Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning

TL;DR

This paper introduces a multi-agent reinforcement learning approach to optimize communication strategies for power- and bandwidth-constrained IoT sensors monitoring environmental conditions, improving data collection efficiency and accuracy.

Contribution

It develops a novel MARL method that leverages environmental data correlations to optimize sensor communication policies under resource constraints.

Findings

MARL effectively balances data collection and resource limitations.

The method improves wildfire spread prediction accuracy.

Sensors exploit spatial-temporal data correlations to reduce redundancy.

Abstract

Internet of Things (IoT) technologies have enabled numerous data-driven mobile applications and have the potential to significantly improve environmental monitoring and hazard warnings through the deployment of a network of IoT sensors. However, these IoT devices are often power-constrained and utilize wireless communication schemes with limited bandwidth. Such power constraints limit the amount of information each device can share across the network, while bandwidth limitations hinder sensors' coordination of their transmissions. In this work, we formulate the communication planning problem of IoT sensors that track the state of the environment. We seek to optimize sensors' decisions in collecting environmental data under stringent resource constraints. We propose a multi-agent reinforcement learning (MARL) method to find the optimal communication policies for each sensor that maximize the tracking accuracy subject to the power and bandwidth limitations. MARL learns and exploits the spatial-temporal correlation of the environmental data at each sensor's location to reduce the redundant reports from the sensors. Experiments on wildfire spread with LoRA wireless network simulators show that our MARL method can learn to balance the need to collect enough data to predict wildfire spread with unknown bandwidth limitations.