Matrix analysis
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Locally constructed algorithms for distributed computations in ad-hoc networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Brief paper: Convergence speed in distributed consensus over dynamically switching random networks
Automatica (Journal of IFAC)
Fast mean square convergence of consensus algorithms in WSNs with random topologies
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Signal Processing
Sensor Networks With Random Links: Topology Design for Distributed Consensus
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
IEEE Communications Magazine
Distributed consensus for multi-agent systems with delays and noises in transmission channels
Automatica (Journal of IFAC)
Leader-follower consensus over numerosity-constrained random networks
Automatica (Journal of IFAC)
| Hi-index | 35.69 |
Distributed consensus algorithms for estimation of parameters or detection of events in wireless sensor networks have attracted considerable attention in recent years. A necessary condition to achieve a consensus on the average of the initial values is that the topology of the underlying graph is balanced or symmetric at every time instant. However, communication impairments can make the topology vary randomly in time, and instantaneous link symmetry between pairs of nodes is not guaranteed unless an acknowledgment protocol or an equivalent approach is implemented. In this paper, we evaluate the convergence of the consensus algorithm in the mean square sense in wireless sensor networks with random asymmetric topologies. For the case of links with equal probability of connection, a closed form expression for the mean square error of the state along with the dynamical range and the optimum value of the link weights that guarantee convergence are derived. For the case of links with different probabilities of connection, an upper bound for the mean square error of the state is derived. This upper bound can be computed for any time instant and can be employed to compute a link weight that reduces the convergence time of the algorithm.