loading page

Optimizing UAV Computation Offloading via MEC with Deep Deterministic Policy Gradient
  • Muhammad Usman Hadi,
  • Ahmed Bashir Abbasi
Muhammad Usman Hadi
Ulster University - Belfast Campus

Corresponding Author:[email protected]

Author Profile
Ahmed Bashir Abbasi
Ulster University - Belfast Campus
Author Profile


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 this paper, 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. The simulation yields outcomes indicating that the suggested algorithm can considerably enhance the competency of MEC systems.
15 May 2023Submitted to Transactions on Emerging Telecommunications Technologies
15 May 2023Submission Checks Completed
15 May 2023Assigned to Editor
15 May 2023Review(s) Completed, Editorial Evaluation Pending
29 May 2023Reviewer(s) Assigned
24 Jul 2023Editorial Decision: Revise Major
10 Aug 20231st Revision Received
11 Aug 2023Submission Checks Completed
11 Aug 2023Assigned to Editor
11 Aug 2023Review(s) Completed, Editorial Evaluation Pending
18 Aug 2023Reviewer(s) Assigned
28 Aug 2023Editorial Decision: Revise Minor
31 Aug 20232nd Revision Received
31 Aug 2023Review(s) Completed, Editorial Evaluation Pending
31 Aug 2023Submission Checks Completed
31 Aug 2023Assigned to Editor
11 Sep 2023Reviewer(s) Assigned
22 Sep 2023Editorial Decision: Accept