@Article{cmc.2025.062451,
AUTHOR = {Jafar Aminu, Rohaya Latip, Zurina Mohd Hanafi, Shafinah Kamarudin, Danlami Gabi},
TITLE = {Efficient Task Allocation for Energy and Execution Time Trade-Off in Edge Computing Using Multi-Objective IPSO},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {2},
PAGES = {2989--3011},
URL = {http://www.techscience.com/cmc/v84n2/62860},
ISSN = {1546-2226},
ABSTRACT = {As mobile edge computing continues to develop, the demand for resource-intensive applications is steadily increasing, placing a significant strain on edge nodes. These nodes are normally subject to various constraints, for instance, limited processing capability, a few energy sources, and erratic availability being some of the common ones. Correspondingly, these problems require an effective task allocation algorithm to optimize the resources through continued high system performance and dependability in dynamic environments. This paper proposes an improved Particle Swarm Optimization technique, known as IPSO, for multi-objective optimization in edge computing to overcome these issues. To this end, the IPSO algorithm tries to make a trade-off between two important objectives, which are energy consumption minimization and task execution time reduction. Because of global optimal position mutation and dynamic adjustment to inertia weight, the proposed optimization algorithm can effectively distribute tasks among edge nodes. As a result, it reduces the execution time of tasks and energy consumption. In comparative assessments carried out by IPSO with benchmark methods such as Energy-aware Double-fitness Particle Swarm Optimization (EADPSO) and ICBA, IPSO provides better results than these algorithms. For the maximum task size, when compared with the benchmark methods, IPSO reduces the execution time by 17.1% and energy consumption by 31.58%. These results allow the conclusion that IPSO is an efficient and scalable technique for task allocation at the edge environment. It provides peak efficiency while handling scarce resources and variable workloads.},
DOI = {10.32604/cmc.2025.062451}
}