Sustainable Task Offloading in UAV Networks via Multi-Agent Reinforcement Learning

The recent growth of IoT devices, along with edge computing, has revealed many opportunities for novel applications. Among them, Unmanned Aerial Vehicles (UAVs), which are deployed for surveillance and environmental monitoring, are attracting increasing attention. In this context, typical solutions must deal with events that may change the state of the network, providing a service that continuously maintains a high level of performance. In this paper, we address this problem by proposing a distributed architecture that leverages a Multi-Agent Reinforcement Learning (MARL) technique to dynamically offload tasks from UAVs to the edge cloud. Nodes of the system co-operate to jointly minimize the overall latency perceived by the user and the energy usage on UAVs by continuously learning from the environment the best action, which entails the decision of offloading and, in this case, the best transmission technology, i.e., Wi-Fi or cellular. Results validate our distributed architecture and show the effectiveness of the approach in reaching the above targets.

Automated summary

The recent growth of IoT devices and edge computing has created many new opportunities for applications, with a special focus on Unmanned Aerial Vehicles (UAVs) for surveillance and environmental monitoring. This paper introduces a distributed architecture leveraging Multi-Agent Reinforcement Learning (MARL) to dynamically offload tasks from UAVs to the edge cloud, aiming to minimize latency and energy usage. Results confirm the effectiveness of this distributed approach in achieving performance targets.