Polynomial filtering for fast convergence in distributed consensus
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Broadcast gossip algorithms for consensus
IEEE Transactions on Signal Processing
On consensus over stochastically switching directed topologies
ACC'09 Proceedings of the 2009 conference on American Control Conference
Adaptive filter algorithms for accelerated discrete-time consensus
IEEE Transactions on Signal Processing
Mean square convergence of consensus algorithms in random WSNs
IEEE Transactions on Signal Processing
A distributed sensor fusion algorithm for the inversion of sparse fields
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Convergence of consensus models with stochastic disturbances
IEEE Transactions on Information Theory
Weight optimization for consensus algorithms with correlated switching topology
IEEE Transactions on Signal Processing
Information theoretic bounds for distributed computation over networks of point-to-point channels
IEEE Transactions on Information Theory
Low-power distributed Kalman filter for wireless sensor networks
EURASIP Journal on Embedded Systems
Consensus acceleration in multiagent systems with the Chebyshev semi-iterative method
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
SIAM Journal on Control and Optimization
Leader-follower consensus over numerosity-constrained random networks
Automatica (Journal of IFAC)
Information Sciences: an International Journal
Optimal topological design for distributed estimation over sensor networks
Information Sciences: an International Journal
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In a sensor network, in practice, the communication among sensors is subject to: 1) errors that can cause failures of links among sensors at random times; 2) costs; and 3) constraints, such as power, data rate, or communication, since sensors and networks operate under scarce resources. The paper studies the problem of designing the topology, i.e., assigning the probabilities of reliable communication among sensors (or of link failures) to maximize the rate of convergence of average consensus, when the link communication costs are taken into account, and there is an overall communication budget constraint. We model the network as a Bernoulli random topology and establish necessary and sufficient conditions for mean square sense (mss) and almost sure (a.s.) convergence of average consensus when network links fail. In particular, a necessary and sufficient condition is for the algebraic connectivity of the mean graph topology to be strictly positive. With these results, we show that the topology design with random link failures, link communication costs, and a communication cost constraint is a constrained convex optimization problem that can be efficiently solved for large networks by semidefinite programming techniques. Simulations demonstrate that the optimal design improves significantly the convergence speed of the consensus algorithm and can achieve the performance of a non-random network at a fraction of the communication cost.