6,305 research outputs found
Approximate conditional distributions of distances between nodes in a two-dimensional sensor network
When we represent a network of sensors in Euclidean space by a graph, there
are two distances between any two nodes that we may consider. One of them is
the Euclidean distance. The other is the distance between the two nodes in the
graph, defined to be the number of edges on a shortest path between them. In
this paper, we consider a network of sensors placed uniformly at random in a
two-dimensional region and study two conditional distributions related to these
distances. The first is the probability distribution of distances in the graph,
conditioned on Euclidean distances; the other is the probability density
function associated with Euclidean distances, conditioned on distances in the
graph. We study these distributions both analytically (when feasible) and by
means of simulations. To the best of our knowledge, our results constitute the
first of their kind and open up the possibility of discovering improved
solutions to certain sensor-network problems, as for example sensor
localization
Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages
This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times
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