# A Kernel-based Learning Approach to Ad Hoc Sensor Network Localization

### XuanLong Nguyen, Michael I. Jordan and Bruno Sinopoli

###
EECS Department

University of California, Berkeley

Technical Report No. UCB/CSD-04-1319

April 2004

### http://www.eecs.berkeley.edu/Pubs/TechRpts/2004/CSD-04-1319.pdf

We show that the coarse-grained and fine-grained localization problems for ad hoc sensor networks can be posed and solved as a pattern recognition problem using kernel methods from statistical learning theory. This stems from an observation that the kernel function, which is a similarity measure critical to the effectiveness of a kernel-based learning algorithm, can be naturally defined in terms of the signal strength received by the sensors. Thus we work in the natural coordinate system provided by the physical devices. This not only allows us to sidestep the difficult ranging procedure required by many existing localization algorithms in the literature, but also enables us to derive a simple and effective localization algorithm. The algorithm is particularly suitable for networks with densely distributed sensors, most of whose locations are unknown. The computations are initially performed at the base sensors and the computation cost depends only on the number of base sensors. The localization step for each sensor of unknown location is then performed locally in linear time. We present an analysis of the localization error bounds, and provide an evaluation of our algorithm on both simulated and real sensor networks.

BibTeX citation:

@techreport{Nguyen:CSD-04-1319, Author = {Nguyen, XuanLong and Jordan, Michael I. and Sinopoli, Bruno}, Title = {A Kernel-based Learning Approach to Ad Hoc Sensor Network Localization}, Institution = {EECS Department, University of California, Berkeley}, Year = {2004}, Month = {Apr}, URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2004/5614.html}, Number = {UCB/CSD-04-1319}, Abstract = {We show that the coarse-grained and fine-grained localization problems for ad hoc sensor networks can be posed and solved as a pattern recognition problem using kernel methods from statistical learning theory. This stems from an observation that the kernel function, which is a similarity measure critical to the effectiveness of a kernel-based learning algorithm, can be naturally defined in terms of the signal strength received by the sensors. Thus we work in the natural coordinate system provided by the physical devices. This not only allows us to sidestep the difficult ranging procedure required by many existing localization algorithms in the literature, but also enables us to derive a simple and effective localization algorithm. The algorithm is particularly suitable for networks with densely distributed sensors, most of whose locations are unknown. The computations are initially performed at the base sensors and the computation cost depends only on the number of base sensors. The localization step for each sensor of unknown location is then performed locally in linear time. We present an analysis of the localization error bounds, and provide an evaluation of our algorithm on both simulated and real sensor networks.} }

EndNote citation:

%0 Report %A Nguyen, XuanLong %A Jordan, Michael I. %A Sinopoli, Bruno %T A Kernel-based Learning Approach to Ad Hoc Sensor Network Localization %I EECS Department, University of California, Berkeley %D 2004 %@ UCB/CSD-04-1319 %U http://www.eecs.berkeley.edu/Pubs/TechRpts/2004/5614.html %F Nguyen:CSD-04-1319