Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing

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.

[1]  S. M. Kamruzzaman,et al.  A New Data Mining Scheme Using Artificial Neural Networks , 2011, Sensors.

[2]  Won Cheol Lee,et al.  Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain , 2021, Sensors.

[3]  Kishor P. Patil,et al.  A Survey of Artificial Neural Network based Spectrum Inference for Occupancy Prediction in Cognitive Radio Networks , 2020, 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184).

[4]  Amandeep Kaur,et al.  A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks , 2020, J. Exp. Theor. Artif. Intell..

[5]  Khaled Ben Letaief,et al.  Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices , 2016, IEEE Journal on Selected Areas in Communications.

[6]  Dave Cavalcanti,et al.  Coexistence challenges for heterogeneous cognitive wireless networks in TV white spaces , 2011, IEEE Wireless Communications.

[7]  David Wang,et al.  Feature scaling , 2018, Radiopaedia.org.

[8]  Naofal Al-Dhahir,et al.  Secure and Energy Efficient Transmission for RSMA-Based Cognitive Satellite-Terrestrial Networks , 2021, IEEE Wireless Communications Letters.

[9]  Simon Fong,et al.  A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation , 2019, Sensors.

[10]  Yasir Saleem,et al.  Primary radio user activity models for cognitive radio networks: A survey , 2014, J. Netw. Comput. Appl..

[11]  Olga Galinina,et al.  Benefits of Positioning-Aided Communication Technology in High-Frequency Industrial IoT , 2018, IEEE Communications Magazine.

[12]  Ilyong Chung,et al.  Spectrum mobility in cognitive radio networks , 2012, IEEE Communications Magazine.

[13]  Bin Le,et al.  Building a Cognitive Radio: From Architecture Definition to Prototype Implementation , 2007 .

[15]  Tao Zhu,et al.  A Survey of Data Semantization in Internet of Things , 2018, Sensors.

[16]  Janusz Dudczyk,et al.  Data fusion in the decision-making process based on artificial neural networks , 2020 .

[17]  Filippo Tosato,et al.  Reliable energy-efficient spectrum management and optimization in cognitive radio networks: how often should we switch? , 2013, IEEE Wireless Communications.

[18]  Sajal K. Das,et al.  Self-coexistence in cellular cognitive radio networks based on the IEEE 802.22 standard , 2013, IEEE Wireless Communications.

[19]  A. Murphy,et al.  Activation function , 2019, Radiopaedia.org.

[20]  Abd-Elhamid M. Taha,et al.  A survey of access management techniques in machine type communications , 2014, IEEE Communications Magazine.

[21]  Kani,et al.  PIXEL DOWNSAMPLING FOR OPTIMIZATION OF ARTIFICIAL NEURAL NETWORK FOR HANDWRITING CHARACTER RECOGNITION , 2017 .

[22]  Andreas Mitschele-Thiel,et al.  Spectrum handoff reduction for cognitive radio ad hoc networks , 2010, 2010 7th International Symposium on Wireless Communication Systems.

[23]  D. W. Yun,et al.  Interference Analysis for Mutual Coexistence between LTE TDD in Spatial and Time Domain , 2017, J. Commun..

[24]  Bin Gu,et al.  Modeling for spectrum handoff based on secondary users with different priorities in cognitive radio networks , 2012, 2012 International Conference on Wireless Communications and Signal Processing (WCSP).

[25]  Lajos Hanzo,et al.  Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications , 2018, IEEE Communications Surveys & Tutorials.

[26]  Jiayuan Wang,et al.  A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data , 2021, Frontiers in Energy Research.

[27]  Chang-Joo Kim,et al.  Channel management in IEEE 802.22 WRAN systems , 2010, IEEE Communications Magazine.

[28]  G. Hancke,et al.  Autonomous Interference Mapping for Industrial Internet of Things Networks Over Unlicensed Bands: Identifying Cross-Technology Interference , 2021, IEEE Industrial Electronics Magazine.

[29]  Youssef Nasser,et al.  Recent advances on artificial intelligence and learning techniques in cognitive radio networks , 2015, EURASIP J. Wirel. Commun. Netw..

[30]  Xiongwen Zhao,et al.  Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT , 2020, IEEE Internet of Things Journal.