Improving Offloading in IIoT with Awareness of Energy and the Age of Information by Reinforcement Genetic Algorithm

Abstract :

With the increasing use of Internet of Things in daily life and especially in industry, improving efficiency and delay time with the help of data offloading is one of the goals of these issues. Controlling these factors will improve energy consumption and longer use of things batteries. In this article, the method is introduced to improve sensor data processing and edge and cloud computing in industrial Internet of Things systems. The architecture is considered in accordance with the real world; in this architecture, edge servers with computing capabilities at the edge of the network, especially Used in base stations. Delay-sensitive requests can be forwarded to nearby edge servers through wireless channels, thereby reducing traffic in the core network and user data transmission latency, especially for data-intensive industrial applications. In the Industrial Internet of Things aims to manage network resources, transfer calculations and minimize energy consumption in Internet of Things devices by guaranteeing the freshness of sensor data. The network environment and input tasks are variable with time. In this article, the environment of the problem and its limitations are expressed with formulas. This problem has been solved using the proposed genetic algorithm and reinforcement learning. The proposed solution has improved the dynamic environment of the problem for offloading data and tasks by considering energy and transferring calculations and data by considering their freshness. The results show an average improvement of 40% compared to the previous methods.

References:

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