Efficient Task Offloading Using Ant Colony Optimization and Reptile Search Algorithms in Edge Computing for Things Context

Abstract: The widespread use of Internet of Things (IoT) technology has triggered unparalleled data creation and processing needs, necessitating effective computation offloading solutions. Conventional edge computing approaches have difficulties in dealing with rising energy usage issues and task allocation delays. This study introduces a novel hybrid metaheuristic algorithm called ACO-RSA, which synergizes two metaheuristic algorithms, Ant Colony Optimization (ACO) and Reptile Search Algorithm (RSA). The proposed approach addresses the energy and latency issues associated with offloading computations in IoT edge computing environments. A comprehensive system design that effectively encapsulates the uplink transmission communication model and a personalized multi-user computing task load model is developed. The system considers various constraints, such as network latency, task complexity, and available computing resources. Based on this, we formulate an optimization objective suitable for computing outsourcing in the IoT ecosystem. Simulations conducted in a real-world IoT scenario demonstrate that ACO-RSA significantly reduces both time delay and energy consumption compared to benchmark algorithms, achieving up to 27.6% energy savings and 25.4% reduction in time delay. ACO-RSA exhibits robustness and scalability when optimizing task offloading in IoT edge computing environments.

Ting Zhang and Xiaojie Guo. “Efficient Task Offloading Using Ant Colony Optimization and Reptile Search Algorithms in Edge Computing for Things Context”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.9 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150960

@article{Zhang2024,
title = {Efficient Task Offloading Using Ant Colony Optimization and Reptile Search Algorithms in Edge Computing for Things Context},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150960},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150960},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Ting Zhang and Xiaojie Guo}
}