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AutoWitness | ||||
Project GoalThe goal of this project is to build a scalable, yet affordable, wireless sensor network system that can be used for large scale tracking of assets such as lost or stolen objects. ResultsIn an ACM SenSys 2010 paper, we present AutoWitness, a system
to deter, detect, and track personal property theft, improve
historically dismal stolen property recovery rates, and disrupt stolen
property distribution networks. A property owner embeds a small tag
inside the asset to be protected, where the tag lies dormant until it
detects vehicular movement. Once moved, the tag
uses inertial sensor-based dead reckoning to estimate position
changes, but to reduce integration errors, the relative position is
reset whenever the sensors indicate the vehicle has stopped. The
sequence of movements, stops, and turns are logged in compact form and
eventually transferred to a server using a cellular modem after both
sufficient time has passed (to avoid detection) and RF power is
detectable (hinting cellular access may be available). Eventually, the
trajectory data are sent to a server which attempts to match a path to
the observations. The algorithm uses a Hidden Markov Model of city
streets and Viterbi decoding to estimate the most likely path. The
proposed design leverages low-power radios and inertial sensors, is
immune to intransit cloaking, and supports post hoc path
reconstruction. A picture of the AutoWitness tag appears on the right. Also, on the right, an overview of the operation of the AutoWitness is depicted in a diagram. Finally, two pictures on the right show the reconstruction of two paths in the city of Memphis where AutoWitness tag was driven. The AutoWitness system is to be deployed for real-life tracking of burglars in the city of Memphis in early 2011. Given the high incidence of burgalries (e.g., more than 2
million reported annually in the U.S. alone that account for over $17
billion in losses), we expect the AutoWitness systems to improve the
quality of life by addressing property crimes. Team MembersLead Faculty Member: Dr. Santosh Kumar Collaborator: Dr. Prabal Dutta, EECS, University of Michigan Post Doctoral FellowsDr. Kurt Plarre (2008-) - On the job marketPh.D. StudentsShantanu Guha (2007-) - sguha(dot)(at)memphis(dot)edu Somnath Mitra (2007-) - smitra3(at)memphis(dot)edu M.S. StudentsBhagavathy Krishna (2007) - bhagavathy(dot)krishna(at)memphis(dot)edu Undergraduate StudentsDaniel Lissner (2009) - dlissner(at)memphis(dot)edu AlumniAnimikh Ghosh (M.S., 2010) - GE Global Research, India SponsorsNational Science Foundation (CISE, CNS, NeTS-NOSS)Fedex Institute of Technology, University of Memphis |
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