Matrix-completion approach to mobile network localization

Localization in mobile networks is of paramount importance to a variety of pervasive applications. Due to the limitations of GPS, such as high deployment cost, many researchers have devised a variety of different localization schemes based on the measurements of connectivity or distance between neighboring nodes. The existing schemes suffer seriously from either low localization precision or overlong computation time. In this paper, we present a novel localization scheme based on matrix completion, MALL, that utilizes the collected connectivity and distance information to achieve high-precision localization. Since MALL only involves convex optimization and low-complexity non-convex optimization, it can localize mobile nodes at a fast pace. Furthermore, MALL leads to low communication cost. Through intensive simulation and testbed experiments, we found that MALL outperforms the state-of-the-art localization schemes. An in-depth analysis of the time complexity and communication cost of MALL is also included in this paper.