Improving latency and energy in task offloading for fog-based IoT networks using software-define networks

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Improving latency and energy in task offloading for fog-based IoT networks using software-define networks

ارائه دهنده: Provider: Reza Khaleghifar

اساتید راهنما: Supervisors: Dr. Mohammad Nassiri , Dr. Reza Mohammadi

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr. Mahdi Sakhaeinia , Dr. Hatam Abdoli

زمان و تاریخ ارائه: Time and date of presentation: 2022/09/20

مکان ارائه: Place of presentation: Seminar 2 civil

چکیده: Abstract: The rapid growth of IoT technology has led to the emergence of a variety of latency-sensitive IoT applications such as smart healthcare, smart transportation automation, industrial automation, and augmented reality. These applications require significant computing resources for real-time processing, which leads to high energy consumption in IoT devices. To address this issue, offloading tasks using fog computing has emerged as a new solution. The fog computing paradigm suggests deploying resource-rich entities at the edge of the network to execute tasks offloaded from resource-constrained IoT devices. In addition, offloading the task using fog computing reduces the latency and flexibility of IoT devices. In this research, a mathematical model aimed at reducing the end-to-end delay and energy consumption for loading tasks in the Internet of Things and Fog network, which is based on the infrastructure of software-oriented networks. Has been introduced. Then, the proposed model is implemented with two meta-heuristic algorithms of genetics and fire-winged cockroaches and compared with the basic paper based on the criteria of delay, energy consumption, load imbalance rate and productivity. After the implementation of two different scenarios and analysis, the results indicate that the proposed model, using meta-heuristic algorithms, has been able to reduce the delay by 27%, the energy consumption by 24%, and the load imbalance rate by 70%, and increase productivity by 51%

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