Crowd Vetting: Rejecting Adversaries via Collaboration With Application to Multirobot Flocking

Crowd Vetting: Rejecting Adversaries via Collaboration With Application to Multirobot Flocking
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人群审查:通过协作拒绝对手并应用于多机器人集群

DOI:
10.1109/tro.2021.3089033
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发表时间:
2022
影响因子:
7.8
通讯作者:
Mallmann-Trenn F
Mallmann-Trenn F
中科院分区:
计算机科学1区
文献类型:
--
作者:
Mallmann-Trenn F

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在这篇文章中,我们描述了使用机器人的邻域来发现和消除存在Sybil攻击的敌对机器人的优势。我们表明,通过利用邻居对传输数据可信度的意见,机器人可以以很高的概率检测到对手。我们的特点是所需的通信轮数是一个函数的通信质量和合法的恶意机器人的比例。这一结果使许多多机器人算法的弹性增加。因为我们的结果是有限的时间,而不是渐近的,他们是特别适合的时间临界性质的问题。我们开发了两种算法,FindSpoofedRobots确定可信邻居的概率很高,和FindResilientAdjacencyMatrix,使分布式计算的图形属性在一个敌对的设置。我们将我们的方法应用于群集问题,其中一组机器人必须在对抗机器人的存在下跟踪移动目标。我们表明,通过使用我们的算法,团队的机器人能够保持跟踪能力的动态目标。
In this article, we characterize the advantage of using a robot’s neighborhood to find and eliminate adversarial robots in the presence of a Sybil attack. We show that by leveraging the opinions of their neighbors on the trustworthiness of transmitted data, robots can detect adversaries with high probability. We characterize the number of communication rounds required to be a function of the communication quality and of the proportion of legitimate to malicious robots. This result enables increased resiliency of many multirobot algorithms. Because our results are finite time and not asymptotic, they are particularly well-suited for problems of a time critical nature. We develop two algorithms,FindSpoofedRobotsthat determines trusted neighbors with high probability, andFindResilientAdjacencyMatrixthat enables distributed computation of graph properties in an adversarial setting. We apply our methods to a flocking problem where a team of robots must track a moving target in the presence of adversarial robots. We show that by using our algorithms, the team of robots are able to maintain tracking ability of the dynamic target.
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