Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Distributed Algorithms
Load Balancing in Parallel Computers: Theory and Practice
Load Balancing in Parallel Computers: Theory and Practice
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
Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
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Proceedings of the 3rd international symposium on Information processing in sensor networks
A decentralized algorithm for spectral analysis
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Robust Aggregation Protocols for Large-Scale Overlay Networks
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and 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
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Distributed consensus and linear functional calculation in networks: an observability perspective
Proceedings of the 6th international conference on Information processing in sensor networks
IEEE Transactions on Information Theory
Distributed consensus with quantized data via sequence averaging
IEEE Transactions on Signal Processing
Adaptive filter algorithms for accelerated discrete-time consensus
IEEE Transactions on Signal Processing
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
Average consensus on general strongly connected digraphs
Automatica (Journal of IFAC)
Analysis of accelerated gossip algorithms
Automatica (Journal of IFAC)
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This paper proposes an approach to accelerate local, linear iterative network algorithms asymptotically achieving distributed average consensus. We focus on the class of algorithms in which each node initializes its "state value" to the local measurement and then at each iteration of the algorithm, updates this state value by adding a weighted sum of its own and its neighbors' state values. Provided the weight matrix satisfies certain convergence conditions, the state values asymptotically converge to the average of the measurements, but the convergence is generally slow, impeding the practical application of these algorithms. In order to improve the rate of convergence, we propose a novel method where each node employs a linear predictor to predict future node values. The local update then becomes a convex (weighted) sum of the original consensus update and the prediction; convergence is faster because redundant states are bypassed. The method is linear and poses a small computational burden. For a concrete theoretical analysis, we prove the existence of a convergent solution in the general case and then focus on one-step prediction based on the current state, and derive the optimal mixing parameter in the convex sum for this case. Evaluation of the optimal mixing parameter requires knowledge of the eigenvalues of the weight matrix, so we present a bound on the optimal parameter. Calculation of this bound requires only local information. We provide simulation results that demonstrate the validity and effectiveness of the proposed scheme. The results indicate that the incorporation of a multistep predictor can lead to convergence rates that are much faster than those achieved by an optimum weight matrix in the standard consensus framework.