Adaptive Consensus Averaging for Information Fusion over Sensor Networks

Talebi, Majid; Kefayati, M.; Khalaj, Babak Hossein; Rabiee, Hamid R.

doi:10.1109/mobhoc.2006.278610

Cited by 28 publications

(11 citation statements)

References 11 publications

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“…Some of the applications of the consensus problem include: sensor and information fusion (Talebi et al 2006;Xiao et al 2005), coordination of groups of robots (OlfatiSaber 2006;Tanner et al 2003aTanner et al , 2003bJadbabaie et al 2003) and coordinated decision making (Bauso et al 2003;Alanyali et al 2004). A review of consensus problem on cooperation of robots is presented by Ren et al (2007).…”

Section: Consensus Problemsmentioning

confidence: 99%

Distributed orientation agreement in a group of robots

Navarro

Matı́a

2012

Auton Robot

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In this article, a method for the agreement of a set of robots on a common reference orientation based on a distributed consensus algorithm is described. It only needs that robots detect the relative positions of their neighbors and communicate with them. Two different consensus algorithms based on the exchange of information are proposed, tested and analyzed. Systematic experiments were carried out in simulation and with real robots in order to test the method. Experimental results show that the robots are able to agree on the reference orientation under certain conditions. Scalability with an increasing number of robots was tested successfully in simulation with up to 49 robots. Experiments with real robots succeeded proving that the proposed method works in reality.

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Section: Consensus Problemsmentioning

confidence: 99%

Distributed orientation agreement in a group of robots

Navarro

Matı́a

2012

Auton Robot

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“…In [15,16,17], the authors compute the average of the sensor measurements combined with local Kalman filtering and/or mobile agents. The works developed in [14,18,19] consist of distributed linear iterations, where each sensor updates its current state by a weighted fusion of its current neighbors' states (which are distorted when they reach it) and these fusion weights decrease to zero in an appropriate way, as time progresses. Other authors consider some practical issues in sensor networks such as fault tolerance and asynchronism.…”

Section: Introductionmentioning

confidence: 99%

“…Other authors consider some practical issues in sensor networks such as fault tolerance and asynchronism. For instance, some works compute the average while taking into account link failures [20], other works study the consensus problem into asynchronous environment [21,22] while considering communication delays, or from the energy point of view by minimizing the number of iterations [19].…”

Section: Introductionmentioning

confidence: 99%

Self-stabilizing Consensus Average Algorithm in Distributed Sensor Networks

Bahi

Haddad

Hakem

et al. 2013

Transactions on Large-Scale Data- And Knowledge-Centered Systems IX

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Abstract. One important issue in sensor networks that has received renewed interest recently is average consensus, i.e., computing the average of n sensor measurements, where nodes iteratively exchange data with their neighbors and update their own data accordingly until reaching convergence to the right parameters estimate. In this paper, we introduce an efficient self-stabilizing algorithm to achieve/ensure the convergence of node states to the average of the initial measurements of the network. We prove that the convergence of the fusion process is finite and express an upper bound of the actual number of moves/iterations required by the algorithm. This means that our algorithm is guaranteed to reach a stable situation where no load will be sent from one sensor node to another. We also prove that the load difference between any two sensor nodes in the network is within< ε, where ε is the prescribed global equilibrium threshold (this threshold is given by the system) and D is the diameter of the network.

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“…Some approaches like [15], [13], [14] compute the average of the sensor measurements combined with local Kalman filtering and/or mobile agents. Other works consist of distributed linear iterations, where each sensor updates its current state by a weighted fusion of its current neighbors' states (which are distorted when they reach it) and these fusion weights decrease to zero in an appropriate way, as time progresses [12], [20], [18].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, some works compute the average while taking into account link failures [11], other works study the consensus problem into asynchronous environment [2], [4] while considering communication delays, or from the energy point of view by minimizing the number of iterations [18]. To the best of our knowledge, none of these approaches is able to successfully integrate all sensor networks constraints in their studies especially the fact of node failures [16], [8], [17], [21], [5] and the design of an interesting analytical bound of the actual number of moves/iterations required by the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A New Reliable and Self-Stabilizing Data Fusion Scheme in Unsafe Wireless Sensor Networks

Bahi

Haddad

Hakem

et al. 2010

2010 International Conference on Parallel and Distributed Computing, Applications and Technologies

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In this paper, we deal with the problem of distributed data fusion in unsafe large-scale sensor networks. Data fusion application is the phase of processing the collected data by sensor nodes before sending it the end user. During this phase, resource failures are more likely to occur and can have an adverse effect on the application. Hence, we introduce first an efficient self-stabilizing algorithm to achieve/ensure the convergence of node states to the average of the initial measurements of the network. Next, we present a fault tolerant scheme to resist to frequent and unexpected not concomitant fail-silent/fail-stop node failures. The major contribution of this paper is the design of an analytical expression (an upper bound) of the actual number of moves/iterations required by the algorithm. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed schemes.

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Adaptive Consensus Averaging for Information Fusion over Sensor Networks

Cited by 28 publications

References 11 publications

Distributed orientation agreement in a group of robots

Distributed orientation agreement in a group of robots

Self-stabilizing Consensus Average Algorithm in Distributed Sensor Networks

A New Reliable and Self-Stabilizing Data Fusion Scheme in Unsafe Wireless Sensor Networks

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