5 research outputs found
Design and Optimisation of the FlyFast Front-end for Attribute-based Coordination
Collective Adaptive Systems (CAS) consist of a large number of interacting
objects. The design of such systems requires scalable analysis tools and
methods, which have necessarily to rely on some form of approximation of the
system's actual behaviour. Promising techniques are those based on mean-field
approximation. The FlyFast model-checker uses an on-the-fly algorithm for
bounded PCTL model-checking of selected individual(s) in the context of very
large populations whose global behaviour is approximated using deterministic
limit mean-field techniques. Recently, a front-end for FlyFast has been
proposed which provides a modelling language, PiFF in the sequel, for the
Predicate-based Interaction for FlyFast. In this paper we present details of
PiFF design and an approach to state-space reduction based on probabilistic
bisimulation for inhomogeneous DTMCs.Comment: In Proceedings QAPL 2017, arXiv:1707.0366
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
Enhancing the VANET Network Layer
The aim of this thesis is to examine existing VANET network layer functionality and to propose enhancements to the VANET network layer to facilitate vehicular (V2X) communication. This thesis proposes three enhancements to the VANET network layer which address many of the issues with V2X communication, these enhancements are: a geographic overlay allowing vehicles to localize themselves; an IPv6 addressing strategy which embeds positional information within an IP address allowing for location based routing; and finally a novel position based routing protocol which has two primary advantages over existing protocols, firstly removing unnecessary overhead information and control communication, and secondly support for multiple types of V2X communication models. The simulation results show that the proposed enhancements are well suited in low and medium vehicular density environments. Based on the observed behaviors the author recommends further modification and study of position based routing protocols