Mobility planning of LoRa gateways for edge storage of IoT data

Abstract

LoRaWAN is now a leading technology in IoT developments due to its low power consumption and simple deployment features. Despite being termed a long-distance technology, its coverage is often hampered by the harsh propagation environment. In such cases, a very cost-effective solution is to use mobile gateways for both data collection and delivery at specific places of the edge, avoiding the need for cellular communication modules. This article addresses the optimization problem of deciding how mobile gateways should move, and which edge devices should be visited (and when) for collected data to be fed into the edge distributed storage, while ensuring data usefulness. This optimization problem of gateway mobility with edge storage is formulated mathematically, and a deep reinforcement learning framework is proposed to solve it. Results show that the proposed framework can achieve near optimal results, in particular for large-dense deployments and not very strict time windows. The proposed deep reinforcement learning solution is able to capture underlying patterns, like device coverage and traffic, without prior knowledge of such information.