Wireless Sensors and Mobile Ad Hoc Networks (WiSe MANet) Lab

Making a Difference via Sound Research

AutoWitness AutoSense Barrier Coverage Research Grants Publications
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WiSe MANet Group Members (Oct 2009)
At WiSe MANet lab, we strive to make a difference in people’s lives via sound research. In our opinion, a  research work makes an impact if it gets frequently cited (for theoretical work) and/or if it gets used widely in real life (for systems work). We are engaged in both theoretical and systems work. In theoretical research, we are recognized for our work on coverage and connectivity. We introduced two new models of coverage, Barrier Coverage (for intrusion detection) and Trap Coverage (for scalable tracking with provable guarantees). With our Mathematician colleagues, we introduced an analytical technique for deriving reliable estimates for probabilistic events, obviating the need to insist on large network size to make probabilistic guarantess (as is traditionally done in making "with high probability" claims). We applied this technique to derive reliable estimates of density to achieve barrier coverage, full coverage, connectivity, and trap coverage, demonstrating its wide applicability.

In systems research, we are building two novel systems,
AutoWitness and AutoSense. AutoWitness is a burglar tracking system that will help law enforcement agencies in recovering stolen assets. The aim is to detect burglary without an explicit report from the owner, instantly notify law enforcement agency, and most importantly, to provide real-time updates on the current location of assets while en-route, maximizing the chances of timely recovery. The system is expected to last several years on tiny self-contained battery.

AutoSense aims to revolutionize
research in behavioral sciences by enabling continuous, reliable and real time measurements of personal exposures to addictive substances and psychosocial stress as experienced by human subjects in their natural environment. This project is part of the prestigious Genes Environment Initiative (GEI) at the National Institutes of Health (NIH). Our new FieldStream project will provide a theoretical foundation to field deployable wireless sensor systems under development at NIH's GEI program and elsewhere such as AutoSense so that personalized models can be developed and used to automatically validate data collected in the field, to enable timely detection of behavioral events (such as stress, craving, panic attacks, etc.), and to optimize sampling and wireless communication to maximize system lifetime.

Our research is currently supported by three grants from NSF and two grants from NIH. We collaborate with twenty faculty members from eight universities (CMU, Georgia Tech, UCLA, UMass Amherst, University of Minnesota, Ohio State University, University of Pittsburgh, and University of Memphis). Our collaborators span nine different disciplines (Computer Science, Electrical Engineering, Mathematics, Psychology, Behavioral Science, Physiology, Public Health, Anthropology, and Biochemistry), making our projects highly transdisciplinary.

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Lab Location: 222 Dunn Hall, Department of Computer Science, University of Memphis, Memphis, TN 38152