526 research outputs found
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN
The localization of the sensor nodes is a fundamental problem in wireless sensor networks.
There are a lot of different kinds of solutions in the literature. Some of them use external
devices like GPS, while others use special hardware or implicit parameters in wireless
communications.
In applications like wildlife localization in a natural environment, where the power available
and the weight are big restrictions, the use of hungry energy devices like GPS or hardware
that add extra weight like mobile directional antenna is not a good solution.
Due to these reasons it would be better to use the localization’s implicit characteristics in
communications, such as connectivity, number of hops or RSSI. The measurement related
to these parameters are currently integrated in most radio devices. These measurement
techniques are based on the beacons’ transmissions between the devices.
In the current study, a novel tracking distributed method, called LIS, for localization of
the sensor nodes using moving devices in a network of static nodes, which have no additional
hardware requirements is proposed.
The position is obtained with the combination of two algorithms; one based on a local
node using a fuzzy system to obtain a partial solution and the other based on a centralized
method which merges all the partial solutions. The centralized algorithm is based on the
calculation of the centroid of the partial solutions.
Advantages of using fuzzy system versus the classical Centroid Localization (CL)
algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for
testing localization algorithms.
With this simulator, it is demonstrated that the proposed method obtains less localization
errors and better accuracy than the centroid algorithm.Junta de AndalucÃa P07-TIC-0247
Modelling and planning reliable wireless sensor networks based on multi-objective optimization genetic algorithm with changeable length
Wireless sensor networks (WSN) have shown their potentials in various applications, which bring a lot of benefits to users from different working areas. However, due to the diversity of the deployed environments and resource constraints, it is difficult to predict the performance of a topology. Besides the connectivity, coverage, cost, network longevity and service quality should all be considered during the planning procedure. Therefore, efficiently planning a reliable WSN is a challenging task, which requires designers coping with comprehensive and interdisciplinary knowledge. A WSN planning method is proposed in this work to tackle the above mentioned challenges and efficiently deploying reliable WSNs. First of all, the above mentioned metrics are modeled more comprehensively and practically compared with other works. Especially 3D ray tracing method is used to model the radio link and sensing signal, which are sensitive to the obstruction of obstacles; network routing is constructed by using AODV protocol; the network longevity, packet delay and packet drop rate are obtained via simulating practical events in WSNet simulator, which to the best of our knowledge, is the first time that network simulator is involved in a planning algorithm. Moreover, a multi-objective optimization algorithm is developed to cater for the characteristics of WSNs. Network size is changeable during evolution, meanwhile the crossovers and mutations are limited by certain constraints to eliminate invalid modifications and improve the computation efficiency. The capability of providing multiple optimized solutions simultaneously allows users making their own decisions, and the results are more comprehensive optimized compared with other state-of-the-art algorithms. Practical WSN deployments are also realized for both indoor and outdoor environments and the measurements coincident well with the generated optimized topologies, which prove the efficiency and reliability of the proposed algorithm
Estimation in Phase-Shift and Forward Wireless Sensor Networks
We consider a network of single-antenna sensors that observe an unknown
deterministic parameter. Each sensor applies a phase shift to the observation
and the sensors simultaneously transmit the result to a multi-antenna fusion
center (FC). Based on its knowledge of the wireless channel to the sensors, the
FC calculates values for the phase factors that minimize the variance of the
parameter estimate, and feeds this information back to the sensors. The use of
a phase-shift-only transmission scheme provides a simplified analog
implementation at the sensor, and also leads to a simpler algorithm design and
performance analysis. We propose two algorithms for this problem, a numerical
solution based on a relaxed semidefinite programming problem, and a closed-form
solution based on the analytic constant modulus algorithm. Both approaches are
shown to provide performance close to the theoretical bound. We derive
asymptotic performance analyses for cases involving large numbers of sensors or
large numbers of FC antennas, and we also study the impact of phase errors at
the sensor transmitters. Finally, we consider the sensor selection problem, in
which only a subset of the sensors is chosen to send their observations to the
FC.Comment: 28 pages, 5 figures, accepted by IEEE Transactions on Signal
Processing, Apr. 201
Review of Optimization Problems in Wireless Sensor Networks
International audienc
Distance Measurement-Based Cooperative Source Localization: A Convex Range-Free Approach
One of the most essential objectives in WSNs is to determine the spatial coordinates
of a source or a sensor node having information. In this study, the problem of range
measurement-based localization of a signal source or a sensor is revisited. The main challenge of the problem results from the non-convexity associated with range measurements
calculated using the distances from the set of nodes with known positions to a xed sen-
sor node. Such measurements corresponding to certain distances are non-convex in two
and three dimensions. Attempts recently proposed in the literature to eliminate the non-
convexity approach the problem as a non-convex geometric minimization problem, using
techniques to handle the non-convexity.
This study proposes a new fuzzy range-free sensor localization method. The method
suggests using some notions of Euclidean geometry to convert the problem into a convex
geometric problem. The convex equivalent problem is built using convex fuzzy sets, thus
avoiding multiple stable local minima issues, then a gradient based localization algorithm
is chosen to solve the problem.
Next, the proposed algorithm is simulated considering various scenarios, including the
number of available source nodes, fuzzi cation level, and area coverage. The results are
compared with an algorithm having similar fuzzy logic settings. Also, the behaviour of
both algorithms with noisy measurements are discussed. Finally, future extensions of the
algorithm are suggested, along with some guidelines
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