Title: Exploring enhanced swarm intelligence algorithms for efficient cyber foraging on transient mobile clouds

Authors: Tiako Fani Ndambomve; Felicitas Mokom; Kolyang Dina Taiwe

Addresses: School of Information Technology, Catholic University Institute of Buea, P.O. Box 563 Buea, Cameroon; LaRI Lab, University of Maroua, P.O. Box 814, Cameroon ' School of Information Technology, Catholic University Institute of Buea, P.O. Box 563 Buea, Cameroon ' LaRI Lab, University of Maroua, P.O. Box 814 Maroua, Cameroon

Abstract: Transient Mobile Clouds (TMCs) enable nearby mobile devices to collaboratively provide low-latency and cost-effective computational services without relying on centralised cloud infrastructures. However, efficient task offloading in TMCs remains challenging due to device mobility, fluctuating network conditions, energy limitations, and dynamic resource availability. This study proposes two enhanced swarm intelligence algorithms, Enhanced Multi-Objective Particle Swarm Optimisation (EMO-PSO) and Enhanced Multi-Objective Discrete Artificial Bee Colony (EMO-DABC), for efficient cyber foraging in TMC environments. The proposed approaches integrate adaptive energy-aware mechanisms, neighbourhood-based clustering, and priority-driven task allocation to optimise energy consumption, task completion time, processor utilisation, and load balancing. EMO-PSO employs adaptive inertia weighting and proximity-based clustering, while EMO-DABC introduces energy-threshold role switching and task prioritisation. Simulation results demonstrate significant improvements over traditional PSO and DABC algorithms, including reduced energy consumption, lower latency, improved throughput, and enhanced load balancing, confirming the effectiveness of the proposed approaches for dynamic TMC systems.

Keywords: cyber foraging; discrete artificial bee colony; multi-objective optimisation; particle swarm optimisation; transient mobile clouds.

DOI: 10.1504/IJWMC.2026.155718

International Journal of Wireless and Mobile Computing, 2026 Vol.31 No.2, pp.154 - 170

Received: 21 Dec 2024
Accepted: 18 Nov 2025
Published online: 11 Aug 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article