Learning Sensor Location from Signal Strength and Connectivity

Received signal strength (RSS) or connectivity, i.e., whether or not two devices can communicate, are two relatively inexpensive (in terms of device and energy costs) measurements at the receiver that indicate the distance from the transmitter. Such measurements can either be quickly dismissed as too unreliable for localization, or idealized by ignoring the non-circular nature of a transmitter’s coverage area. This chapter finds a middle ground between these two extremes by using measurement-based statistical models to represent the inaccuracies of RSS and connectivity. While a particular RSS or connectivity measurement may be hard to predict, a statistical model for RSS and connectivity can in fact be wellcharacterized. Many numerical examples are used to provide the reader with intuition about the variability of real-world RSS and connectivity measurements. This chapter then gives a description of three sensor localization algorithms which are based on ‘manifold learning’, a class of non-linear dimension reduction methods. These algorithms include Isomap [1], the distributed weighted multi-dimensional scaling (dwMDS) algorithm [2], and the Laplacian Eigenmap adaptive neighbor (LEAN) algorithm [3]. The performance of these estimators is compared via simulation using the RSS and connectivity measurement models. The results show that while RSS and connectivity measurements are highly variable, these manifold learning-based algorithms demonstrate their robustness by achieving location estimates with low bias and often variance close to the lower bound. Due to their desirability as low cost, low complexity mea-