Suining He

Bio: Suining He is an academic researcher from University of Connecticut. The author has contributed to research in topics: Computer science & Fingerprint (computing). The author has an hindex of 16, co-authored 44 publications receiving 1598 citations. Previous affiliations of Suining He include University of Michigan & Hong Kong University of Science and Technology.

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Abstract: The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

Indoor Localization and Automatic Fingerprint Update with Altered AP Signals

Abstract: Wi-Fi fingerprinting has been extensively studied for indoor localization due to its deployability under pervasive indoor WLAN. As the signals from access points (APs) may change due to, for example, AP movement or power adjustment, the traditional approach is to conduct site survey regularly in order to maintain localization accuracy, which is costly and time-consuming. Here, we study how to accurately locate a target and automatically update fingerprints in the presence of altered AP signals (or simply, “altered APs”). We propose L ocalization with A ltered A Ps and F ingerprint U pdating (LAAFU) system, employing implicit crowdsourced signals for fingerprint update and survey reduction. Using novel subset sampling, LAAFU identifies any altered APs and filter them out before a location decision is made, hence maintaining localization accuracy under altered AP signals. With client locations anywhere in the region, fingerprint signals can be adaptively and transparently updated using non-parametric Gaussian process regression. We have conducted extensive experiments in our campus hall, an international airport, and a premium shopping mall. Compared with traditional weighted nearest neighbors and probabilistic algorithms, results show that LAAFU is robust against altered APs, achieving 20 percent localization error reduction with the fingerprints adaptive to environmental signal changes.

Contour-based Trilateration for Indoor Fingerprinting Localization

Abstract: Trilateration has been widely and successfully employed to locate outdoor mobile devices due to its accuracy. However, it cannot be directly applied for indoor localization due to issues such as non-line-of-sight measurement and multipath fading. Though fingerprinting overcomes these issues, its accuracy is often hampered by signal noise and the choice of similarity metric between signal vectors. We propose INTRI, a novel, simple and effective indoor localization technique combining the strengths of trilateration and fingerprinting.For a signal level received from an access point (AP) by the target, INTRI first forms a contour consisting of all the reference points (RPs) of the same signal level for that AP, taking into account the signal noise. The target is hence at the juncture of all the contours. With an optimization formulation following the spirit of trilateration, it then finds the target location by minimizing the distance between the position and all the contours. INTRI further uses an online algorithm based on signal correlation to efficiently calibrate heterogeneous mobile devices to achieve higher accuracy. We have implemented INTRI, and our extensive simulation and experiments in an international airport, a shopping mall and our university campus show that it outperforms recent schemes with much lower location error (often by more than 20%).

Fusing noisy fingerprints with distance bounds for indoor localization

Abstract: Fusing fingerprints with mutual distance information potentially improves indoor localization accuracy. Such distance information may be spatial (e.g., via inter-node measurement) or temporal (e.g., via dead reckoning). Previous approaches on distance fusion often require exact distance measurement, assume the knowledge of distance distribution, or apply narrowly to some specific sensing technology or scenario. Due to random signal fluctuation, wireless fingerprints are inherently noisy and distance cannot be exactly measured. We hence propose Wi-Dist, a highly accurate indoor localization framework fusing noisy fingerprints with uncertain mutual distances (given by their bounds). Wi-Dist is a generic framework applicable to a wide range of sensors (peer-assisted, INS, etc.) and wireless fingerprints (Wi-Fi, RFID, CSI, etc.). It achieves low errors by a convex-optimization formulation which jointly considers distance bounds and only the first two moments of measured fingerprint signals. We implement Wi-Dist, and conduct extensive simulation and experimental studies based on Wi-Fi in our international airport and university campus. Our results show that Wi-Dist achieves significantly better accuracy than other state-of-the-art schemes (often by more than 40%).

Spatio-Temporal Capsule-based Reinforcement Learning for Mobility-on-Demand Network Coordination

Abstract: As an alternative means of convenient and smart transportation, mobility-on-demand (MOD), typified by online ride-sharing and connected taxicabs, has been rapidly growing and spreading worldwide. The large volume of complex traffic and the uncertainty of market supplies/demands have made it essential for many MOD service providers to proactively dispatch vehicles towards ride-seekers. To meet this need effectively, we propose STRide, an MOD coordination-learning mechanism reinforced spatio-temporally with capsules. We formalize the adaptive coordination of vehicles into a reinforcement learning framework. STRide incorporates spatial and temporal distributions of supplies (vehicles) and demands (ride requests), customers' preferences and other external factors. A novel spatio-temporal capsule neural network is designed to predict the provider's rewards based on MOD network states, vehicles and their dispatch actions. This way, the MOD platform adapts itself to the supply-demand dynamics with the best potential rewards. We have conducted extensive data analytics and experimental evaluation with three large-scale datasets (~ 21 million rides from Uber, Yellow Taxis and Didi). STRide is shown to outperform state-of-the-arts, substantially reducing request-rejection rate and passenger waiting time, and also increasing the service provider's profits, often making 30% improvement over state-of-the-arts.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Abstract: The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.