Intelligent Collaborative Control of Multi-source Heterogeneous Data Streams for Low-Power IoT: A Flow Machine Learning Approach

Abstract

LPWAN has partially replaced traditional wired networks in fields such as smart industry, smart healthcare, smart home, etc., due to its low power consumption, high reliability and low cost. LPWAN can achieve long-distance and low-power data transmission without increasing bandwidth, thus meeting the energy efficiency, cost-effectiveness and security requirements of IoT devices. However, low-power IoT also faces some challenges due to design limitations. For example, the reliability of connection under harsh environment and communication interference conditions, and ensuring the long life of devices. To solve this problem, in addition to improving hardware aspects, we also seek to use machine learning methods to make devices run under highly intelligent scheduling conditions, so as to optimize device connection reliability and energy utilization. To this end, this paper proposes a data acquisition, denoising, prediction and transmission optimization method for low-power sensor networks. First, by collecting sound data, video data and light data using temporal flow for modality alignment we achieve data denoising and prediction. Second, by predicting the transmission efficiency of sensors under different temperature humidity and illumination conditions we dynamically adjust sensor power and bandwidth according to transmission loss changes to maximize data transmission efficiency. Finally, we deployed the multimodal Transformer method on edge sensor nodes, combining the data transmitted from image, temperature, and humidity sensors. This approach improved the reliability of data transmission for the sensor devices. Experimental results show that compared with existing methods our proposed method has significant improvement in delay, wavelet denoising efficiency, packet delivery ratio and transmission efficiency.