Browsing by Author "Chuah, Mooi Choo"

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    Enabling Self-healing Smart Grid Through Jamming Resilient Local Controller Switching
    (IEEE, 2015-09) Liu, Hongbo; Chen, Yingying; Chuah, Mooi Choo; Yang, Jie; Poor, H. Vincent; Department of Computer and Information Science, School of Science
    A key component of a smart grid is its ability to collect useful information from a power grid for enabling control centers to estimate the current states of the power grid. Such information can be delivered to the control centers via wireless or wired networks. It is envisioned that wireless technology will be widely used for local-area communication subsystems in the smart grid (e.g., in distribution networks). However, various attacks with serious impact can be launched in wireless networks such as channel jamming attacks and denial-of-service attacks. In particular, jamming attacks can cause significant damages to power grids, e.g., delayed delivery of time-critical messages can prevent control centers from properly controlling the outputs of generators to match load demands. In this paper, a communication subsystem with enhanced self-healing capability in the presence of jamming is designed via intelligent local controller switching while integrating a retransmission mechanism. The proposed framework allows sufficient readings from smart meters to be continuously collected by various local controllers to estimate the states of a power grid under various attack scenarios. The jamming probability is also analyzed considering the impact of jammer power and shadowing effects. In addition, guidelines on optimal placement of local controllers to ensure effective switching of smart meters under jamming are provided. Via theoretical, experimental and simulation studies, it is demonstrated that our proposed system is effective in maintaining communications between smart meters and local controllers even when multiple jammers are present in the network.
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    WiFi-Enabled Smart Human Dynamics Monitoring
    (ACM, 2017-11) Guo, Xiaonan; Liu, Bo; Shi, Cong; Liu, Hongbo; Chen, Yingying; Chuah, Mooi Choo; Computer and Information Science, School of Science
    The rapid pace of urbanization and socioeconomic development encourage people to spend more time together and therefore monitoring of human dynamics is of great importance, especially for facilities of elder care and involving multiple activities. Traditional approaches are limited due to their high deployment costs and privacy concerns (e.g., camera-based surveillance or sensor-attachment-based solutions). In this work, we propose to provide a fine-grained comprehensive view of human dynamics using existing WiFi infrastructures often available in many indoor venues. Our approach is low-cost and device-free, which does not require any active human participation. Our system aims to provide smart human dynamics monitoring through participant number estimation, human density estimation and walking speed and direction derivation. A semi-supervised learning approach leveraging the non-linear regression model is developed to significantly reduce training efforts and accommodate different monitoring environments. We further derive participant number and density estimation based on the statistical distribution of Channel State Information (CSI) measurements. In addition, people's walking speed and direction are estimated by using a frequency-based mechanism. Extensive experiments over 12 months demonstrate that our system can perform fine-grained effective human dynamic monitoring with over 90% accuracy in estimating participants number, density, and walking speed and direction at various indoor environments.