Maximization in Wireless Sensor Networks
Authors: Xiaoxu Han, Xin Mu, Jinghui Zhong,
Abstract summary: We propose a new framework combining heterogeneous graph neural network with deep reinforcement learning to automatically construct the movement path of the sink. We design ten types of static and dynamic maps to simulate different wireless sensor networks in the real world. Our approach consistently outperforms the existing methods on all types of maps.
Score: 5.4894758104028245
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Planning the movement of the sink to maximize the lifetime in wireless sensor networks is an essential problem of great research challenge and practical value. Many existing mobile sink techniques based on mathematical programming or heuristics have demonstrated the feasibility of the task. Nevertheless, the huge computation consumption or the over-reliance on human knowledge can result in relatively low performance. In order to balance the need for high-quality solutions with the goal of minimizing inference time, we propose a new framework combining heterogeneous graph neural network with deep reinforcement learning to automatically construct the movement path of the sink. Modeling the wireless sensor networks as heterogeneous graphs, we utilize the graph neural network to learn representations of sites and sensors by aggregating features of neighbor nodes and extracting hierarchical graph features. Meanwhile, the multi-head attention mechanism is leveraged to allow the sites to attend to information from sensor nodes, which highly improves the expressive capacity of the learning model. Based on the node representations, a greedy policy is learned to append the next best site in the solution incrementally. We design ten types of static and dynamic maps to simulate different wireless sensor networks in the real world, and extensive experiments are conducted to evaluate and analyze our approach. The empirical results show that our approach consistently outperforms the existing methods on all types of maps.

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