Wireless underground sensor networks (WUSNs) consist of sensors that are buried in and communicate through soil medium, while the channel quality of WUSNs is greatly impacted by the underground environment, such as soil moisture and composition. Due to the precipitation and harsh weather, the underground environments change frequently, which make wireless communication in WUSNs much complicated than that in terrestrial over-the-air wireless sensor networks. To achieve reliable and energy-efficient data gathering in dynamic WUSNs, this article proposes an optimal transmission policy, where path loss of sensory data transmission, energy constraint, and network load balancing are the factors to be considered. Specifically, we capture the effect of underground environments on wireless communications, and evaluate path probability, energy consumption, and load balancing factor with respect to reliability and efficiency of transmission paths. The transmission topology can be reduced to a multi-objective and multi-constrained optimization problem and solved through an improved maximum flow minimum cost algorithm. By using reinforcement learning, we derive an adaptive transmission policy for underground sensors to efficiently use their energy and avoid transmitting sensory data in unreliable paths under a dynamic environment. Through simulations and comparison upon publicly available real data, our technique achieves more reliable wireless communication with significant reduction of packet loss, and enables more energy-efficient data gathering than other techniques, especially when soil moisture varies frequently.

无线地下传感器网络（WUSN）由埋在土壤中并通过土壤介质通信的传感器组成，而WUSN的信道质量受地下环境（例如土壤湿度和成分）的影响很大。由于降雨和恶劣的天气，地下环境经常变化，这使得WUSN中的无线通信比地面无线传感器网络复杂得多。在动态WUSN中实现可靠且节能的数据收集，本文提出了一种最佳的传输策略，其中要考虑的因素包括感觉数据传输的路径损耗，能量约束和网络负载平衡。具体而言，我们捕获了地下环境对无线通信的影响，并就传输路径的可靠性和效率评估了路径概率，能耗和负载平衡因子。可以将传输拓扑简化为多目标，多约束的优化问题，并通过改进的最大流量最小成本算法进行求解。通过使用强化学习，我们得出了地下传感器的自适应传输策略，以有效利用其能量，并避免在动态环境下以不可靠的路径传输传感数据。