5,975 research outputs found
Petri nets for systems and synthetic biology
We give a description of a Petri net-based framework for
modelling and analysing biochemical pathways, which uni¯es the qualita-
tive, stochastic and continuous paradigms. Each perspective adds its con-
tribution to the understanding of the system, thus the three approaches
do not compete, but complement each other. We illustrate our approach
by applying it to an extended model of the three stage cascade, which
forms the core of the ERK signal transduction pathway. Consequently
our focus is on transient behaviour analysis. We demonstrate how quali-
tative descriptions are abstractions over stochastic or continuous descrip-
tions, and show that the stochastic and continuous models approximate
each other. Although our framework is based on Petri nets, it can be
applied more widely to other formalisms which are used to model and
analyse biochemical networks
A Comparison of Stealthy Sensor Attacks on Control Systems
As more attention is paid to security in the context of control systems and
as attacks occur to real control systems throughout the world, it has become
clear that some of the most nefarious attacks are those that evade detection.
The term stealthy has come to encompass a variety of techniques that attackers
can employ to avoid detection. Here we show how the states of the system (in
particular, the reachable set corresponding to the attack) can be manipulated
under two important types of stealthy attacks. We employ the chi-squared fault
detection method and demonstrate how this imposes a constraint on the attack
sequence either to generate no alarms (zero-alarm attack) or to generate alarms
at a rate indistinguishable from normal operation (hidden attack)
A Polynomial Translation of pi-calculus FCPs to Safe Petri Nets
We develop a polynomial translation from finite control pi-calculus processes
to safe low-level Petri nets. To our knowledge, this is the first such
translation. It is natural in that there is a close correspondence between the
control flows, enjoys a bisimulation result, and is suitable for practical
model checking.Comment: To appear in special issue on best papers of CONCUR'12 of Logical
Methods in Computer Scienc
SPIDER: Fault Resilient SDN Pipeline with Recovery Delay Guarantees
When dealing with node or link failures in Software Defined Networking (SDN),
the network capability to establish an alternative path depends on controller
reachability and on the round trip times (RTTs) between controller and involved
switches. Moreover, current SDN data plane abstractions for failure detection
(e.g. OpenFlow "Fast-failover") do not allow programmers to tweak switches'
detection mechanism, thus leaving SDN operators still relying on proprietary
management interfaces (when available) to achieve guaranteed detection and
recovery delays. We propose SPIDER, an OpenFlow-like pipeline design that
provides i) a detection mechanism based on switches' periodic link probing and
ii) fast reroute of traffic flows even in case of distant failures, regardless
of controller availability. SPIDER can be implemented using stateful data plane
abstractions such as OpenState or Open vSwitch, and it offers guaranteed short
(i.e. ms) failure detection and recovery delays, with a configurable trade off
between overhead and failover responsiveness. We present here the SPIDER
pipeline design, behavioral model, and analysis on flow tables' memory impact.
We also implemented and experimentally validated SPIDER using OpenState (an
OpenFlow 1.3 extension for stateful packet processing), showing numerical
results on its performance in terms of recovery latency and packet losses.Comment: 8 page
SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems
The recent drive towards achieving greater autonomy and intelligence in
robotics has led to high levels of complexity. Autonomous robots increasingly
depend on third party off-the-shelf components and complex machine-learning
techniques. This trend makes it challenging to provide strong design-time
certification of correct operation.
To address these challenges, we present SOTER, a robotics programming
framework with two key components: (1) a programming language for implementing
and testing high-level reactive robotics software and (2) an integrated runtime
assurance (RTA) system that helps enable the use of uncertified components,
while still providing safety guarantees. SOTER provides language primitives to
declaratively construct a RTA module consisting of an advanced,
high-performance controller (uncertified), a safe, lower-performance controller
(certified), and the desired safety specification. The framework provides a
formal guarantee that a well-formed RTA module always satisfies the safety
specification, without completely sacrificing performance by using higher
performance uncertified components whenever safe. SOTER allows the complex
robotics software stack to be constructed as a composition of RTA modules,
where each uncertified component is protected using a RTA module.
To demonstrate the efficacy of our framework, we consider a real-world
case-study of building a safe drone surveillance system. Our experiments both
in simulation and on actual drones show that the SOTER-enabled RTA ensures the
safety of the system, including when untrusted third-party components have bugs
or deviate from the desired behavior
Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving
Adverse weather conditions and occlusions in urban environments result in
impaired perception. The uncertainties are handled in different modules of an
automated vehicle, ranging from sensor level over situation prediction until
motion planning. This paper focuses on motion planning given an uncertain
environment model with occlusions. We present a method to remain collision free
for the worst-case evolution of the given scene. We define criteria that
measure the available margins to a collision while considering visibility and
interactions, and consequently integrate conditions that apply these criteria
into an optimization-based motion planner. We show the generality of our method
by validating it in several distinct urban scenarios
Distributed Computing with Adaptive Heuristics
We use ideas from distributed computing to study dynamic environments in
which computational nodes, or decision makers, follow adaptive heuristics (Hart
2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly
"best replying" to others' actions, and minimizing "regret", that have been
extensively studied in game theory and economics. We explore when convergence
of such simple dynamics to an equilibrium is guaranteed in asynchronous
computational environments, where nodes can act at any time. Our research
agenda, distributed computing with adaptive heuristics, lies on the borderline
of computer science (including distributed computing and learning) and game
theory (including game dynamics and adaptive heuristics). We exhibit a general
non-termination result for a broad class of heuristics with bounded
recall---that is, simple rules of behavior that depend only on recent history
of interaction between nodes. We consider implications of our result across a
wide variety of interesting and timely applications: game theory, circuit
design, social networks, routing and congestion control. We also study the
computational and communication complexity of asynchronous dynamics and present
some basic observations regarding the effects of asynchrony on no-regret
dynamics. We believe that our work opens a new avenue for research in both
distributed computing and game theory.Comment: 36 pages, four figures. Expands both technical results and discussion
of v1. Revised version will appear in the proceedings of Innovations in
Computer Science 201
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