Decentralized LTL Enforcement

07/07/2021
by   Florian Gallay, et al.
0

We consider the runtime enforcement of Linear-time Temporal Logic formulas on decentralized systems with no central observation point nor authority. A so-called enforcer is attached to each system component and observes its local trace. Should the global trace violate the specification, the enforcers coordinate to correct their local traces. We formalize the decentralized runtime enforcement problem and define the expected properties of enforcers, namely soundness, transparency and optimality. We present two enforcement algorithms. In the first one, the enforcers explore all possible local modifications to find the best global correction. Although this guarantees an optimal correction, it forces the system to synchronize and is more costly, computation and communication wise. In the second one, each enforcer makes a local correction before communicating. The reduced cost of this version comes at the price of the optimality of the enforcer corrections.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/31/2019

RVHyper: A Runtime Verification Tool for Temporal Hyperproperties

We present RVHyper, a runtime verification tool for hyperproperties. Hyp...
research
03/08/2022

Runtime Enforcement of Hyperproperties

An enforcement mechanism monitors a reactive system for undesired behavi...
research
08/14/2020

Can determinism and compositionality coexist in RML? (extended version)

Runtime verification (RV) consists in dynamically verifying that the eve...
research
05/05/2021

Flavours of Sequential Information Flow

Information-flow policies prescribe which information is available to a ...
research
02/25/2017

Efficient coordinate-wise leading eigenvector computation

We develop and analyze efficient "coordinate-wise" methods for finding t...
research
07/20/2021

Approximate Trace Reconstruction via Median String (in Average-Case)

We consider an approximate version of the trace reconstruction problem, ...
research
03/06/2020

Teaching Temporal Logics to Neural Networks

We show that a deep neural network can learn the semantics of linear-tim...

Please sign up or login with your details

Forgot password? Click here to reset