Task and resource allocation in the internet of things based on an improved version of the moth-flame optimization algorithm

【Author】 Nematollahi, Masoud; Ghaffari, Ali; Mirzaei, A.

【Source】CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS

【影响因子】2.303

【Abstract】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%.

【Keywords】Internet of things; Fog computing; Task offloading; Resource allocation; Efficiency

【发表时间】2023 2023 JUN 5

【收录时间】2023-06-29

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