Localization and sensing applications in the Pushpin Computer Network

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 117-124).The utility and purpose of a node in a wireless sensor network is intimately tied to the physical space in which it is distributed. As such, it is advantageous under most circumstances for a sensor node to know its position. In this work, we present two systems for localizing a network of roughly 60 sensor nodes distributed over an area of 1-m2. One is based on a linear lateration technique, while the second approach utilizes non-linear optimization techniques, namely spectral graph drawing and mesh relaxation. In both cases, localization is accomplished by generating distance constraints based on ultrasound time-of-flight measurements to distinct, global sensor stimuli. These distance constraints alone are sufficient to achieve localization; no a priori knowledge of sensor node coordinates or the coordinates of the global sensor events are required. Using this technique, we have achieved a localization error of 2.30-cm and an error standard deviation of 2.36-cm.by Michael Joseph Broxton.M.Eng.and S.B

Network Localization by Shadow Edges

Localization is a fundamental task for sensor networks. Traditional network construction approaches allow to obtain localized networks requiring the nodes to be at least tri-connected (in 2D), i.e., the communication graph needs to be globally rigid. In this paper we exploit, besides the information on the neighbors sensed by each robot/sensor, also the information about the lack of communication among nodes. The result is a framework where the nodes are required to be bi-connected and the communication graph has to be rigid. This is possible considering a novel typology of link, namely Shadow Edges, that account for the lack of communication among nodes and allow to reduce the uncertainty associated to the position of the nodes.Comment: preprint submitted to 2013 European Control Conference, July 17-19 2013, Zurich, Switzerlan

Large-Scale Sensor Network Localization via Rigid Subnetwork Registration

In this paper, we describe an algorithm for sensor network localization (SNL) that proceeds by dividing the whole network into smaller subnetworks, then localizes them in parallel using some fast and accurate algorithm, and finally registers the localized subnetworks in a global coordinate system. We demonstrate that this divide-and-conquer algorithm can be used to leverage existing high-precision SNL algorithms to large-scale networks, which could otherwise only be applied to small-to-medium sized networks. The main contribution of this paper concerns the final registration phase. In particular, we consider a least-squares formulation of the registration problem (both with and without anchor constraints) and demonstrate how this otherwise non-convex problem can be relaxed into a tractable convex program. We provide some preliminary simulation results for large-scale SNL demonstrating that the proposed registration algorithm (together with an accurate localization scheme) offers a good tradeoff between run time and accuracy.Comment: 5 pages, 8 figures, 1 table. To appear in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, April 19-24, 201