Open Access

ARTICLE

Energy Aware Task Scheduling of IoT Application Using a Hybrid Metaheuristic Algorithm in Cloud Computing

Ahmed Awad Mohamed¹, Eslam Abdelhakim Seyam^2,*, Ahmed R. Elsaeed³, Laith Abualigah⁴, Aseel Smerat^5,6, Ahmed M. AbdelMouty⁷, Hosam E. Refaat⁸

1 Information System Department, Cairo Higher Institute for Languages and Simultaneous Interpretation, and Administrative Science, Cairo, 11765, Egypt
2 Department of Insurance and Risk Management, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 13318, Saudi Arabia
3 Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
4 Computer Science Department, Al-AlBayt University, Mafraq, 25113, Jordan
5 Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman, 19328, Jordan
6 Centre for Research Impact and Outcome, Chitkara University, Punjab, 140401, India
7 Information System Department, Faculty of Computers and Information, Zagazig University, Zagazig, 44519, Egypt
8 Information System Department, Faculty of Computers and Information, Suez Canal University, Ismailia, 41522, Egypt

* Corresponding Author: Eslam Abdelhakim Seyam. Email:

Computers, Materials & Continua 2026, 86(3), 77 https://doi.org/10.32604/cmc.2025.073171

Received 12 September 2025; Accepted 31 October 2025; Issue published 12 January 2026

Abstract

In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths. Task scheduling is developed using modified artificial ecosystem optimization (AEO), combined with negative swarm operators, Salp Swarm Algorithm (SSA), in order to competitively optimize their capabilities during the exploitation phase of the optimal search process. In addition, the proposed strategy, Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm (EAEOSSA), attempts to find the most suitable solution. The optimization that combines cost and energy for multi-objective task scheduling optimization problems. The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well. A comparison was made between the proposed strategy and other strategies in terms of time, cost, energy, and productivity. Experimental results showed that the proposed strategy improved energy consumption, cost, and time over other algorithms. Simulation results demonstrate that the proposed algorithm increases the average cost, average energy consumption, and mean service time in most scenarios, with average reductions of up to 21.15% in cost and 25.8% in energy consumption.

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Keywords

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