Proceedings Article10.1145/1236360.1236373
Differential nested lattice encoding for consensus problems
Mehmet E. Yildiz,Anna Scaglione +1 more
- 25 Apr 2007
- pp 89-98
53
TL;DR: The problem of transmitting quantized data while performing an average consensus algorithm is considered and it is concluded that noisy recursions lead to a consensus if the data correlation is exploited in the messages source encoders and decoders.
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Abstract: In this paper we consider the problem of transmitting quantized data while performing an average consensus algorithm. Average consensus algorithms are protocols to compute the average value of all sensor measurements via near neighbors communications. The main motivation for our work is the observation that consensus algorithms offer the perfect example of network communications where there is an increasing correlation between the data exchanged, as the system updates its computations. Henceforth, it is possible to utilize previously exchanged data and current side information to reduce significantly the demands of quantization bit rate for a certain precision. We analyze the case of a network with a topology built as that of a random geometric graph and with links that are assumed to be reliable at a constant bit rate. Numerically we show that in consensus algorithms, increasing number of iterations does not have the effect of increasing the error variance. Thus, we conclude that noisy recursions lead to a consensus if the data correlation is exploited in the messages source encoders and decoders. We briefly state the theoretical results which are parallel to our numerical experiments.
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Citations
Gossip Algorithms for Distributed Signal Processing
Alexandros G. Dimakis,Soummya Kar,Jose M. F. Moura,Michael G. Rabbat,Anna Scaglione +4 more
- 09 Aug 2010
TL;DR: An overview of recent gossip algorithms work, including convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping, and the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.
Gossip Algorithms for Distributed Signal Processing
TL;DR: Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions.
783
Broadcast Gossip Algorithms for Consensus
TL;DR: It is proved 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.
Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication
TL;DR: This paper proves consistency (all sensors reach consensus almost surely and converge to the true parameter value), efficiency, and asymptotic unbiasedness, and provides convergence rate guarantees in distributed static parameter (vector) estimation in sensor networks with nonlinear observation models and noisy intersensor communication.
Distributed Consensus Algorithms in Sensor Networks: Quantized Data and Random Link Failures
Soummya Kar,Jose M. F. Moura +1 more
TL;DR: In this article, the authors studied the problem of distributed average consensus in sensor networks with quantized data and random link failures. But their work was restricted to the case where the quantizer range is unbounded.
447
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