Proposal for managing sensor selection through the integration of trust for indoor tracking systems

With the proliferation of sensors, specifically those capable of providing positional estimates, it is now possible to build large sensor networks for the purpose of tracking objects within indoor environments. A key component of building such indoor tracking systems, is the ability to properly select a subset of these available sensors for the purpose of tracking. Often sensors do not afford the luxury of a priori mapping or calibration, specifically mobile sensors, and thus the resulting positional data may prove to be untrustworthy or unreliable. Current indoor tracking efforts do not consider trust and reliability as separate criteria for the sensor selection process. This identification and further classification of both trust and reliability is a challenging task due to the unpredictable and unknown nature of both the sensors and the physical environment in which they reside. This proposal defines trust and reliability in the scope of indoor tracking, attempts to quantify both parameters, provides a framework to improve the sensor subset selection process through the inclusion of these parameters, and makes use of an objective function to evaluate the tradeoff between accuracy and cost. We will make use of an existing indoor tracking system to empirically evaluate this proposal.