Multi Sensor Time Series

Multi-sensor time series analysis focuses on extracting meaningful information from data streams collected by multiple sensors over time, aiming to improve accuracy and efficiency in various applications. Current research emphasizes developing robust algorithms, such as convolutional neural networks and hyperdimensional computing methods, to handle challenges like noisy data, distribution shifts across different sensor deployments, and computational limitations on edge devices. These advancements are crucial for improving applications ranging from structural health monitoring and human activity recognition to optimizing resource utilization in Internet of Things deployments and enhancing data security in cyber-physical systems.