Matrix analysis
Distributed Algorithms
Local Divergence of Markov Chains and the Analysis of Iterative Load-Balancing Schemes
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Geographic gossip: efficient aggregation for sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Computing separable functions via gossip
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
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
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Differential nested lattice encoding for consensus problems
Proceedings of the 6th international conference on Information processing in sensor networks
Distributed Average Consensus using Probabilistic Quantization
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Sensor Networks With Random Links: Topology Design for Distributed Consensus
IEEE Transactions on Signal Processing - Part II
Geographic Gossip: Efficient Averaging for Sensor Networks
IEEE Transactions on Signal Processing
Distributed Average Consensus With Dithered Quantization
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Information Theory
Toward a theory of in-network computation in wireless sensor networks
IEEE Communications Magazine
Randomized consensus algorithms over large scale networks
IEEE Journal on Selected Areas in Communications
Reaching consensus in wireless networks with probabilistic broadcast
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Computing along routes via gossiping
IEEE Transactions on Signal Processing
Distributed multiagent learning with a broadcast adaptive subgradient method
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A simple and scalable algorithm for alignment in broadcast networks
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
Weight optimization for consensus algorithms with correlated switching topology
IEEE Transactions on Signal Processing
Location-aided fast distributed consensus in wireless networks
IEEE Transactions on Information Theory
Binary consensus over fading channels
IEEE Transactions on Signal Processing
Distributed consensus for multi-agent systems with delays and noises in transmission channels
Automatica (Journal of IFAC)
Hierarchical Spatial Gossip for Multiresolution Representations in Sensor Networks
ACM Transactions on Sensor Networks (TOSN)
Robust distributed orthogonalization based on randomized aggregation
Proceedings of the second workshop on Scalable algorithms for large-scale systems
The cost of fault tolerance in multi-party communication complexity
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
Convergence time analysis of quantized gossip consensus on digraphs
Automatica (Journal of IFAC)
Distributed QR factorization based on randomized algorithms
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
On the mean square error of randomized averaging algorithms
Automatica (Journal of IFAC)
Consensus networks over finite fields
Automatica (Journal of IFAC)
Hi-index | 35.76 |
Motivated by applications to wireless sensor, peer-to-peer, and ad hoc networks, we study distributed broadcasting algorithms for exchanging information and computing in an arbitrarily connected network of nodes. Specifically, we study a broadcasting-based gossiping algorithm to compute the (possibly weighted) average of the initial measurements of the nodes at every node in the network. We show that the broadcast gossip algorithm converges almost surely to a consensus. We prove that the random consensus value is, in expectation, the average of initial node measurements and that it can be made arbitrarily close to this value in mean squared error sense, under a balanced connectivity model and by trading off convergence speed with accuracy of the computation. We provide theoretical and numerical results on the mean square error performance, on the convergence rate and study the effect of the "mixing parameter" on the convergence rate of the broadcast gossip algorithm. The results indicate that the mean squared error strictly decreases through iterations until the consensus is achieved. Finally, we assess and compare the communication cost of the broadcast gossip algorithm to achieve a given distance to consensus through theoretical and numerical results.