Bruno Sinopoli will give a talk.
Title: A kernel-based learning approach to ad hoc sensor network
localization
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.