4 research outputs found
A Logic for Monitoring Dynamic Networks of Spatially-distributed Cyber-Physical Systems
Cyber-Physical Systems (CPS) consist of inter-wined computational (cyber) and
physical components interacting through sensors and/or actuators. Computational
elements are networked at every scale and can communicate with each other and
with humans. Nodes can join and leave the network at any time or they can move
to different spatial locations. In this scenario, monitoring spatial and
temporal properties plays a key role in the understanding of how complex
behaviors can emerge from local and dynamic interactions. We revisit here the
Spatio-Temporal Reach and Escape Logic (STREL), a logic-based formal language
designed to express and monitor spatio-temporal requirements over the execution
of mobile and spatially distributed CPS. STREL considers the physical space in
which CPS entities (nodes of the graph) are arranged as a weighted graph
representing their dynamic topological configuration. Both nodes and edges
include attributes modeling physical and logical quantities that can evolve
over time. STREL combines the Signal Temporal Logic with two spatial modalities
reach and escape that operate over the weighted graph. From these basic
operators, we can derive other important spatial modalities such as everywhere,
somewhere and surround. We propose both qualitative and quantitative semantics
based on constraint semiring algebraic structure. We provide an offline
monitoring algorithm for STREL and we show the feasibility of our approach with
the application to two case studies: monitoring spatio-temporal requirements
over a simulated mobile ad-hoc sensor network and a simulated epidemic
spreading model for COVID19
Offline and online energy-efficient monitoring of scattered uncertain logs using a bounding model
Monitoring the correctness of distributed cyber-physical systems is
essential. Detecting possible safety violations can be hard when some samples
are uncertain or missing. We monitor here black-box cyber-physical system, with
logs being uncertain both in the state and timestamp dimensions: that is, not
only the logged value is known with some uncertainty, but the time at which the
log was made is uncertain too. In addition, we make use of an over-approximated
yet expressive model, given by a non-linear extension of dynamical systems.
Given an offline log, our approach is able to monitor the log against safety
specifications with a limited number of false alarms. As a second contribution,
we show that our approach can be used online to minimize the number of sample
triggers, with the aim at energetic efficiency. We apply our approach to three
benchmarks, an anesthesia model, an adaptive cruise controller and an aircraft
orbiting system
Predictive Runtime Verification of Stochastic Systems
Runtime Verification (RV) is the formal analysis of the execution of a system against some
properties at runtime. RV is particularly useful for stochastic systems that have a non-zero
probability of failure at runtime. The standard RV assumes constructing a monitor that
checks only the currently observed execution of the system against the given properties.
This dissertation proposes a framework for predictive RV, where the monitor instead
checks the current execution with its finite extensions against some property. The extensions are generated using a prediction model, that is built based on execution samples
randomly generated from the system. The thesis statement is that predictive RV for
stochastic systems is feasible, effective, and useful.
The feasibility is demonstrated by providing a framework, called Prevent, that builds a
predictive monitor by using trained prediction models to finitely extend an execution path,
and computing the probabilities of the extensions that satisfy or violate the given property.
The prediction model is trained using statistical learning techniques from independent and
identically distributed samples of system executions. The prediction is the result of a
quantitative bounded reachability analysis on the product of the prediction model and the
automaton specifying the property. The analysis results are computed offline and stored in
a lookup table. At runtime the monitor obtains the state of the system on the prediction
model based on the observed execution, directly or by approximation, and uses the lookup
table to retrieve the computed probability that the system at the current state will satisfy
or violate the given property within some finite number of steps.
The effectiveness of Prevent is shown by applying abstraction when constructing the
prediction model. The abstraction is on the observation space based on extracting the
symmetry relation between symbols that have similar probabilities to satisfy a property.
The abstraction may introduce nondeterminism in the final model, which is handled by
using a hidden state variable when building the prediction model. We also demonstrate
that, under the convergence conditions of the learning algorithms, the prediction results
from the abstract models are the same as the concrete models.
Finally, the usefulness of Prevent is indicated in real-world applications by showing
how it can be applied for predicting rare properties, properties with very low but non-zero
probability of satisfaction. More specifically, we adjust the training algorithm that uses
the samples generated by importance sampling to generate the prediction models for rare
properties without increasing the number of samples and without having a negative impact
on the prediction accuracy