Qiang Li, PhD Dissertation Presentation

Wednesday, April 12, 2017 at 7:30 am in Rice 536


Committee: John A. Stankovic (Advisor), Kamin Whitehouse (Chair), Jack Davidson, Gabriel Robins, and John Lach (Minor Representative)

Title: Reliable Multi-Body, Multi-Function Body Sensor Networks


With the cost of health care increasing, Body Sensor Networks (BSNs) have been proposed to provide low-cost health care solutions for the senior population. Reliability is essential for BSNs because they are mainly used for medical purposes such as detecting health-detrimental accidents and monitoring health status. The reliability of BSNs is twofold: reliable communication and reliable applications. The former focuses on data collection, while the latter focuses on data processing. Though existing work studies some of the reliability issues, the state of art lacks systematic approaches to implement reliable BSNs, especially when multiple BSNs coexist.

Therefore, in this thesis we systematically study how to develop reliable multi-body, multi-function BSNs. First, we perform an empirical study to investigate the challenges for developing reliable BSNs. Then, we proposed a QoS framework that guarantees reliable communication and fidelity of BSN applications. Reliable communication is achieved by dynamically grouping nearby BSNs into one group and using different frequency channels and time sharing to schedule transmission within each group. Fidelity of BSNs is guaranteed by profiling BSN platforms and applications, and finding the optimal configuration to accommodate multiple BSN systems. At last, we develop two representative BSN applications, fall detection (accident detection) and social activity detection (health monitoring), to study how to develop reliable BSN applications using proposed framework.

BSN systems implemented using our framework achieves overall Packet Reception Ratio (PRR) of over 97% even when multiple BSNs are within range of interference, while PRR of systems not considering nearby BSNs drops below 50% when BSNs coexist in our experiment. Profiling BSNs and automatically switching between configurations guarantee data fidelity in terms of sample frequency and sample delivery. Our fall detection application detects over 96% of falls and our in-person interaction monitoring application achieves over 82% accuracy.

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