Matrix analysis
Distributed Algorithms
Linear System Theory and Design
Linear System Theory and Design
Geographic gossip: efficient aggregation for sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Norm of the inverse of a random matrix
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Finite-time convergent gradient flows with applications to network consensus
Automatica (Journal of IFAC)
Computing and communicating functions over sensor networks
IEEE Journal on Selected Areas in Communications
Polynomial filtering for fast convergence in distributed consensus
IEEE Transactions on Signal Processing
Accelerated distributed average consensus via localized node state prediction
IEEE Transactions on Signal Processing
Scheduling for finite time consensus
ACC'09 Proceedings of the 2009 conference on American Control Conference
Optimization and analysis of distributed averaging with memory
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Optimization and analysis of distributed averaging with short node memory
IEEE Transactions on Signal Processing
Distributed data association in smart camera networks using belief propagation
ACM Transactions on Sensor Networks (TOSN)
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We study the problem of performing sensor fusion and distributed consensus in networks, where the objective is to calculate some linear function of the initial sensor values at some or all of the sensors. We utilize a linear iteration where, at each time-step, each sensor updates its value to be a weighted average of its own previous value and those of its neighbors. We show that this approach can be viewed as a linear dynamical system, with dynamics that are given by the weight matrix for the linear iteration, and with outputs for each sensor that are captured by the subset of the state vector that is measurable by that sensor. We then cast the fusion and consensus problems as that of observing a linear functional of the initial state vector using only local measurements (that are available at each sensor). When the topology of the network is time-invariant, we show that the weight matrix can be chosen so that each sensor can calculate the desired function as a linear combination of its measurements over a finite number of time-steps.