ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Networks and Communications
A new Gaussian Black-winged Kite Algorithm for Task Scheduling Optimization of Industrial IoT Applications in a Fog Computing Environment
Provisionally accepted
Rania Mahmoud Eisa1
Hussein Karam Abd El-Sattar2*Haitham Farouk Abd El fatah1Fatima Omara Abd El-Sattar3- 1Suez University, Suez, Egypt
- 2Faculty of Science, Ain Sham University, Cairo, Egypt
- 3Cairo University Faculty of Computers and Artificial Intelligence, Giza, Egypt
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As a result of the increase in industrial Internet of Things (IoT) applications, fog computing (FC) has become a major area of research. A decentralized computing system called fog computing extends cloud computing to the network's edge. The cloud allows for real-time insights and analysis by processing and storing enormous volumes of data produced by IoT devices. Consequently, the task scheduling technique in cloud computing is crucial. A number of metrics, such as makespan, resource utilization, and energy consumption, must be optimized for FC to function efficiently. This paper proposes a novel metaheuristic optimization technique called the Gaussian Black-winged Kite Algorithm (GBKA) to address task scheduling optimization of industrial IoT applications in a fog computing environment. The proposed algorithm employs Gaussian mutation, and the migration patterns and attack style of the black-winged kite serve as the inspiration for the proposed GBKA. The algorithm is designed to balance exploration of the search space and exploitation of the best solutions, avoiding local optima and improving energy efficiency. The Google Cloud Jobs dataset (GoCJ) with varying task sizes is used to validate the proposed algorithm. An analysis has been conducted to compare the performance of the proposed algorithm with the standard Black-winged Kite Algorithm (BKA) and metaheuristic algorithms like Dragonfly Algorithm (DA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). Experimental results show that GBKA reduces energy and makespan by an average of 7.26% and 9.32%, respectively. Additionally, it attains optimal resource utilization with an average overall improvement of 8.54%.
Keywords: energy consumption, fog computing, Internet of Things (IoT), makespan, Metaheuristic algorithms, Optimization problem, resource utilization, task scheduling
Received: 06 Nov 2025; Accepted: 02 Feb 2026.
Copyright: © 2026 Eisa, Abd El-Sattar, Abd El fatah and Abd El-Sattar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hussein Karam Abd El-Sattar
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