Multi-objective optimization for integrated task scheduling and load balancing in fog computing for IoT applications - Rafique, Majid (2025)
Fog Computing acts as a bridge between cloud infrastructure and the Internet of Things (IoT) devices, including mobile devices, sensors, and smart technologies. By locating itself closer to edge devices, fog computing enhances data processing. This proximity reduces latency, energy consumption, and communication costs while improving real-time response capabilities. Even though 5G technology has significantly improved connectivity and data transmission rates, the increasing number of IoT devices creates processing challenges. This study focuses on a Fog Optimized Computing System (FOCS) algorithm, which is developed to handle network congestion and processing challenges imposed by the increasing number of IoT devices. By using a task scheduling and offloading method, the FOCS algorithm arranges data according to size and sends it to the appropriate fog nodes. High-capacity fog nodes get large data packets, while lower-capacity nodes receive smaller packets. During times of network congestion, FOCS uses load-balancing mechanisms to ensure that data is transmitted to the closest accessible fog nodes. The FOCS algorithm seeks to improve system performance through the reduction of latency, stabilization of energy consumption and communication costs. By utilizing the Eclipse IDE for implementation and the Cloudsim Toolkit for analysis, the efficiency of the FOCS algorithm will be evaluated, focusing on how effectively it optimizes latency, energy consumption, and cost. FOCS outperforms current techniques in comparative studies conducted under the same simulated conditions, further validating its effectiveness. The results demonstrate that the proposed FOCS algorithm considerably boosts performance through effective task distribution among fog nodes, reduces latency, energy conservation, and utilization cost.