Privacy-Constrained Location Accuracy in CooperativeWearable Networks in Multi-Floor Buildings

Abstract

<p>This paper proposes a geometric dilution-of-precision approach to quantize the privacy-aware location errors in a cooperative wearable network with opportunistic positioning. The main hypothesis is that, a wearable inside a multi-floor building could localize itself based on cooperative pseudoranges measurements from nearby wearables, as long as the nearby wearables are heard above the sensitivity limit and as long as nearby wearables choose to disclose their own positions. A certain percentage of wearables, denoted by γ, is assumed to not want to disclose their positions in order to preserve their privacy. Our paper investigates the accuracy limits under the privacy constraints with variable γ and according to various building maps and received signal strength measurements extracted from real buildings. The data (wearable positions and corresponding power maps) are synthetically generated using a floor-and-wall path-loss model with statistical parameters extracted from real-field measurements. It is found that the network is tolerant to about 30% of the wearables not disclosing their position (i.e., opting for a full location-privacy mode).</p>