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We address the problem of reaching resilient consensus among a set of agents in the presence of faulty nodes (attacked or noisy). We propose general algorithms, i.e., receiving as inputs a consensus algorithm, the network topology, the initial states, and the number of maximum allowed faulty nodes. These algorithms let the agents identify the set of attacked nodes and correct the consensus value by ignoring the faulty nodes. We prove that if the number of faulty nodes is below the maximum allowed, then each non-faulty agent detects them without false positives. If the inputted discrete-time consensus algorithm has polynomial-time complexity O (C) , then the proposed correction algorithms have polynomial-time complexity O (C n f) (and O (C n) for the detection of faulty nodes), for n nodes, and f maximum allowed faulty nodes. Finally, we show the effectiveness of the algorithms through simulation, pointing out attacking scenarios dealt with our methods, where the state-of-the-art underperformed. [ABSTRACT FROM AUTHOR]

This article investigates a political party or an association social network where members share a common set of beliefs. In modeling it as a distributed iterative algorithm with network dynamics mimicking the interactions between people, the problem of interest becomes that of determining: 1) the conditions when convergence happens in finite time and 2) the corresponding steady-state opinion. For a traditional model, it is shown that finite-time convergence requires a complete topology and that by removing neighbors with duplicate opinions reduces in half the number of links. Finite-time convergence is proved for two novel models even when nodes contact two other nodes of close opinion. In a deterministic setting, the network connectivity influences the final consensus and changes the relative weight of each node on the final value. In the case of mobile robots, a similar communication constraint is present which makes the analysis of the social network so relevant in the domain of control systems as a guideline to save resources and obtain finite-time consensus. Through simulations, the main results regarding convergence are illustrated paying special attention to the rates at which consensus is achieved. [ABSTRACT FROM AUTHOR]

This paper addresses the design problem of a resilient consensus algorithm for agents with continuous-time dynamics. The main proposal is that by incorporating a switching mechanism selecting the network topology to avoid malicious nodes from communicating, the remaining nodes will converge to a value closer to the original steady-state without the attacker being present. The switching occurs at discrete-time steps where each node evaluates the reputation score of the neighbors and deactivates/ignores edges in the network. We explore the proposed method with illustrative examples ranging from static topologies to dynamic ones, considering directed and undirected graphs, presenting several attacking scenarios that are successfully mitigated with our method. Finally, we compare the best undetectable attacking strategy and the commonly used approach named MSR, highlighting the advantages of our method. [ABSTRACT FROM AUTHOR]