17,395 research outputs found
Locating sensors with fuzzy logic algorithms
In a system formed by hundreds of sensors deployed
in a huge area it is important to know the position where every
sensor is.
This information can be obtained using several methods.
However, if the number of sensors is high and the deployment
is based on ad-hoc manner, some auto-locating techniques must
be implemented.
In this paper we describe a novel algorithm based on fuzzy
logic with the objective of estimating the location of sensors
according to the knowledge of the position of some reference
nodes.
This algorithm, called LIS (Localization based on Intelligent
Sensors) is executed distributively along a wireless sensor network
formed by hundreds of nodes, covering a huge area.
The evaluation of LIS is led by simulation tests. The result
obtained shows that LIS is a promising method that can easily
solve the problem of knowing where the sensors are located.Junta de AndalucĂa P07-TIC-0247
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Target Tracking in Confined Environments with Uncertain Sensor Positions
To ensure safety in confined environments such as mines or subway tunnels, a
(wireless) sensor network can be deployed to monitor various environmental
conditions. One of its most important applications is to track personnel,
mobile equipment and vehicles. However, the state-of-the-art algorithms assume
that the positions of the sensors are perfectly known, which is not necessarily
true due to imprecise placement and/or dropping of sensors. Therefore, we
propose an automatic approach for simultaneous refinement of sensors' positions
and target tracking. We divide the considered area in a finite number of cells,
define dynamic and measurement models, and apply a discrete variant of belief
propagation which can efficiently solve this high-dimensional problem, and
handle all non-Gaussian uncertainties expected in this kind of environments.
Finally, we use ray-tracing simulation to generate an artificial mine-like
environment and generate synthetic measurement data. According to our extensive
simulation study, the proposed approach performs significantly better than
standard Bayesian target tracking and localization algorithms, and provides
robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201
Distributed on-line multidimensional scaling for self-localization in wireless sensor networks
The present work considers the localization problem in wireless sensor
networks formed by fixed nodes. Each node seeks to estimate its own position
based on noisy measurements of the relative distance to other nodes. In a
centralized batch mode, positions can be retrieved (up to a rigid
transformation) by applying Principal Component Analysis (PCA) on a so-called
similarity matrix built from the relative distances. In this paper, we propose
a distributed on-line algorithm allowing each node to estimate its own position
based on limited exchange of information in the network. Our framework
encompasses the case of sporadic measurements and random link failures. We
prove the consistency of our algorithm in the case of fixed sensors. Finally,
we provide numerical and experimental results from both simulated and real
data. Simulations issued to real data are conducted on a wireless sensor
network testbed.Comment: 32 pages, 5 figures, 1 tabl
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
Distributed Cooperative Localization in Wireless Sensor Networks without NLOS Identification
In this paper, a 2-stage robust distributed algorithm is proposed for
cooperative sensor network localization using time of arrival (TOA) data
without identification of non-line of sight (NLOS) links. In the first stage,
to overcome the effect of outliers, a convex relaxation of the Huber loss
function is applied so that by using iterative optimization techniques, good
estimates of the true sensor locations can be obtained. In the second stage,
the original (non-relaxed) Huber cost function is further optimized to obtain
refined location estimates based on those obtained in the first stage. In both
stages, a simple gradient descent technique is used to carry out the
optimization. Through simulations and real data analysis, it is shown that the
proposed convex relaxation generally achieves a lower root mean squared error
(RMSE) compared to other convex relaxation techniques in the literature. Also
by doing the second stage, the position estimates are improved and we can
achieve an RMSE close to that of the other distributed algorithms which know
\textit{a priori} which links are in NLOS.Comment: Accepted in WPNC 201
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