Resilient Consensus Against Mobile Malicious Agents

Wang, Yuan; Ishii, Hideaki; Bonnet, François; Défago, Xavier

doi:10.48550/arxiv.2006.11711

Cited by 1 publication

(6 citation statements)

References 35 publications

(80 reference statements)

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“…We next introduce a lemma from our previous mobile malicious work [18]. This lemma will be used in the proofs of the results for the complete graph case studied in this section.…”

Section: Resiliency Results For Complete Graphsmentioning

confidence: 99%

“…The algorithm is outlined below. In particular, we present a modified version of the MSR algorithm from [18].…”

Section: A Resilient Consensusmentioning

confidence: 99%

“…Under such circumstances, the conventional resilient consensus algorithms are not capable to eliminate the adversarial effects. Instead, one must resort to the notion of mobile adversarial models, where the identities of the malicious agents switch, and algorithms robust to such models (e.g., [2], [5], [18]).…”

Section: B Overall System Modelmentioning

confidence: 99%

“…The contribution of this work is threefold: First, we extend the mobile adversary model of one-to-one mobile behavior to a epidemic case. In our previous work [18], a malicious agent moves to another agent and leaves a corrupted value. The agent that just recovered is considered as a cured agent.…”

mentioning

confidence: 99%

“…It is emphasized that to deal with this problem, the notion of mobile malicious agents studied in [18] becomes critical. Inspired by epidemic peak control [9], [12], we extend the mobile malicious model to the case with pandemic behaviors.…”

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confidence: 99%

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Resilient Consensus Against Epidemic Malicious Attacks

Wang

Ishii

Bonnet

et al. 2021

2021 European Control Conference (ECC)

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This paper addresses novel consensus problems for multi-agent systems operating in a pandemic environment where infectious diseases are spreading. The dynamics of the diseases follows the susceptible-infected-recovered (SIR) model, where the infection induces faulty behaviors in the agents and affects their state values. To ensure resilient consensus among the noninfectious agents, the difficulty is that the number of infectious agents changes over time. We assume that a high-level policy maker announces the level of infection in realtime, which can be adopted by the agents for their preventative measures. It is demonstrated that this problem can be formulated as resilient consensus in the presence of the socalled mobile malicious models, where the mean subsequence reduced (MSR) algorithms are known to be effective. We characterize sufficient conditions on the network structures for different policies regarding the announced infection levels and the strength of the pandemic. Numerical simulations are carried out for random graphs to verify the effectiveness of our approach. I. INTRODUCTIONRecently, the pandemic of COVID-19 has highlighted the necessity and effectiveness of unknown disease peak control. Since it may take up to several years to develop and supply vaccines sufficiently broadly [6], a large ratio of population may become infected simultaneously, which would cause huge pressure to hospitals. Initial measures such as keeping social distance and avoiding unnecessary gatherings are introduced to slow down the disease spreading. By temporarily reducing contacts with others at the proper period, peaks can be reduced in frequency and the number of infected patients. Recent works related to theoretical studies on peak control can be found in [9], [12].Epidemic models have been long studied among many different fields including mathematical biology [8], computer science [14], social science [7] and so on. Different epidemic models have been studied in the literature, but the most common ones include the susceptible-infected-susceptible (SIS) model and the susceptible-infected-recovered (SIR) model. In the traditional SIS model, the population is divided into two groups taking the susceptible and infected states; the ratios of the two groups may exhibit dynamic behaviors over time. More recently, the SIS model incorporates networks representing interactions of agents, where the pandemic process evolves over time-varying networks [15]- [17]. Similar trends can be found in the SIR model, where the agents

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“…We next introduce a lemma from our previous mobile malicious work [18]. This lemma will be used in the proofs of the results for the complete graph case studied in this section.…”

Section: Resiliency Results For Complete Graphsmentioning

confidence: 99%

“…The algorithm is outlined below. In particular, we present a modified version of the MSR algorithm from [18].…”

Section: A Resilient Consensusmentioning

confidence: 99%