Fog-based architecture and efficient task offloading methodology in IoT-based applications for smart irrigation system

• Published in: Computing Vol. 107; no. 3; p. 76
• Main Authors: Sohrabi, Sakine, Sakhaei-nia, Mehdi, Nassiri, Mohamad, Mohammadi, Reza
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.03.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Cloud computing; Computation offloading; Computer Appl. in Administrative Data Processing; Computer Communication Networks; Computer Science; Data transfer (computers); Data transmission; Edge computing; Energy consumption; Energy distribution; Information Systems Applications (incl.Internet); Internet of Things; Irrigation efficiency; Irrigation systems; Network latency; Priority scheduling; Quality of service architectures; Regular Article; Resource management; Resource scheduling; Response time (computers); Simulated annealing; Software Engineering; Task complexity; Task scheduling; Transmission rate (communications); Fog computing; Energy consumption; Jellyfish algorithm; Metaheuristics; Priority; Task scheduling; 68Q85
• ISSN: 0010-485X; 1436-5057
• DOI: 10.1007/s00607-025-01430-1

Fog computing enhances the Quality of Service for Internet of Things applications, addressing issues arising from the significant data transfer volume of Internet of Things devices that leads to increased latency and bandwidth demands in cloud computing. The growth of the Internet of Things generates vast data for processing, straining the fog-cloud network. High data transmission rates can disrupt system operations, necessitating effective resource management. Prioritized tasks are executed through scheduling algorithms, with high-priority tasks executed at nearby fog nodes and complex tasks sent to cloud data centers. While FC reduces response time, it raises energy consumption for users, and vice versa for cloud computing. Addressing these challenges is crucial for optimal task allocation in fog-cloud computing systems. Therefore, this study proposes a hybrid multi-objective task scheduling algorithm using the jellyfish algorithm and simulated annealing algorithm to reduce energy consumption and execution time while respecting task priorities, which play a fundamental role in task distribution. The simulation results confirm the efficiency of the IJFSA algorithm when compared with advanced algorithms such as PWAO, EETSPSO, and IJFA in reducing execution time and energy consumption.