Cover
Vol. 2 No. 2 (2026)

Published: October 7, 2026

Pages: 128 - 138

Original Article

Energy-Aware Load Balancing in Mobile Edge Computing Using Deep Reinforcement Learning for IoT Applications

Zainab M. Abdulameer 1
1 Department of Computer Engineering, Azad University, Tehran, Iran
History
  • Received: May 9, 2026
  • Revised: June 1, 2026
  • Accepted: June 21, 2026
  • Available Online: November 14, 2026
DOI

https://doi.org/10.33971/ijmrai.2.2.4

Abstract

The increased computational loads of the Internet of Things (IoT) devices have been increasing exponentially, approaching 29.4 billion connected units in the world by 2030, which is beyond the processing capabilities of small-scale IoT devices. Mobile Edge Computing (MEC) reduces latency by offloading tasks to edge servers, but coordinating offloading across heterogeneous MEC servers, while minimizing energy use and meeting strict service-level agreements (SLAs), remains an open problem. Rule-based load balancers cannot handle dynamic, non-stationary IoT workloads, and existing DRL methods converge slowly and waste energy under variable server loads. In this paper, a proposal of an Energy-Aware Load Balancing framework of MEC-IoT settings is presented, which utilizes Double Deep Q-Network (DDQN) and Prioritized Experience Replay (PER) to learn adaptive offloading policies, and Dynamic Voltage and Frequency Scaling (DVFS) to control fine-grained MEC server energy expenditure. The DDQN agent sees a single state description of the IoT task features (type, size, deadline) and real-time load on the MEC server, queue lengths, and leftover energy budgets and chooses an offloading destination: local processing, one of five edge servers, or cloud to optimize a hybrid reward function that punishes both task completion time and cumulative system energy usage. The framework is tested with the high-fidelity SimPy discrete-event simulation, which is, in turn, calibrated to the real MEC testbed parameters, and compared to seven baselines with different IoT device counts (20 to 200 devices) and workload intensities. The proposed framework cuts total energy use by 45.9% versus random offloading and 19.5% versus the best DRL baseline (A3C). It also reduces mean latency to 41.8ms (70.6% lower than random offloading) and keeps the SLA violation rate at 3.2%, below the 5% target. Its advantage holds across all tested device scales.​

Keywords

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