Abstract
Edge computing offers a promising paradigm for implementing the industrial Internet of things (IIoT) by offloading intensive computing tasks from resource constrained machine type devices to powerful edge servers. However, efficient spectrum resource management is required to meet the quality of service requirements of various applications, taking into account the limited spectrum resources, batteries, and the characteristics of available spectrum fluctuations. Therefore, this study proposes intelligent dynamic spectrum resource management consisting of learning engines that select optimal backup channels based on history data, reasoning engines that infer idle channels based on backup channel lists, and transmission parameter optimization engines based genetic algorithm using interference analysis in time, space and frequency domains. The performance of the proposed intelligent dynamic spectrum resource management was evaluated in terms of the spectrum efficiency, number of spectrum handoff, latency, energy consumption, and link maintenance probability according to the backup channel selection technique and the number of IoT devices and the use of transmission parameters optimized for each traffic environment. The results demonstrate that the proposed method is superior to existing spectrum resource management functions.
Highlights
Based on the recent development of the Internet of things (IoT), a paradigm shift in computing technology for effectively processing the huge amounts of data generated by various IoT devices is rapidly occurring [1]
We propose an intelligent dynamic spectrum resource management structure based on sensing data that can coexist with other cognitive radio (CR) networks in adjacent channel and location while efficiently using limited spectrum resources
In the case of a channel selection method based on the occupancy probability and state transition probability using history data representing past incumbent user activity patterns, channels with low activity can be preferentially selected during the idle channel reasoning process
Summary
Based on the recent development of the Internet of things (IoT), a paradigm shift in computing technology for effectively processing the huge amounts of data generated by various IoT devices is rapidly occurring [1]. A reasoning engine using the backup channel list performs a spectrum handoff operation to another idle channel to enable continuous communication without interfering with incumbent users At this time, efficient spectrum resource management that enables self coexistence between adjacent networks is achieved through an optimization engine when incumbent users existing in adjacent channel and location are affected by interference. By using such a conditional expression, as shown, it is possible to define the activity patterns of incumbent users using zeroes and ones, which represent the state information of the incumbent user from the past to the present These history data support a backup channel selection mechanism that can be characterized by occupancy and state transition probabilities
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