LOCALE: Collaborative localization estimation for sparse mobile sensor networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

58 Scopus citations

Abstract

As the field of sensor networks matures, research in this area is focusing not only on fixed networks, but also on mobile sensor networks. For many reasons, both technical and logistical, such networks will often be very sparse for all or part of their operation, sometimes functioning more as disruption-tolerant networks (DTNs). While much work has been done on localization methods for densely populated fixed networks, most of these methods are inefficient or ineffective for sparse mobile networks, where connections can be infrequent. While some mobile networks rely on fixed location beacons or per-node, onboard GPS, these methods are not always possible due to cost, power and other constraints. In this paper we present the Low-density Collaborative Ad-Hoc Localization Estimation (LOCALE) system for sparse sensor networks. In LOCALE, each node estimates its own position, and collaboratively refines that location estimate by updating its prediction based on neighbors it encounters. Nodes also estimate (as a probability density function) the likelihood their prediction is accurate. We evaluate LOCALE'S collaborative localization both through real implementations running on sensor nodes, as well as through simulations of larger systems. We consider scenarios of varying density (down to 0.02 neighbors per communication attempt), as well as scenarios that demonstrate LOCALE'S resilience in the face of extremely-inaccurate individual nodes. Overall, our algorithms yield up to a median of 21X better accuracy for location estimation compared to existing approaches. In addition, by allowing nodes to refine location estimates collaboratively, LOCALE also reduces the need for fixed location beacons (i.e. GPS-enabled beacon towers) by as much as 64X.

Original languageEnglish (US)
Title of host publicationProceedings - 2008 International Conference on Information Processing in Sensor Networks, IPSN 2008
Pages195-206
Number of pages12
DOIs
StatePublished - Sep 16 2008
Event2008 International Conference on Information Processing in Sensor Networks, IPSN 2008 - St. Louis, MO, United States
Duration: Apr 22 2008Apr 24 2008

Publication series

NameProceedings - 2008 International Conference on Information Processing in Sensor Networks, IPSN 2008

Other

Other2008 International Conference on Information Processing in Sensor Networks, IPSN 2008
CountryUnited States
CitySt. Louis, MO
Period4/22/084/24/08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Electrical and Electronic Engineering

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