Wireless perception allocation method based on edge computing in cognitive Internet of Things
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DOI: 10.25236/icceme.2024.025
Author(s)
Guoqiang Wang, Qiang Wu, Zhanjun Hao
Corresponding Author
Zhanjun Hao
Abstract
With the rapid increase in the number of Internet of Things (IoT) devices, the traditional wireless sensing allocation method faces challenges in network delay, data processing capacity and energy consumption. Therefore, edge computing (EC) technology is introduced in this study. By deploying EC nodes between the sensing layer and the network layer, the local preprocessing and intelligent distribution of sensing data are realized, so as to optimize the data processing and transmission process. The system model is based on the basic architecture of cognitive IoT, including perception layer, network layer and application layer, among which EC node plays a key role between perception layer and network layer. This paper proposes an innovative wireless sensing allocation method, which realizes the efficient allocation and processing of sensing data by comprehensively considering the load and energy efficiency of EC nodes. Firstly, this method collects environmental data through the perception layer, and uses the computing power of EC nodes to preprocess the original data. Then, according to the optimization objectives and constraints, the data allocation strategy is formulated to ensure that the perceived data is processed and analyzed in the shortest time, and at the same time, the system throughput is improved and energy consumption is reduced. The simulation results show that compared with the traditional methods, the method proposed in this study significantly reduces the data processing time, improves the system throughput, and achieves a significant reduction in energy consumption. This achievement not only demonstrates the potential of EC technology in wireless sensing distribution of IoT, but also provides an energy-saving and efficient solution for the practical application of IoT.
Keywords
Internet of Things; Wireless perception allocation; edge computing