A general discrete-time method to achieve resilience in consensus algorithms

Abstract

In this paper, we approach the problem of a set of network agents reaching resilient consensus in the presence of a subset of attacked nodes. We devise a generalized method, with polynomial time complexity, which receives as input a discrete-time, synchronous-communication consensus algorithm, a dynamic network of agents, and the maximum number of attacked nodes. The distributed algorithm enables each normal node to detect and discard the values of the attacked agents while reaching the consensus of normal agents for the input consensus algorithm. Hence, the proposed method adds an extra layer of resilience to a given discrete-time and synchronous-communication consensus algorithm. Finally, we demonstrate the effectiveness of the method with experimental results, showing some attack circumstances which we can counter, where the state-of-the-art methods fail.

Minimum jointly structural input and output selection for strongly connected networks

“…Hence, the problem that we aim to solve in this paper is the following. P 1 Given a structural matrix Ā associated with (2), such that G( Ā) is strongly connected, find…”

“…Multi-agent dynamical systems (MADS) can resolve problems that are challenging or unsuitable for solving either with a single agent or a monolithic system [1]. These systems emerge in a plethora of applications, including consensus problems [2,3], target surveillance [4], online trading [5], network resistance [6], disaster response [7], and wireless sensor networks (WSN) [8], just to name a few.…”

Structurally quotient fixed modes

“…Autonomy is at the heart of automation of increasingly largescale dynamical systems such as chemical processes, smart grid, smart cities, cyber-physical systems, multi-agent systems, and the Internet of Things (IoT) [1][2][3][4][5]. To address the inherent problem of restricted communications and spatially distributed sensors and actuators associated with these systems, decentralized control is often the setting used to perform their design [6].…”

Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation

“…Therefore, numerous systems have been developed to compute distributedly a function. The distributed function results from each agent computing a local function, using the neighbors' communicated information, and sharing the output with its neighbors, for instance, using consensus [1][2][3][4] or state retrieval [5]. Under the described setting, for security reasons, the agents may want to ensure that their information is not disclosed.…”