Higher dimensional consensus algorithms in sensor networks
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Signal Processing
Distributed sensor localization in random environments using minimal number of anchor nodes
IEEE Transactions on Signal Processing
Distributed consensus algorithms in sensor networks: quantized data and random link failures
IEEE Transactions on Signal Processing
Higher dimensional consensus: learning in large-scale networks
IEEE Transactions on Signal Processing
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The paper studies the effect of noise on the asymptotic properties of high dimensional consensus (HDC). HDC offers a unified framework to study a broad class of distributed algorithms with applications to average consensus, leader-follower dynamics in multi-agent networks and distributed sensor localization. We show that under a broad range of perturbations, including inter-sensor communication noise, random data packet dropouts and algorithmic parameter uncertainty, a modified version of the HDC converges almost surely (a.s.) We characterize the asymptotic mean squared error (m.s.e.) from the desired agreement state of the sensors (which, in general, vary from sensor to sensor) and show broad conditions on the noise leading to zero asymptotic m.s.e. The convergence proof of the modified HDC algorithm is based on stochastic approximation arguments and offers a general framework to study the convergence properties of distributed algorithms in the presence of noise.