Mobile air pollution sensing methods have emerged to collect air quality data with improved spatial and temporal resolutions. However, existing methodologies struggle to effectively process spatially mixed gas samples due to the highly dynamic fluctuations experienced by sensors, resulting in significant measurement deviations. We identify an opportunity to address this issue by exploring potential patterns within sensor measurements. To this end, we propose CatUA, a novel city-scale fine-grained air quality estimation system designed to deliver accurate mobile air quality data. First, we design AirBERT, a representation learning model specifically aimed at discerning mixed gas concentrations from sensor data. Second, we implement a Prompt-informed Training Strategy that leverages extensive unlabeled and minimal labeled city-scale data to enhance the performance of CatUA. Notably, the Auto-Prompt mechanism allows CatUA to conveniently acquire new knowledge tailored to specific downstream tasks. To ensure the practicality of CatUA, we have invested considerable effort in developing the software stack on our meticulously crafted Sensing Front-end, which has successfully gathered city-scale air quality data for over 1,200 hours. Experiments conducted on the collected data demonstrate that CatUA reduces sensing errors by 96.9% with a latency of only 44.9ms, outperforming the state-of-the-art baseline by 42.6%.

As unmanned aerial vehicles (UAVs) become more prevalent in smart cities, their capacity for visual language navigation (VLN) is garnering increasing interest. VLN in cities has significant applications in delivery, rescue, and security patrol, among other fields. One of the most representative tasks is to navigate to specific locations following the language instructions. While some current methods have achieved notable results in indoor settings, challenges persist outdoors, including agents’ inaccurate spatial understanding and ambiguous language instructions. In this work, we explore an embodied navigation agent design, in which a fine-grained spatial verbalizer and a history path memory are proposed to guarantee accurate VLN in open 3D urban environments.

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 propose a proprioceptive sensing system that allows MAV to "touch", recognize, and locate the landing platform even when visual or other positioning systems are not functioning properly. We leverage a physical phenomenon: as the MAV approaches a beneath obstacle, it experiences attitude disturbances caused by the airflow generated by the rotor’s reflections from the ground. By employing traditional signal processing and learning-based techniques to analyze signals from the IMU (Inertial Measurement Unit) and motors, the MAV can sense the edges of the platform and further calculate the precise landing coordinates. With a power consumption of less than 40 mW, our system achieves an edge detection error of less than 2 cm and a landing success rate exceeding 90%.CCS CONCEPTS• Applied computing → Aerospace; • Computing methodologies → Machine learning approaches; • Computer systems organization → Sensors and actuators.

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 gas samples effectively due to the highly dynamic temporal and spatial fluctuations experienced by the sensor, leading to significant measurement deviations. We find an opportunity to tackle the problem by exploring the potential patterns from sensor measurements. In light of this, we propose MobiAir, a novel city-scale fine-grained air quality estimation system to deliver accurate mobile air quality data. First, we design AirBERT, a representation learning model to discern mixed gas concentrations. Second, we design a knowledge-informed training strategy leveraging massive unlabeled city-scale data to enhance the AirBERT performance. To ensure the practicality of MobiAir, we have invested significant efforts in implementing the software stack on our meticulously crafted Sensing Front-end, which has successfully gathered air quality data at a city-scale for more than 1200 hours. Experiments conducted on collected data show that MobiAir reduces sensing errors by 96.7% with only 44.9ms latency, outperforming the SOTA baseline by 39.5%.

Human-machine interface (HMI) of high degrees of freedom (DoF) is one of the most critical bases of the metaverse. The ideal HMI for the metaverse should be cheap, robust, customizable, and ergonomically friendly. In light of this, we propose a triboelectric nanogenerator (TENG)-based sensing system. We developed a low-cost, soft, light, and customizable TENG sensor to collect data from the human body. We then used an artificial neural network (ANN) to obtain the corresponding human motion from collected sensory data. The effectiveness of the proposed system is demonstrated with experiments of a working prototype.

Fine-grained air pollution data is essential for smart living and efficient city management. However, it is arduous to obtain accurate air pollution data with high spatial and temporal resolutions via mobile crowdsensing (MCS) under limited budgets. Thus, we propose FAD, a system fully using fine-grained air pollution data to provide diverse services. Moreover, a low-cost yet highly accurate portable sensing device is designed for MCS applications to enhance data resolutions. Finally, we demonstrate various FAD-based services for citizens and governments in the real world.

CO2 concentration data with high resolution in large venues is highly required during indoor sport events for in-time environment adjustment to guarantee the athlete performances and audience experience. However, the limited battery energy of the wireless sensors cannot support high data resolution and long time coverage simultaneously. Besides, there also lacks effective embedded methods to clean anomaly data caused by the human and environmental factors probably occurring in large venues. Thus, in this paper, we propose C-RIDGE, a low-power sensing system for high resolution CO2 data collection in large venues. Based on prior knowledge, firstly, an adaptive sampling rate adjustment policy is developed for lower energy consumption to extend the time coverage of data. Secondly, CO2 physical property (CPP) aided data cleaning algorithm is designed to improve data quality as well, using Pearson Correlation Coefficient (PCC) and standard deviation with sliding windows. C-RIDGE has been deployed in one venue during a world-class event. The experiments and collected data have shown the system power consumption can be reduced by 36.1%, with measurement error less than 10.2%. The outliers and anomaly trends can also be detected and calibrated effectively via CPP algorithm. The dataset is available at https://doi.org/10.5281/zenodo.7160830.