Abstract
Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we propose the implementation of intelligent dynamic real-time spectrum resource management through the application of data mining and case-based reasoning, which reduces the complexity of existing intelligent dynamic spectrum resource management and enables efficient real-time resource allocation. In this case, data mining and case-based reasoning analyze the activity patterns of incumbent users using vast amounts of sensing data from industrial IoT and enable rapid resource allocation, making use of case DB classified by case. In this study, we confirmed a number of optimization engine operations and spectrum resource management capabilities (spectrum handoff, handoff latency, energy consumption, and link maintenance) to prove the effectiveness of the proposed intelligent dynamic real-time spectrum resource management. These indicators prove that it is possible to minimize the complexity of existing intelligent dynamic spectrum resource management and maintain efficient real-time resource allocation and reliable communication; also, the above findings confirm that our method can achieve a superior performance to that of existing spectrum resource management techniques.
Highlights
Based on the recent development of the Internet of Things (IoT), a paradigm shift is rapidly occurring in computing technology, creating the ability to effectively process large amounts of data generated by various industrial IoT devices [1]
Compared to the performance of 255 IoT devices when the number of channel searches was less than 500 and the number of time slots that could be used continuously had increased by more than seven, it was confirmed that the link maintenance performance was improved by more than 90%, which is very similar to the value of the existing intelligent dynamic spectrum resource management performance
We proposed a method for intelligent dynamic real-time spectrum resource management using data mining and case-based reasoning to overcome the complexity of existing methods and the difficulty of rapid resource allocation
Summary
Based on the recent development of the Internet of Things (IoT), a paradigm shift is rapidly occurring in computing technology, creating the ability to effectively process large amounts of data generated by various industrial IoT devices [1]. -This presents some spectrum management challenges, in terms of managing use of different frequency bands and allowing GSO and NGSO satellite systems to operate simultaneously, while mitigating the risk of harmful interference This in turn results frequent spectrum handoffs in dynamic spectrum environments that require machine type device (MTD) operation in real time, which may lead to serious performance degradation owing to interference with other homogeneous networks. A reasoning engine uses the backup channel list to perform a spectrum handoff operation to another idle channel, enabling continuous communication without interfering with incumbent users At this time, when interference is affected in incumbent users existing in adjacent channels and locations, transmission parameters that can coexist between neighboring networks are dynamically reconstructed through an optimization engine using interference analysis and genetic algorithms. Artificial neural networks (ANNs) to analyze the activity patterns of incumbent users from historic data and compares and analyzes the prediction accuracy and spectrum resource management performance based on the number of IoT devices involved
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