Big Data Analytics for 6G-Enabled Massive Internet of Things

Abstract

The purposes are to enable large-scale Internet of Things (IoT) devices to analyze data more effectively and provide high-efficiency, low-energy, and wide-coverage technical services for terminals. The channel model and energy loss model analyze the devices' access performance, data transmission path delay, energy consumption in the IoT, and large-scale devices' access in the cellular narrowband IoT (NB-IoT) based on big data analysis technology are also discussed. The results show that in the access success rate analysis, the access success rate is the highest with an access time ( T) of 5 s and a preamble resource number ( K) of 25. The restriction factor is inversely proportional to the access success rate. In the node utilization analysis, different transmission node priorities result in different node utilization, and priority 2's node utilization is better than that of priority 1. Moreover, local data makes data analysis and transmission faster. The search time is prolonged, and the corresponding energy consumption is also higher without local data. In the energy consumption analysis, with the 6-generation (6G) technology, different interference thresholds lead to the different energy efficiency of data transmission. The larger the interference threshold, the higher the energy efficiency. Therefore, the 6G-based big data analysis technology can significantly improve large-scale IoT devices' access success rate and enable the system to meet the requirements of low energy consumption and high access success rate, significant for research on more devices' access data analysis.