JDACO: Joint Data Aggregation and Computation Offloading in UAV-Enabled Internet of Things for Post-Disaster Scenarios

Abstract

Owing to high flexibility and rapid deployment, unmanned aerial vehicles (UAVs) can offer network coverage for Internet of Things (IoT) devices in post-disaster scenarios. UAV-aided mobile edge computing (MEC) provides computational support and facilitates optimal decision-making processes for ground-based IoT devices. However, existing literature has separately examined both data aggregation and computational offloading. In this article, we introduce a joint data aggregation and computational offloading (JDACO) scheme for UAV-enabled IoT systems in post-disaster scenarios. JDACO's primary objective is to minimize the overall energy consumption and latency in the aggregation and computation processes. It achieves this by employing UAVs as MEC servers and deploying multiple UAVs. We initially design an objective function to assess the costs associated with the aggregation and offloading processes. Subsequently, we frame the optimization problem as a Markov model and employ a multiagent deep reinforcement learning algorithm. This approach utilizes value decomposition with the double deep {Q} -Network algorithm to optimize data aggregation and enable a cost-effective offloading process through cooperative learning. Our experimental results demonstrate that our proposed JDACO scheme surpasses existing methods in terms of training time reduction, processed data volume, energy efficiency, and mission duration by 20%, 11.4%, 5.6%, and 11.2%, respectively, compared to the conventional schemes while serving up to 98% of IoT devices.