Optimizing UAV computation offloading via MEC with deep deterministic policy gradient

Mobile edge computing (MEC) seems to be highly efficient to process the generated data from IoT devices by providing computational resources locating in close range to network edge. MEC can be promising in reduction of latency and consumption of energy from data transmissions from offloading computational tasks from IoT devices to nearby edge servers. In the context of the growing IoT ecosystem, there is an increasing need for efficient data processing and communication strategies. There is a demand of bridging the gap in current research with novel optimization algorithms tailored for UAV-assisted MEC systems, shedding light on the necessity of efficient computation offloading in meeting the demands of the IoT era. In this article, a computation offloading optimization algorithm is proposed which is based on deep deterministic policy gradient for realistic Aurelia X6 Pro unmanned aerial vehicle (UAV)-assisted MEC systems. The proposed algorithm optimizes the offloading decision for UAVs by taking task characteristics and the communication environment into consideration. To demonstrate the effectiveness of the proposed algorithm, comprehensive simulations were conducted, and the results indicate substantial improvements in MEC systems' competency. Our research not only showcases the feasibility of deep deterministic policy gradient in UAV-assisted MEC systems but also highlights the importance of developing efficient computation offloading strategies for the evolving landscape of IoT and edge computing.