Improving Delay and Energy Consumption in Task Offloading for Fog-Based IoT Networks Using Software-Defined Networks
Subject Areas : electrical and computer engineeringreza khaleghi far 1 , Reza mohammadi 2 * , Mohammad Nassiri 3 , Sakine sohrabi 4
1 - Faculty of Computer Engineering, bu-ali sinaThamedanT iran
2 - Faculty of Computer Engineering, bu-ali sina, hamedan , iran
3 - Faculty of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran
4 - Faculty of Computer Engineering, Bu-ali Sina, Hamedan, Iran
Keywords: Internet of Things (IoT), Firefly Optimization Algorithm, Fog Computing, Software-Defined Networks (SDN).,
Abstract :
The rapid growth of IoT technology has led to the emergence of various latency-sensitive IoT applications. These applications require significant computational resources for real-time processing, resulting in high energy consumption in IoT devices. To address this issue, task offloading using fog computing has emerged as a novel solution. Fog-based task offloading reduces latency and enhances the flexibility of IoT devices. This study proposes a mathematical model aimed at minimizing end-to-end delay and energy consumption for task offloading in IoT-fog networks based on software-defined networking (SDN) infrastructure. The simulation results of the proposed model are compared with two metaheuristic algorithms (Genetic Algorithm and Firefly Algorithm) and a baseline paper, focusing on delay and energy consumption metrics. After implementing the scenario and conducting analysis, the simulation results indicate that the proposed model, using metaheuristic algorithms, achieved approximate average reductions of 18% in delay and 19% in energy consumption.
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