Affiliation:
1. Indian Institute of Science, Bangalore, India
Abstract
Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes in a region of Euclidean space. Following deployment, the nodes self-organize into a mesh topology with a key aspect being
self-localization
. Having obtained a mesh topology in a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this work, we analyze this approximation through two complementary analyses. We assume that the mesh topology is a random geometric graph on the nodes; and that some nodes are designated as
anchors
with known locations. First, we obtain high probability bounds on the Euclidean distances of
all
nodes that are
h
hops away from a fixed anchor node. In the second analysis, we provide a heuristic argument that leads to a direct approximation for the density function of the Euclidean distance between two nodes that are separated by a hop distance
h
. This approximation is shown, through simulation, to very closely match the true density function.
Localization algorithms that draw upon the preceding analyses are then proposed and shown to perform better than some of the well-known algorithms present in the literature. Belief-propagation-based message-passing is then used to further enhance the performance of the proposed localization algorithms. To our knowledge, this is the first usage of message-passing for hop-count-based self-localization.
Funder
Ministry of Defence, Government of India
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Reference36 articles.
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3. Distributed weighted-multidimensional scaling for node localization in sensor networks
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