Showing papers by "Neal Patwari published in 2006"
TL;DR: A scalable, distributed weighted-multidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally accounts for communication constraints within the sensor network is introduced.
Abstract: Accurate, distributed localization algorithms are needed for a wide variety of wireless sensor network applications. This article introduces a scalable, distributed weighted-multidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors, updates its position estimate by minimizing a local cost function and then passes this update to neighboring sensors. Derived bounds on communication requirements provide insight on the energy efficiency of the proposed distributed method versus a centralized approach. For received signal-strength (RSS) based range measurements, we demonstrate via simulation that location estimates are nearly unbiased with variance close to the Cramer-Rao lower bound. Further, RSS and time-of-arrival (TOA) channel measurements are used to demonstrate performance as good as the centralized maximum-likelihood estimator (MLE) in a real-world sensor network.
563 citations
12 Jul 2006
TL;DR: The Bayesian Cramer-Rao bound is derived assuming transmit powers are random with known prior distribution and both directional measurements on each link, from i to j and from j to i, and their correlation, are explicitly considered.
Abstract: Sensor localization bounds have been derived assuming that received signal strength (RSS) measurements are performed with perfectly known sensor transmit powers. In this paper the Bayesian Cramer-Rao bound is derived assuming transmit powers are random with known prior distribution. Further, both directional measurements on each link, from i to j and from j to i, and their correlation, are explicitly considered. Results show that random transmit powers have a small (5-13%) impact on coordinate estimation bounds. However, using only the average of the directional measurements can significantly increase these bounds
24 citations
31 Oct 2006
TL;DR: This demonstration will provide an interactive display of distributed, cooperative localization, using wideband received signal-strength measurements, and the distributed weighted multi-dimensional scaling (dwMDS) algorithm.
Abstract: Distributed estimation of sensor location is a key enabling technology for sensor networks. This demonstration will provide an interactive display of distributed, cooperative localization, using wideband received signal-strength measurements, and the distributed weighted multi-dimensional scaling (dwMDS) algorithm.
6 citations