Fundamental Analyses of Collaborative and Noncollaborative Positioning
Determining the locations of devices in mobile ad-hoc networks (MANETs), wireless sensor networks (WSNs), and cellular networks has many important applications. In MANETs, which are useful in disaster recovery, rescue operations, and military communications, location information is used to enable location-aided routing and geodesic packet forwarding. In WSNs, whose applications include environmental monitoring (e.g., for precision agriculture) and asset tracking in warehouses, not only is location information useful for the self-organization of the network, but in addition, tying locations to the sensor observations is crucial for adding meaning to the sensed data. In cellular networks, location information is used to provide subscribers with location-based services in addition to providing public service answering points with potentially life-saving location information during emergency calls. These applications are largely not new, which is evidenced by the fact that the literature is quite rich with localization studies presented over the span of many years. Because of this, it may be surprising to learn that there is a lack of analyses concerning the fundamental factors impacting localization performance.
Fundamentally, localization performance depends upon three factors: (i) the number of devices participating in the localization procedure, (ii) the locations of the participating devices, and (iii) the quality of the positioning observations gathered from the participating devices. For the most part, these factors cannot reasonably be considered deterministic. Instead, at any point in time, random effects within a network and its surroundings will determine these factors for individual positioning scenarios. Unfortunately, there are currently no analytical approaches for characterizing localization performance over these random factors. Instead, researchers either provide analytical results for a deterministic set of factors or use complex system-level simulations to obtain general performance insights. While the latter certainly averages over the random factors, the validity of the results is limited by the simulation assumptions. Any change in a network parameter requires running a new time-consuming simulation.
In this dissertation, we address current deficiencies in several ways. We present a new model for tractably analyzing network localization fundamentals. This is demonstrated through fundamental analyses of hearability and geometry. Further, collaboration among non-reference devices has recently garnered increasing interest from the research community as a means to (i) improve positioning accuracy and (ii) improve positioning availability. We present fundamental analyses of both of these potential benefits. As a result of our work, we not only characterize several key performance metrics, we also demonstrate that there exist new tractable ways to analyze localization performance.