12,038 research outputs found
Consensus Algorithms and Distributed Structure Estimation in Wireless Sensor Networks
abstract: Distributed wireless sensor networks (WSNs) have attracted researchers recently due to their advantages such as low power consumption, scalability and robustness to link failures. In sensor networks with no fusion center, consensus is a process where
all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied.
Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear average consensus algorithm is used. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives guidelines towards how to choose the design parameter for the max estimate. It is also shown that if some prior knowledge of the initial measurements is available, the consensus process can be accelerated.
Secondly, a distributed system size estimation algorithm is proposed. The proposed algorithm is based on distributed average consensus and L2 norm estimation. Different sources of error are explicitly discussed, and the distribution of the final estimate is derived. The CRBs for system size estimator with average and max consensus strategies are also considered, and different consensus based system size estimation approaches are compared.
Then, a consensus-based network center and radius estimation algorithm is described. The center localization problem is formulated as a convex optimization problem with a summation form by using soft-max approximation with exponential functions. Distributed optimization methods such as stochastic gradient descent and diffusion adaptation are used to estimate the center. Then, max consensus is used to compute the radius of the network area.
Finally, two average consensus based distributed estimation algorithms are introduced: distributed degree distribution estimation algorithm and algorithm for tracking the dynamics of the desired parameter. Simulation results for all proposed algorithms are provided.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Split Distributed Computing in Wireless Sensor Networks
We designed a novel method intended to improve the performance of distributed computing in wireless sensor networks. Our proposed method is designed to rapidly increase the speed of distributed computing and decrease the number of the messages required for a network to achieve the desired result. In our analysis, we chose Average consensus algorithm. In this case, the desired result is that every node achieves the average value calculated from all the initial values in the reduced number of iterations. Our method is based on the idea that a fragmentation of a network into small geographical structures which execute distributed calculations in parallel significantly affects the performance
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Likelihood Consensus and Its Application to Distributed Particle Filtering
We consider distributed state estimation in a wireless sensor network without
a fusion center. Each sensor performs a global estimation task---based on the
past and current measurements of all sensors---using only local processing and
local communications with its neighbors. In this estimation task, the joint
(all-sensors) likelihood function (JLF) plays a central role as it epitomizes
the measurements of all sensors. We propose a distributed method for computing,
at each sensor, an approximation of the JLF by means of consensus algorithms.
This "likelihood consensus" method is applicable if the local likelihood
functions of the various sensors (viewed as conditional probability density
functions of the local measurements) belong to the exponential family of
distributions. We then use the likelihood consensus method to implement a
distributed particle filter and a distributed Gaussian particle filter. Each
sensor runs a local particle filter, or a local Gaussian particle filter, that
computes a global state estimate. The weight update in each local (Gaussian)
particle filter employs the JLF, which is obtained through the likelihood
consensus scheme. For the distributed Gaussian particle filter, the number of
particles can be significantly reduced by means of an additional consensus
scheme. Simulation results are presented to assess the performance of the
proposed distributed particle filters for a multiple target tracking problem
A Chemistry-Inspired Framework for Achieving Consensus in Wireless Sensor Networks
The aim of this paper is to show how simple interaction mechanisms, inspired
by chemical systems, can provide the basic tools to design and analyze a
mathematical model for achieving consensus in wireless sensor networks,
characterized by balanced directed graphs. The convergence and stability of the
model are first proven by using new mathematical tools, which are borrowed
directly from chemical theory, and then validated by means of simulation
results, for different network topologies and number of sensors. The underlying
chemical theory is also used to derive simple interaction rules that may
account for practical issues, such as the estimation of the number of neighbors
and the robustness against perturbations. Finally, the proposed chemical
solution is validated under real-world conditions by means of a four-node
hardware implementation where the exchange of information among nodes takes
place in a distributed manner (with no need for any admission control and
synchronism procedure), simply relying on the transmission of a pulse whose
rate is proportional to the state of each sensor.Comment: 12 pages, 10 figures, submitted to IEEE Sensors Journa
Improved Distributed Estimation Method for Environmental\ud time-variant Physical variables in Static Sensor Networks
In this paper, an improved distributed estimation scheme for static sensor networks is developed. The scheme is developed for environmental time-variant physical variables. The main contribution of this work is that the algorithm in [1]-[3] has been extended, and a filter has been designed with weights, such that the variance of the estimation errors is minimized, thereby improving the filter design considerably\ud
and characterizing the performance limit of the filter, and thereby tracking a time-varying signal. Moreover, certain parameter optimization is alleviated with the application of a particular finite impulse response (FIR) filter. Simulation results are showing the effectiveness of the developed estimation algorithm
Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Growing progress in sensor technology has constantly expanded the number and
range of low-cost, small, and portable sensors on the market, increasing the
number and type of physical phenomena that can be measured with wirelessly
connected sensors. Large-scale deployments of wireless sensor networks (WSN)
involving hundreds or thousands of devices and limited budgets often constrain
the choice of sensing hardware, which generally has reduced accuracy,
precision, and reliability. Therefore, it is challenging to achieve good data
quality and maintain error-free measurements during the whole system lifetime.
Self-calibration or recalibration in ad hoc sensor networks to preserve data
quality is essential, yet challenging, for several reasons, such as the
existence of random noise and the absence of suitable general models.
Calibration performed in the field, without accurate and controlled
instrumentation, is said to be in an uncontrolled environment. This paper
provides current and fundamental self-calibration approaches and models for
wireless sensor networks in uncontrolled environments
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