Intelligent Communication Planning for Constrained Environmental IoT
  Sensing with Reinforcement Learning
Yi Hu Jinhang Zuo Bob Iannucci Carlee Joe-Wong
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.