Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control.

Air pollution is at the center of pollution-control discussion due to the significant adverse health effects on individuals and the environment. Research has shown the association between unsafe environments and different sizes of particulate matter (PM), highlighting the importance of pollutant monitoring to mitigate its detrimental effect. By monitoring air quality with low-cost monitoring devices that collect massive observations, such as Air Box, a comprehensive collection of ground-level PM concentration is plausible due to the simplicity and low-cost, propelling applications in agriculture, aquaculture, and air quality, water resources, and disaster prevention. This paper aims to view IoT-based systems with low-cost microsensors at the sensor, network, and application levels, along with machine learning algorithms that improve sensor networks' precision, providing better resolution. From the analysis at the three levels, we analyze current PM monitoring methods, including the use of sensors when collecting PM concentrations, demonstrate the use of IoT-based systems in PM monitoring and its challenges, and finally present the integration of AI and IoT (AIoT) in PM monitoring, indoor air quality control, and future directions. In addition, the inclusion of Taiwan as a site analysis was illustrated to show an example of AIoT in PM-control policy-making potential directions.

Low-Complexity Privacy-Preserving Compressive Analysis Using Subspace-Based Dictionary for ECG Telemonitoring System.

Compressive sensing (CS) is attractive in long-term electrocardiography (ECG) telemonitoring to extend life-time for resource-constrained wireless wearable sensors. However, the availability of transmitted personal information has posed great concerns for potential privacy leakage. Moreover, the traditional CS-based security frameworks focus on secured signal recovery instead of privacy-preserving data analytics; hence, they provide only computational secrecy and have impractically high complexities for decryption. In this paper, to protect privacy from an information-theoretic perspective while delivering the classification capability, we propose a low-complexity framework of Privacy-Preserving Compressive Analysis (PPCA) based on subspace-based representation. The subspace-based dictionary is used for both encrypting and decoding the CS measurements online, and it is built by dividing signal space into discriminative and complementary subspace offline. The encrypted signal is unreconstructable even if the eavesdropper cracks the measurement matrix and the dictionary. PPCA is implemented in ECG-based atrial fibrillation detection. It can reduce the mutual information by 1.98 bits via encrypting measurements with signal-dependent noise at 1 dB, while the classification accuracy remains 96.05% with the decoding matrix. Furthermore, by decoding via matrix-vector product, rather than sparse coding, this computational complexity of PPCA is 341 times fewer compared with the traditional CS-based security.