1,678 research outputs found
Resilient Consensus for Robust Multiplex Networks with Asymmetric Confidence Intervals
The consensus problem with asymmetric confidence intervals considered in this paper is characterized by the fact that each agent can have optimistic and/or pessimistic interactions with its neighbors. To deal with the asymmetric confidence scenarios, we introduce a novel multiplex network presentation for directed graphs and its associated connectivity concepts including the pseudo-strongly connectivity and graph robustness, which provide a resilience characterization in the presence of malicious nodes. We develop distributed resilient consensus strategies for a group of dynamical agents with locally bounded Byzantine agents in both continuous-time and discrete-time multi-agent systems. Drawing on our multiplex network framework, much milder connectivity conditions compared to existing works are proposed to ensure resilient consensus. The results are further extended to cope with resilient scaled consensus problems which allow both cooperative and antagonistic agreements among agents. Numerical examples are also exhibited to confirm the theoretical results and reveal the factors that affect the speed of convergence in our multiplex network framework
How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems
Multi-agent cyberphysical systems enable new capabilities in efficiency,
resilience, and security. The unique characteristics of these systems prompt a
reevaluation of their security concepts, including their vulnerabilities, and
mechanisms to mitigate these vulnerabilities. This survey paper examines how
advancement in wireless networking, coupled with the sensing and computing in
cyberphysical systems, can foster novel security capabilities. This study
delves into three main themes related to securing multi-agent cyberphysical
systems. First, we discuss the threats that are particularly relevant to
multi-agent cyberphysical systems given the potential lack of trust between
agents. Second, we present prospects for sensing, contextual awareness, and
authentication, enabling the inference and measurement of ``inter-agent trust"
for these systems. Third, we elaborate on the application of quantifiable trust
notions to enable ``resilient coordination," where ``resilient" signifies
sustained functionality amid attacks on multiagent cyberphysical systems. We
refer to the capability of cyberphysical systems to self-organize, and
coordinate to achieve a task as autonomy. This survey unveils the cyberphysical
character of future interconnected systems as a pivotal catalyst for realizing
robust, trust-centered autonomy in tomorrow's world
Can Competition Outperform Collaboration? The Role of Misbehaving Agents
We investigate a novel approach to resilient distributed optimization with
quadratic costs in a multi-agent system prone to unexpected events that make
some agents misbehave. In contrast to commonly adopted filtering strategies, we
draw inspiration from phenomena modeled through the Friedkin-Johnsen dynamics
and argue that adding competition to the mix can improve resilience in the
presence of misbehaving agents. Our intuition is corroborated by analytical and
numerical results showing that (i) there exists a nontrivial trade-off between
full collaboration and full competition and (ii) our competition-based approach
can outperform state-of-the-art algorithms based on Weighted Mean Subsequence
Reduced. We also study impact of communication topology and connectivity on
resilience, pointing out insights to robust network design.Comment: Submitted to IEEE TAC - first revisio
Resilient Multi-Dimensional Consensus in Adversarial Environment
This paper considers the multi-dimensional consensus in networked systems,
where some of the agents might be misbehaving (or faulty). Despite the
influence of these misbehaviors, the healthy agents aim to reach an agreement
within the convex hull of their initial states. Towards this end, this paper
develops a resilient consensus algorithm, where each healthy agent sorts its
received values on one dimension, computes two "middle points" based on the
sorted values, and moves its state toward these middle points. We further show
that the computation of middle points can be efficiently achieved by linear
programming. Compared with the existing works, this approach has lower
computational complexity. Assuming that the number of malicious agents is upper
bounded, sufficient conditions on the network topology are then presented to
guarantee the achievement of resilient consensus. Some numerical examples are
finally provided to verify the theoretical results.Comment: arXiv admin note: substantial text overlap with arXiv:1911.1083
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