Deep Reinforcement Learning for Task Partitioning and Partial Offloading in UAV Networks

In recent times, Unmanned Aerial Vehicles (UAVs) have played a significant role in various fields like agriculture, defense, environmental monitoring, and many more. By minimizing the latency, energy consumption and improving the quality of services (QoS) while offloading the tasks, UAVs have played an outstanding role in the Internet of Things (IoT). Despite having a huge advantage, as the UAVs have limited computation, they cannot handle all the tasks that require intensive computation. To tackle the above problem, we have adopted the Deep Reinforcement Learning (DRL) technique in the UAV network, which helps in handling computationally expensive tasks by sharing the workload among the UAVs. The DRL-based strategy helps in reducing latency and energy consumption while maximizing the resource utilization of UAVs. Experiments have shown the significance of the adopted DRL strategy in reducing energy consumption by at least 16% compared to traditional algorithms.