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20172022
most citedA Survey of Graph-Theoretic Approaches for Analyzing the Resilience of Networked Control Systems

4 citations · 9 across the 7 of their papers we have counts for

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eess.SY20224 cited

A Survey of Graph-Theoretic Approaches for Analyzing the Resilience of Networked Control Systems

Mohammad Pirani, Aritra Mitra, Shreyas Sundaram

As the scale of networked control systems increases and interactions between different subsystems become more sophisticated, questions of the resilience of such networks increase i…

eess.SY2021

On the Computational Complexity of the Secure State-Reconstruction Problem

Yanwen Mao, Aritra Mitra, Shreyas Sundaram +1

In this paper, we discuss the computational complexity of reconstructing the state of a linear system from sensor measurements that have been corrupted by an adversary. The first r…

eess.SY2020

On the Impacts of Redundancy, Diversity, and Trust in Resilient Distributed State Estimation

Aritra Mitra, Faiq Ghawash, Shreyas Sundaram +1

We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. Recent attempts to solve this problem impose stringent redundan…

eess.SY2020

Distributed State Estimation over Time-Varying Graphs: Exploiting the Age-of-Information

Aritra Mitra, John A. Richards, Saurabh Bagchi +1

We study the problem of designing a distributed observer for an LTI system over a time-varying communication graph. The limited existing work on this topic imposes various restrict…

eess.SY2019

A Communication-Efficient Algorithm for Exponentially Fast Non-Bayesian Learning in Networks

Aritra Mitra, John A. Richards, Shreyas Sundaram

We introduce a simple time-triggered protocol to achieve communication-efficient non-Bayesian learning over a network. Specifically, we consider a scenario where a group of agents…

eess.SY2019

A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience

Aritra Mitra, John A. Richards, Shreyas Sundaram

We study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finit…