Hopfield-Like Neural Nets and Sensor Networks
Neural Processing Letters
Consensus over networks with dynamic channels
International Journal of Systems, Control and Communications
Distributed agreement in the presence of noise
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Distributed estimation of channel gains in wireless sensor networks
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Self-aware distributed consensus building for sensor networks
ISRN Communications and Networking
| Hi-index | 35.69 |
In this paper we propose and analyze a distributed algorithm for achieving globally optimal decisions, either estimation or detection, through a self-synchronization mechanism among linearly coupled integrators initialized with local measurements. We model the interaction among the nodes as a directed graph with weights (possibly) dependent on the radio channels, and we pose special attention to the effect of the propagation delay occurring in the exchange of data among sensors, as a function of the network geometry. We derive necessary and sufficient conditions for the proposed system to reach a consensus on globally optimal decision statistics. One of the major results proved in this work is that a consensus is reached with exponential convergence speed for any bounded delay condition if and only if the directed graph is quasi-strongly connected. We provide a closed form expression for the global consensus, showing that the effect of delays is, in general, the introduction of a bias in the final decision. Finally, we exploit our closed form expression to devise a double-step consensus mechanism able to provide an unbiased estimate with minimum extra complexity, without the need to know or estimate the channel parameters.