655 citations · 1.4k across the 34 of their papers we have counts for
7 papers · 1 filter
Optimal Model-Based Sensor Placement & Adaptive Monitoring Of An Oil Spill
Zak Hodgson, Inaki Esnaola, Bryn Jones
This paper presents a model based adaptive monitoring method for the estimation of flow tracers, with application to mapping, prediction and observation of oil spills in the immedi…
A combined ocean and oil model for model-based adaptive monitoring
Zak Hodgson, David Browne, Inaki Esnaola +1
This paper presents a combined ocean and oil model for adaptive placement of sensors in the immediate aftermath of oilspills. A key feature of this model is the ability to correct…
Denial of Service Attacks on Control Systems with Packet Loss
William Casbolt, Iñaki Esnaola, Bryn Jones
The performance of control systems with packet loss as a result of an attack over the actuation communication channel is analysed. The operator is assumed to monitor the state of t…
Optimal Control over Multiple Input Lossy Channels
William Casbolt, Bryn Jones, Iñaki Esnaola
The performance of control systems with input packet losses on the controller to plant communication channel is analysed. The main contribution of this work is a proof that linear…
Recovery of Missing Data in Correlated Smart Grid Datasets
Cristian Genes, Iñaki Esnaola, Samir Perlaza +1
We study the recovery of missing data from multiple smart grid datasets within a matrix completion framework. The datasets contain the electrical magnitudes required for monitoring…
Universal Mutual Information Privacy Guarantees for Smart Meters
Miguel Arrieta, Iñaki Esnaola, Michelle Effros
Smart meters enable improvements in electricity distribution system efficiency at some cost in customer privacy. Users with home batteries can mitigate this privacy loss by applyin…
Learning requirements for stealth attacks
Ke Sun, Iñaki Esnaola, Antonia M. Tulino +1
The learning data requirements are analyzed for the construction of stealth attacks in state estimation. In particular, the training data set is used to compute a sample covariance…