scholarly journals SMT-Based CPS Parameter Synthesis

10.29007/msr8 ◽  
2018 ◽  
Author(s):  
Heinz Riener ◽  
Robert Koenighofer ◽  
Goerschwin Fey ◽  
Roderick Bloem
Keyword(s):  
Cyber Physical Systems ◽  
Satisfiability Modulo Theories ◽  
Proof Of Concept ◽  
Physical Systems ◽  
Inductive Synthesis ◽  
Parameter Synthesis ◽  
Guided Inductive

We present a simple, yet flexible parameter synthesis and repair approach for Cyber-Physical Systems (CPS). The user defines the behavior of a CPS, a set of (un)safe states, and a generic template for an inductive invariant using Satisfiability Modulo Theories (SMT) formulas. Counterexample-Guided Inductive Synthesis (CEGIS) is then used to compute values for open parameters and a concrete invariant to prove that all unsafe states are unreachable. Using templates for expressions, the approach can also be used for CPS repair. We present a proof-of-concept tool, optimizations, and first experiments.

scholarly journals Scheduling Optimization of Time-Triggered Cyber-Physical Systems Based on Fuzzy-Controlled QPSO and SMT Solver

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 668
Author(s):  
Jie Jian ◽  
Lide Wang ◽  
Huang Chen ◽  
Xiaobo Nie
Keyword(s):  
Scheduling Algorithm ◽  
Cyber Physical Systems ◽  
Satisfiability Modulo Theories ◽  
Random Mutation ◽  
Physical Systems ◽  
Smt Solver ◽  
Time Requirements ◽  
Adaptive Adjustment ◽  
Periodic Traffic ◽  
Cost Efficient

The time-triggered communication paradigm is a cost-efficient way to meet the real-time requirements of cyber-physical systems. It is a non-deterministic polynomial NP-complete problem for multi-hop networks and non-strictly periodic traffic. A two-level scheduling approach is proposed to simplify the complexity during optimization. In the first level, a fuzzy-controlled quantum-behaved particle swarm optimization (FQPSO) algorithm is proposed to optimize the scheduling performance by assigning time-triggered frame instances to the basic periods of each link. In order to prevent population from high aggregation, a random mutation mechanism is used to disturb particles at the aggregation point and enhance the diversity at later stages. Fuzzy logic is introduced and well designed to realize a dynamic adaptive adjustment of the contraction–expansion coefficient and mutation rate in FQPSO. In the second level, we use an improved Satisfiability Modulo Theories (SMT) scheduling algorithm to solve the collision-free and temporal constraints. A schedulability ranking method is proposed to accelerate the computation of the SMT-based incremental scheduler. Our approach can co-optimize the jitter and load balance of communication for an off-line schedule. The experiments show that the proposed approach can improve the performance of the scheduling table, reduce the optimization time, and reserve space for incremental messages.

Functional verification of cyber-physical systems containing machine-learnt components

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Farzaneh Moradkhani ◽  
Martin Fränzle
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Cyber Physical Systems ◽  
Satisfiability Modulo Theories ◽  
Activation Functions ◽  
Physical Systems ◽  
Smt Solvers ◽  
Non Linear ◽  
Artificial Neural

Abstract Functional architectures of cyber-physical systems increasingly comprise components that are generated by training and machine learning rather than by more traditional engineering approaches, as necessary in safety-critical application domains, poses various unsolved challenges. Commonly used computational structures underlying machine learning, like deep neural networks, still lack scalable automatic verification support. Due to size, non-linearity, and non-convexity, neural network verification is a challenge to state-of-art Mixed Integer linear programming (MILP) solvers and satisfiability modulo theories (SMT) solvers [2], [3]. In this research, we focus on artificial neural network with activation functions beyond the Rectified Linear Unit (ReLU). We are thus leaving the area of piecewise linear function supported by the majority of SMT solvers and specialized solvers for Artificial Neural Networks (ANNs), the successful like Reluplex solver [1]. A major part of this research is using the SMT solver iSAT [4] which aims at solving complex Boolean combinations of linear and non-linear constraint formulas (including transcendental functions), and therefore is suitable to verify the safety properties of a specific kind of neural network known as Multi-Layer Perceptron (MLP) which contain non-linear activation functions.

scholarly journals Co-simulation of cyber-physical systems using a DEVS wrapping strategy in the MECSYCO middleware

SIMULATION ◽  
2018 ◽  
Vol 94 (12) ◽  
pp. 1099-1127 ◽  
Author(s):  
Benjamin Camus ◽  
Thomas Paris ◽  
Julien Vaubourg ◽  
Yannick Presse ◽  
Christine Bourjot ◽  
...  
Keyword(s):  
Discrete Event ◽  
Cyber Physical Systems ◽  
Simulation Software ◽  
Proof Of Concept ◽  
System Specification ◽  
Physical Systems ◽  
Simulation Engine ◽  
Rigorous Framework ◽  
Multi Agent ◽  
Scientific Fields

