Urban mobility undergoes a profound decline in the aftermath of a disaster, subsequently
exhibiting a complex recovery trajectory. Effectively capturing and predicting this dynamic …

Fine-grained air pollution map reconstruction is critical for urban pollution management and
healthy building construction. Recent advancements in air pollution measurement, such as …

We address the challenges in achieving optimal Quality of Information (QoI) for non-
dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally …

The use of cloud computing, big data, IoT, and mobile applications in the public
transportation industry has resulted in the generation of vast and complex data, of which the …

Low-cost sensors (LCS) are crucial for high-resolution air quality monitoring (AQM), but their
inherent noise poses challenges for wide-scale adoption. Typically, LCS are calibrated …

Mobile air pollution sensing methods are developed to collect air quality data with higher
spatial-temporal resolutions. However, existing methods cannot process the spatially mixed …

MAV (Micro Aerial Vehicle) requires landing on a docking platform for recharging during or
after missions due to their limited energy capacity. Inspired by biological tactile sensing, we …

Although modern localization methods have achieved remarkable accuracy with various
sensors, there are still some circumstances where only proprioceptive sensing works …

With the rise of Artificial Intelligence of Things (AIoT), integrating deep neural networks
(DNNs) into mobile and embedded devices has become a significant trend, enhancing the …

Event cameras excel in high-speed and high-dynamic-range scenarios but are highly
sensitive to rain, which introduces significant noise while also revealing detailed rain …