Yazar "Nematollahi, Masoud" seçeneğine göre listele

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    Task and resource allocation in the internet of things based on an improved version of the moth-flame optimization algorithm
    (Springer, 2024) Nematollahi, Masoud; Ghaffari, Ali; Mirzaei, A.
    The Internet of Things (IoT) technology is used to develop a wide range of applications and services, including intelligent healthcare systems and virtual reality applications. Low processing power limits IoT devices' capabilities. It's common practice to use cloud services to do operations that would otherwise require a user's device to be overloaded with data. High latency, high traffic, and high energy consumption remain, though. Given the above concerns, Fog Computing (FC) should be applied in the IoT to speed up time-sensitive data processing and management. In this study, a novel architecture for offloading jobs and allocating resources in the IoT is presented. Sensors, controllers, and FC servers are all part of the upgraded system. The second layer uses the subtask pool approach to offload work and the Moth-Flame Optimization (MFO) algorithm combined with Opposition-based Learning (OBL) to distribute resources. This combination is known as OBLMFO. A stack cache approach is used to complete resource allocation in the second layer to avoid system load imbalance. In addition, the second layer relies on the blockchain to guarantee the accuracy of transaction data. Another way to put it is that the proposed architecture utilizes blockchain advantages to optimize resource distribution in the IoT. The evaluation of the OBLMFO model was done through the Python 3.9 environment, which contains a large variety of distinct jobs. The results show that the OBLMFO model reduced the delay factor by 12.18% and the energy consumed by 6.22%.
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    Task offloading in Internet of Things based on the improved multi-objective aquila optimizer
    (Springer London Ltd, 2024) Nematollahi, Masoud; Ghaffari, Ali; Mirzaei, Abbas
    The Internet of Things (IoT) is a network of tens of billions of physical devices that are all connected to each other. These devices often have sensors or actuators, small microprocessors and ways to communicate. With the expansion of the IoT, the number of portable and mobile devices has increased significantly. Due to resource constraints, IoT devices are unable to complete tasks in full. To overcome this challenge, IoT devices must transfer tasks created in the IoT environment to cloud or fog servers. Fog computing (FC) is a computing paradigm that bridges the gap between the cloud and IoT devices and has lower latency compared to cloud computing. An algorithm for task offloading should have smart ways to make the best use of FC resources and cut down on latency. In this paper, an improved multi-objective Aquila optimizer (IMOAO) equipped with a Pareto front is proposed to task offloading from IoT devices to fog nodes with the aim of reducing the response time. To improve the MOAO algorithm, opposition-based learning (OBL) is used to diversify the population and discover optimal solutions. The IMOAO algorithm has been evaluated by the number of tasks and the number of fog nodes in order to reduce the response time. The results show that the average response time and failure rate obtained by IMOAO algorithm are lower compared to particle swarm optimization (PSO) and firefly algorithm (FA). Also, the comparisons show that the IMOAO model has a lower response time compared to multi-objective bacterial foraging optimization (MO-BFO), ant colony optimization (ACO), particle swarm optimization (PSO) and FA.