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  1. null (Ed.)
    Spot-level parking availability information (the availability of each spot in a parking lot) is in great demand, as it can help reduce time and energy waste while searching for a parking spot. In this article, we propose a crowdsensing system called SpotE that can provide spot-level availability in a parking lot using drivers’ smartphone sensors. SpotE only requires the sensor data from drivers’ smartphones, which avoids the high cost of installing additional sensors and enables large-scale outdoor deployment. We propose a new model that can use the parking search trajectory and final destination (e.g., an exit of the parking lot) of a single driver in a parking lot to generate the probability profile that contains the probability of each spot being occupied in a parking lot. To deal with conflicting estimation results generated from different drivers, due to the variance in different drivers’ parking behaviors, a novel aggregation approach SpotE-TD is proposed. The proposed aggregation method is based on truth discovery techniques and can handle the variety in Quality of Information of different vehicles. We evaluate our proposed method through a real-life deployment study. Results show that SpotE-TD can efficiently provide spot-level parking availability information with a 20% higher accuracy than the state-of-the-art. 
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    Recently, significant efforts are made to explore device-free human activity recognition techniques that utilize the information collected by existing indoor wireless infrastructures without the need for the monitored subject to carry a dedicated device. Most of the existing work, however, focuses their attention on the analysis of the signal received by a single device. In practice, there are usually multiple devices "observing" the same subject. Each of these devices can be regarded as an information source and provides us an unique "view" of the observed subject. Intuitively, if we can combine the complementary information carried by the multiple views, we will be able to improve the activity recognition accuracy. Towards this end, we propose DeepMV, a unified multi-view deep learning framework, to learn informative representations of heterogeneous device-free data. DeepMV can combine different views' information weighted by the quality of their data and extract commonness shared across different environments to improve the recognition performance. To evaluate the proposed DeepMV model, we set up a testbed using commercialized WiFi and acoustic devices. Experiment results show that DeepMV can effectively recognize activities and outperform the state-of-the-art human activity recognition methods. 
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  6. null (Ed.)
    Accurately profiling potholes on road surfaces not only helps eliminate safety related concerns and improve commuting efficiency for drivers, but also reduces unnecessary maintenance cost for transportation agencies. In this paper, we propose a smartphone-based system that is capable of precisely estimating the length and depth of potholes, and introduce a holistic design on pothole data collection, profile aggregation and pothole warning and reporting. The proposed system relies on the built-in inertial sensors of vehicle-carried smartphones to estimate pothole profiles, and warn the driver about incoming potholes. Because of the difference in driving behaviors and vehicle suspension systems, a major challenge in building such system is how to aggregate conflicting sensory reports from multiple participating vehicles. To tackle this challenge, we propose a novel reliability-aware data aggregation algorithm called Reliability Adaptive Truth Discovery (RATD). It infers the reliability for each data source and aggregates pothole profiles in an unsupervised fashion. Our field test shows that the proposed system can effectively estimate pothole profiles, and the RATD algorithm significantly improves the profiling accuracy compared with popular data aggregation methods. 
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