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Iterative Localization of Wireless Sensor Networks:
An Accurate and Robust Approach
ABSTRACT:
In wireless sensor networks, an important research problem is to use a few
anchor nodes with known locations to derive locations of other nodes
deployed in the sensor field. A category of solutions for this problem is the
iterative localization, which sequentially merges the elements in a network
to finally locate them. Here, a network element is different from its
definition in iterative trilateration. It can be either an individual node or a
group of nodes. For this approach, we identify a new problem called
inflexible body merging, whose objective is to align two small network
elements and generate a larger element. It is more generalized than the
traditional tools of trilateration and patch stitching and can replace them
as a new merging primitive. We solve this problem and make the following
contributions. 1) Our primitive can tolerate ranging noise when merging
two network elements. It adopts an optimization algorithm based on rigid
body dynamics and relaxing springs. 2) Our primitive improves the
robustness against flip ambiguities. It uses orthogonal regression to detect
the rough collinearity of nodes in the presence of ranging noise, and then
enumerate flip ambiguities accordingly. 3) We present a condition to
indicate when we can apply this primitive to align two network elements.
This condition can unify previous work and thus achieve a higher
percentage of localizable nodes. All the declared contributions have been
validated by both theoretical analysis and simulation results.

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EXISTING SYSTEM:
We identify a new problem called inflexible body merging, whose objective
is to align two small network elements and generate a larger element. It is
more generalized than the traditional tools of trilateration and patch
stitching and can replace them as a new merging primitive. we adopt the
fine-grained ranging techniques to obtain the accurate measurements of
internode distances. For fine-grained localization, the existing solutions are
divided into two categories: whole-topology approach and iterative
approach. The former approach analyzes the whole network topology
directly, using some numerical optimization algorithms. For example,
anchor-free localization (AFL) method models the network topology as a
group of nodes interconnected by springs. The spring forces acting on the
nodes are used as heuristics that guide the nodes’ movement toward their
lowest energy positions.
PROPOSED SYSTEM:
We have proposed an optimization algorithm that can tolerate ranging
noise when aligning two bodies. This algorithm is very generalized and can
replace the traditional multilateration and patch stitching as the new
primitive for network localization. We also proposed a flip ambiguity
Enumeration algorithm to improve the robustness of localization. This
algorithm can discover the rough collinearity of constraints due to the
interference of ranging noise.

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CONCLUSION:
This paper focused on fine-grained iterative localization of wireless sensor
networks. We have proposed an optimization algorithm that can tolerate
ranging noise when aligning two bodies. This algorithm is very generalized
and can replace the traditional multilateration and patch stitching as the
new primitive for network localization. We also proposed a flip ambiguity
enumeration algorithm to improve the robustness of localization. This
algorithm can discover the rough collinearity of constraints due to the
interference of ranging noise. contribution is that our body merging
condition can achieve higher localization percentage than state-of-the-art
SWEEPS and CALL. Our final demonstration shows that our
IIBMlocalization algorithm can work well, even in concave networks with
non-uniform network density.