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Modeling and Mitigation of Faults in Cyber-Physical Systems with Binary Sensors

by Volkan Gunes, Steffen Peter, Tony Givargis - in Proc. of the 16th IEEE International Conference on Computational Science and Engineering (CSE , 2013
"... Abstract — This paper presents an analysis framework for correct system operation (i.e. system success) of Cyber-Physical Systems (CPS) that deploy binary sensors with possible faults. We discuss potential faults in the interface part of such systems and address solutions for those faults in order t ..."
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Abstract — This paper presents an analysis framework for correct system operation (i.e. system success) of Cyber-Physical Systems (CPS) that deploy binary sensors with possible faults. We discuss potential faults in the interface part of such systems and address solutions for those faults in order to build dependable and reliable CPS applications. As a practical tool, we present a set of models in SIMULINK to help system designers extend simulations of general CPS applications that deploy binary sensor networks. We provide methodologies to add well-defined fault behaviors and offer assessment tools to measure the effects of possible faults on the overall system success. We demonstrate the feasibility of our contributions using a CPS application and explore various architectures for fault mitigation in a holistic design space exploration environment. With the ability to help system designers analyze and assess a non-trivial design space, the presented approach contributes to the design of fault-tolerant CPSs.
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ABSTRACT OF THE DISSERTATION Learning Human Contexts through

by Chenren Xu, Yanyong Zhang, Chenren Xu, Chenren Xu, Yanyong Zhang , 2014
"... Learning human contexts is critical to the development of many applications, ranging from healthcare, business, to social sciences. Most existing work, however, acquires contextual information in an obtrusive manner – they may require subjects to carry mobile devices, or rely on self or peer report ..."
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Learning human contexts is critical to the development of many applications, ranging from healthcare, business, to social sciences. Most existing work, however, acquires contextual information in an obtrusive manner – they may require subjects to carry mobile devices, or rely on self or peer report to report data. In this dissertation, we present two unobtrusive techniques that can help us learn important human contex-tual information including count, location, trajectory, and speech characteristics. We first present SCPL, a radio frequency-based device-free localization technique. SCPL is able to count how many people are in an indoor setting and track their locations by observing how they disturb the wireless radio links in the environment. Second, we present Crowd++, a smartphone-based speech sensing technique, which records a conversation and automatically counts the number of people in the conversation with-out prior knowledge of their speech characteristics. Both techniques are unobtrusive, low-cost, and private, which can thus enable a large array of important applications that rely upon the knowledge of human contextual information. ii
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Device-Free People Counting and Localization

by Chenren Xu
"... Abstract Device-free passive (DfP) localization has been proposed as an emerging technique for localizing people, without requiring them to carry any devices. Potential applications include elder-care, security enforcement, building occupancy statistics, etc. We first present PC-DfP, an accurate an ..."
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Abstract Device-free passive (DfP) localization has been proposed as an emerging technique for localizing people, without requiring them to carry any devices. Potential applications include elder-care, security enforcement, building occupancy statistics, etc. We first present PC-DfP, an accurate and efficient RF-based device-free localization solution. PC-DfP adopts a stochastic fingerprinting approach to mitigate the error caused by the multipath and meanwhile minimize the system calibration overhead. Second, we present SCPL, a RF-based device-free people counting and localization technique. SCPL takes the calibration data collected with one person and the map information to accurately count people sequentially and localize them in parallel. Finally we present Crowd++, an unsupervised speaker counting technique through audio inference with smartphones to estimate the number of people in social hotspot places.
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FindingHuMo: Real-Time User Tracking in Smart Environments with Anonymous Binary Sensing

by Debraj De, Wen-zhan Song, Mingsen Xu, Cheng-liang Wang, Diane Cook, Xiaoming Huo
"... Abstract—We will demo FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Envi-ronments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) bi ..."
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Abstract—We will demo FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Envi-ronments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. It can solve complex challenges in multi-user tracking where user motion trajectories may crossover with each other in all different ways. We will demo the evaluation of tracking performance by feeding sensor data from our designed Smart Environment Simulator to Find-ingHuMo and then comparing the tracking output with ground truth. We will also demo live user tracking of a smart workplace environment. I.
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