Opportunistic Localization: Modeling and Analysis

Localization and tracking functionalities can benefit a number of applications. Despite the large number of algorithms and technologies that have been proposed in this context, the literature still lacks a widely accepted solution, capable of cutting a tradeoff between service quality (i.e., localization accuracy) and device/architecture cost and complexity. In this paper, we tackle the problem from a different and rather new perspective: we investigate how the localization accuracy of nodes can be ameliorated by opportunistically exchanging localization information among heterogeneous nodes that occasionally happen to be in proximity. To this end, we define a simple though accurate opportunistic meeting model and, then, we develop a mathematical framework that permits to analyze the performance of an opportunistic localization strategy based on a Maximum Likelihood argument.