# Reinforcement Learning-Enabled Reliable Wireless Sensor Networks in Dynamic Underground Environments

## TL;DR

This paper develops a reinforcement learning-based approach to optimize wireless sensor network communication in dynamic underground environments, addressing soil condition variability to improve data transmission reliability.

## Contribution

It introduces a novel reinforcement learning framework that adapts transmission policies based on real soil data, enhancing underground sensor network performance in changing conditions.

## Key findings

- Reduces packet loss in underground sensor networks
- Improves timely data transmission under dynamic soil conditions
- Demonstrates effectiveness using real-world data simulations

## Abstract

Wireless underground sensor networks play an important role in underground sensing such as climate-smart agriculture and underground infrastructure monitoring. Existing works consider a static underground environment, which is not practical since the dielectric parameters of soil change frequently due to precipitation and harsh weather. This challenge cannot be ignored in real implementation due to the drastic change of wireless underground channel. In this paper, we study the effect of dynamic underground environment on wireless communications for sensor networks. We use the real data collected by in-situ sensors to train a Hidden Markov Model. Then, by using reinforcement learning, we derive the optimal transmission policies for underground sensors to efficiently use their energy and reduce the number of dropped and unsuccessfully transmitted packets. Through simulations using real data, we find that the developed algorithm can reduce the packet loss and transmit the sensed data in a timely manner.