Relative location estimation in wireless sensor networks

TLDR

A small fraction of sensors have known locations while the rest must be estimated. The study investigates self‑configuration in wireless sensor networks using the Cramer‑Rao bound. The authors derive CRBs and maximum‑likelihood estimators for TOA and RSS measurements under Gaussian and log‑normal models, validate them with an indoor measurement campaign, and implement the algorithms on a testbed deployed indoors and outdoors. Experiments show 1‑m RMS error with TOA and 1‑to‑2‑m RMS error with RSS.

Abstract

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.