Sensor Systems and Signal Processing Algorithms for Wireless Applications

• Other Authors: Mukherjee, Avishek (author)
• Format: Others
• Language: English; English
• Published: Florida State University
• Subjects: Computer science
• Online Access: http://purl.flvc.org/fsu/fd/2018_Su_Mukherjee_fsu_0071E_14750

The demand for high performance wireless networks and systems have become increasingly high over the last decade. This dissertation addresses three systems that were designed to improving the efficiency, reliability and security of wireless systems. To improve the efficiency and reliability of wireless systems, we propose two algorithms, namely CSIFit and CSIApx, to compress the Channel State Information (CSI) of Wi-Fi networks with Orthogonal Frequency Division Multiplexing (OFDM) and Multiple Input Multiple Output (MIMO). We evaluated these systems with both experimental and synthesized CSI data. Our work on CSIApx confirmed that we can achieve very good compression ratios with very little loss accuracy, at a fraction of the complexity needed in current state-of-the-art compression methods. The second system is sensor based application to reliably detect falls inside homes. A automatic fall detection system has tremendous value to the well-being of seniors living alone. We design and implement MultiSense, a novel fall detection system, which has the following desirable features. First, it does not require the human to wear any device, therefore is convenient to seniors. Second, it has been tested in typical settings including living rooms and bathrooms, and has shown very good accuracy. Third, it is built with inexpensive components, with expected hardware cost around $150 to cover a typical room. MultiSense does not require any training data and is comparatively non-invasive than similar systems. Our evaluation showed that MultiSense achieved no False Negatives, i.e., was able to detect falls accurately each time, while producing no False Positives in a daily use test. Therefore, we believe MultiSense can be used to accurately detect human falls and can be extremely helpful to seniors living alone. Lastly, TBAS is a spoof detection method designed to improve the security of wireless networks. TBAS is based on two facts: 1) different transmitting locations likely result in different wireless channels, and 2) the drift in channel state information within a short time interval should be bounded. We proposed and implemented TBAS on Microsoft's SORA platform as well as commodity wireless cards and tested it's performance in typical Wi-Fi environments with different levels of channel mobility. Our results show that TBAS can be very accurate when running on 3 by 2 systems and above, i.e., TBAS on MIMO has a very low false positive error ratio, where a false positive event occurs when two packets from the same user are misclassified as from different users, while also maintaining a very low false negative ratio of 0.1%, where a false negative event occurs when two packets from different users are misclassified as from the same user. We believe our experimental findings can be used as a guideline for future systems that will deploy TBAS. === A Dissertation submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy. === Summer Semester 2018. === July 12, 2018. === channel state, networks, security, wireless === Includes bibliographical references. === Zhenghao Zhang, Professor Directing Dissertation; Ming Yu, University Representative; Piyush Kumar, Committee Member; Xiuwen Liu, Committee Member.