Matrix analysis
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
Polynomial filtering for fast convergence in distributed consensus
IEEE Transactions on Signal Processing
Fastest Mixing Markov Chain on Graphs with Symmetries
SIAM Journal on Optimization
Location-aided fast distributed consensus in wireless networks
IEEE Transactions on Information Theory
Geographic Gossip: Efficient Averaging for Sensor Networks
IEEE Transactions on Signal Processing
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Distributed computation of averages over ad hoc networks
IEEE Journal on Selected Areas in Communications
Hi-index | 35.68 |
Distributed consensus algorithms have recently gained large interest in sensor networks as a way to achieve globally optimal decisions in a totally decentralized way, that is, without the need of sending all the data collected by the sensors to a fusion center. However, distributed algorithms are typically iterative and they suffer from convergence time and energy consumption. In this paper, we show that introducing appropriate asymmetric interaction mechanisms, with time-varying weights on each edge, it is possible to provide a substantial increase of convergence rate with respect to the symmetric time-invariant case. The basic idea underlying our approach comes from modeling the average consensus algorithm as an advection-diffusion process governing the homogenization of fluid mixtures. Exploiting such a conceptual link, we show how introducing interaction mechanisms among nearby nodes, mimicking suitable advection processes, yields a substantial increase of convergence rate. Moreover, we show that the homogenization enhancement induced by the advection term produces a qualitatively different scaling law of the convergence rate versus the network size with respect to the symmetric case.