Distributed Localization: A Linear Theory

Fifth-generation (5G) networks providing much higher bandwidth and faster data rates will allow connecting vast number of stationary and mobile devices, sensors, agents, users, machines, and vehicles, supporting Internet-of-Things (IoT), real-time dynamic networks of mobile things. Positioning and location awareness will become increasingly important, enabling deployment of new services and contributing to significantly improving the overall performance of the 5G system. Many of the currently talked about solutions to positioning in 5G are centralized, mostly requiring direct communication to the access nodes (or anchors, i.e., nodes with known locations), which in turn requires a high density of anchors. But such centralized positioning solutions may become unwieldy as the number of users and devices continues to grow without limit in sight. As an alternative to the centralized solutions, this paper discusses distributed localization in a 5G-enabled IoT environment where many low power devices, users, or agents are to locate themselves without a direct access to anchors. Even though positioning is essentially a nonlinear problem (solving circle equations by trilateration or triangulation), we discuss a cooperative linear distributed iterative solution with only local measurements, local communication, and local computation needed at each agent. Linearity is obtained by reparametrization of the agent location through barycentric coordinate representations based on local neighborhood geometry that may be computed in terms of certain Cayley-Menger determinants involving relative local inter-agent distance measurements. After a brief introduction to the localization problem, and other available distributed solutions primarily based on directly addressing the nonlinear formulation, we present the distributed linear solution for stationary agent networks and study its convergence, its robustness to noise, and extensions to mobile scenarios, in which agents, users, and (possibly) anchors are dynamic.

I. Introduction

With fifth-generation (5G) technologies looming on the horizon, there is potential for networking vast numbers of heterogeneous devices, the Internet-of-Things (IoT), not only of stationary devices, but also of moving objects, users, or vehicles [1]–[7]. Location awareness, providing the physical location of every stationary or moving object or agent, will enhance the ability to deploy new services and better management of the overall 5G system. Beyond these, location-aware technologies can also enable a variety of other applications from precision agriculture [8], to intruder detection [9], health care [10], asset tracking, ocean data acquisition [11], or emergency services [12]. For example, location information is essential in providing an effective response in disasters such as fire rescue situations. Other relevant applications include military sensing [13], physical security, industrial and manufacturing automation, and robotics [14]. In addition, localization is essential in randomly deployed networks, where manual positioning of objects is not practical, and the location of network nodes may change during runtime.