Resilience of Dynamic Routing in the Face of Recurrent and Random Sensing Faults
Qian Xie, Li Jin

TL;DR
This paper analyzes how recurrent and random sensing faults affect the stability and throughput of dynamic routing in transportation networks, providing bounds and conditions for resilience.
Contribution
It introduces a Markov chain-based model for sensing faults and derives stability conditions and resilience bounds for dynamic routing under such faults.
Findings
Sensing faults can reduce throughput and cause instability.
Higher failure rates do not always decrease throughput; a worst rate may exist.
Greater correlation between link failures can increase network throughput.
Abstract
Feedback dynamic routing is a commonly used control strategy in transportation systems. This class of control strategies relies on real-time information about the traffic state in each link. However, such information may not always be observable due to temporary sensing faults. In this article, we consider dynamic routing over two parallel routes, where the sensing on each link is subject to recurrent and random faults. The faults occur and clear according to a finite-state Markov chain. When the sensing is faulty on a link, the traffic state on that link appears to be zero to the controller. Building on the theories of Markov processes and monotone dynamical systems, we derive lower and upper bounds for the resilience score, i.e. the guaranteed throughput of the network, in the face of sensing faults by establishing stability conditions for the network. We use these results to study…
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Taxonomy
TopicsFault Detection and Control Systems · Petri Nets in System Modeling · Advanced Control Systems Optimization
