Understanding packet delivery performance in dense wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Fastest Mixing Markov Chain on a Graph
SIAM Review
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
Statistical model of lossy links in wireless sensor networks
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
IEEE Transactions on Signal Processing
Broadcast gossip algorithms for consensus
IEEE Transactions on Signal Processing
Distributed consensus algorithms in sensor networks: quantized data and random link failures
IEEE Transactions on Signal Processing
Sensor Networks With Random Links: Topology Design for Distributed Consensus
IEEE Transactions on Signal Processing - Part II
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Topology for Distributed Inference on Graphs
IEEE Transactions on Signal Processing
Hi-index | 35.68 |
We design the weights in consensus algorithms for spatially correlated random topologies. These arise with 1) networks with spatially correlated random link failures and 2) networks with randomized averaging protocols. We show that the weight optimization problem is convex for both symmetric and asymmetric random graphs. With symmetric random networks, we choose the consensus mean-square error (MSE) convergence rate as the optimization criterion and explicitly express this rate as a function of the link formation probabilities, the link formation spatial correlations, and the consensus weights. We prove that the MSE convergence rate is a convex, nonsmooth function of the weights, enabling global optimization of the weights for arbitrary link formation probabilities and link correlation structures.We extend our results to the case of asymmetric random links.We adopt as optimization criterion the mean-square deviation (MSdev) of the nodes' states from the current average state. We prove that MSdev is a convex function of the weights. Simulations show that significant performance gain is achieved with our weight design method when compared with other methods available in the literature.