Position and Movement Detection of Wireless Sensor Network Devices Relative to a Landmark Graph

Abstract

We present a novel probabilistic framework for reliable indoor positioning of mobile sensor network devices. Compared to existing approaches, ours adopts complex computations in exchange for high localization accuracy while needing low hardware investment and moderate set-up cost. To that end, we use full distributional information on signal measurements at a set of discrete locations, termed landmarks. Positioning of a mobile device is done relative to the resulting landmark graph and the device can be found near a landmark or in the area between two landmarks. Key elements of our approach include profiling the signal measurement distributions over the coverage area using a special interpolation technique; a two-tier statistical positioning scheme that improves efficiency by adding movement detection; and joint clusterhead placement optimization for both localization and movement detection. The proposed system is practical and has been implemented using standard wireless sensor network hardware. Experimentally, our system achieved an accuracy equivalent to less than 5 meters with 95 percent success probability and less than 3 meters with an 87 percent success probability. This performance is superior to well-known contemporary systems that use similar low-cost hardware.

Formation Detection with Wireless Sensor Networks

“…The present work essentially extends the line of work in Ray et al [2006], Paschalidis and Guo [2009], and Li et al [2012] to the more complex problem of formation detection. The key salient difference of our present work from the localization work in Ray et al [2006], Paschalidis and Guo [2009], and Li et al [2012] is that localization utilizes the (marginal) pdf of measurements from a single sensor at a set of receivers whereas formation detection needs the joint pdf of measurements from multiple sensors at a single receiver.…”

“…The discretization of formations is in line with our earlier sensor localization work [Ray et al 2006;Paschalidis and Guo 2009;Li et al 2012]. It makes the detection/classification problem more tractable but introduces the requirement that the techniques to be used should be robust enough and tolerant to mild or moderate perturbations.…”

“…To that end, one can introduce a threshold on the value of the likelihood and declare a new posture if that value drops below the threshold. Such a test has similar optimality guarantees as GLT (see the analysis of movement detection in Li et al [2012]). MSVM, on the other hand, partitions the feature space into subregions and is not suited to detecting novel postures.…”

“…The key salient difference of our present work from the localization work in Ray et al [2006], Paschalidis and Guo [2009], and Li et al [2012] is that localization utilizes the (marginal) pdf of measurements from a single sensor at a set of receivers whereas formation detection needs the joint pdf of measurements from multiple sensors at a single receiver. As we will see, this requires several innovations including appropriate procedures for pdf estimation and interpolation.…”

“…The techniques we develop are also applicable in detecting (and controlling) the formation of robot swarms deployed in the interior of a building. In indoor deployments, the mapping from RSSI to distance is erratic and unpredictable, requiring the more sophisticated classification or hypothesis testing techniques we develop (see also Ray et al [2006], Paschalidis and Guo [2009], and Li et al [2012]). …”