TITLE:
QoE-Driven Task Offloading under Shared Resources in 5G-Enabled IoT Systems
AUTHORS:
Kadjo Tanon Lambert, Kone Kigninman Désiré, Bita Romaric de Judidael, Asseu Olivier
KEYWORDS:
5G IoT, Computation Offloading, Mobile Edge Computing, PiFBOA, Metaheuristic Optimization, Shared Resources, QoE, Latency
JOURNAL NAME:
Journal of Sensor Technology,
Vol.16 No.4,
October
27,
2026
ABSTRACT: Computation-intensive Internet of Things (IoT) applications require offloading decisions that account for latency, device-side energy consumption, and contention for shared edge resources. This study proposes a hybrid metaheuristic for ternary task assignment across local devices, Mobile Edge Computing (MEC), and cloud resources. The offloading model couples user decisions through shared uplink bandwidth and MEC computing capacity. Magnificent Frigatebird Optimization (MFO) provides the main population-search mechanism, while Piranha Foraging Optimization Algorithm (PFOA) contributes a selectively accepted nonlinear reverse-evasion refinement around the incumbent best solution. The proposed approach is evaluated on 400-user workloads over 30 paired random seeds using a matched search budget of 11,250 fitness evaluations against MFO, HGSTO, and discrete Particle Swarm Optimization (PSO). It achieves a Quality of Experience (QoE) satisfaction ratio of 0.5769, a mean latency of 0.4926 s, and a mean device-side energy consumption of 0.0933 J, compared with QoE values of 0.5719 for MFO and 0.5242 for HGSTO. The QoE improvement over MFO remains statistically significant under the paired t-test, Wilcoxon signed-rank test, and Holm correction. In addition, exhaustive evaluation on ten tractable 9-task instances shows that the proposed method reaches the exact optimum in all cases. Overall, the results indicate that selective PFOA refinement provides a small but consistent improvement over the MFO backbone under the considered resource-coupled offloading scenario.