Most modeling and simulation (M&S) questions about cyber-physical systems (CPSs) require expert skills belonging to different scientific fields. The challenges are then to integrate each domain’s tools (formalism and simulation software) within the rigorous framework of M&S process. To answer this issue, we give the specifications of the Multi-agent Environment for Complex-SYstem CO-simulation (MECSYCO) middleware which enables to interconnect several pre-existing and heterogeneous M&S tools, so they can simulate a whole CPS together. The middleware performs the co-simulation in a parallel, decentralized, and distributable fashion thanks to its modular multi-agent architecture. In order to rigorously integrate tools that use different formalisms, the co-simulation engine of MECSYCO is based on the discrete event system specification (DEVS). The central idea of MECSYCO is to use a DEVS wrapping strategy to integrate each tool into the middleware. Thus, heterogeneous tools can be homogeneously co-simulated in the form of a DEVS system. By using DEVS, MECSYCO benefits from the numerous scientific works which have demonstrated the integrative power of this formalism and give crucial guidelines to rigorously design wrappers. We demonstrate that our discrete framework can integrate a vast amount of continuous M&S tools by wrapping the Functional Mockup Interface (FMI) standard. To this end, we take advantage of DEVS efforts of the literature (namely, the DEV&DESS hybrid formalism and Quantized State System (QSS) solvers) to design DEVS wrappers for Functional Mockup Unit (FMU) components. As a side-effect, this wrapping is not restricted to MECSYCO but can be applied in any DEVS-based platform. We evaluate MECSYCO with the proof of concept of a smart heating use case, where we co-simulate non-DEVS-centric M&S tools.

Emerging Cyber-Physical Systems : An Overview

2018 ◽  
pp. 306-316
Author(s):  
Okolie S.O. ◽  
Kuyoro S.O. ◽  
Ohwo O. B
Keyword(s):  
Health Care ◽  
Economic Benefit ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Cyber Physical System ◽  
Economic Sectors ◽  
Strategic Research ◽  
Physical Systems ◽  
Security Issues ◽  
Wide Range

Cyber-Physical Systems (CPS) will revolutionize how humans relate with the physical world around us. Many grand challenges await the economically vital domains of transportation, health-care, manufacturing, agriculture, energy, defence, aerospace and buildings. Exploration of these potentialities around space and time would create applications which would affect societal and economic benefit. This paper looks into the concept of emerging Cyber-Physical system, applications and security issues in sustaining development in various economic sectors; outlining a set of strategic Research and Development opportunities that should be accosted, so as to allow upgraded CPS to attain their potential and provide a wide range of societal advantages in the future.

Security of Cyber-Physical Systems: A Generalized Algorithm for Intrusion Detection and Determining Security Robustness of Cyber Physical Systems using Logical Truth Tables

2013 ◽  
Vol 9 ◽  
Author(s):  
Curtis G. Northcutt
Keyword(s):  
Intrusion Detection ◽  
Cyber Security ◽  
Logical Truth ◽  
Cyber Physical Systems ◽  
Security Model ◽  
Security Measures ◽  
Physical Systems ◽  
Multiple Signal ◽  
Security Attack ◽  
Truth Tables

The recent proliferation of embedded cyber components in modern physical systems [1] has generated a variety of new security risks which threaten not only cyberspace, but our physical environment as well. Whereas earlier security threats resided primarily in cyberspace, the increasing marriage of digital technology with mechanical systems in cyber-physical systems (CPS), suggests the need for more advanced generalized CPS security measures. To address this problem, in this paper we consider the first step toward an improved security model: detecting the security attack. Using logical truth tables, we have developed a generalized algorithm for intrusion detection in CPS for systems which can be defined over discrete set of valued states. Additionally, a robustness algorithm is given which determines the level of security of a discrete-valued CPS against varying combinations of multiple signal alterations. These algorithms, when coupled with encryption keys which disallow multiple signal alteration, provide for a generalized security methodology for both cyber-security and cyber-physical systems.

Models of decision support in socio-cyber-physical systems

2019 ◽  
pp. 55-70 ◽  
Author(s):  
A. V. Smirnov ◽  
T. V. Levashova
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Situation Awareness ◽  
Cyber Physical Systems ◽  
Emergent Behavior ◽  
Emergent Properties ◽  
Decision Making Process ◽  
Physical Systems ◽  
Decision Support Models ◽  
Common Goals

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures.Purpose: Development of models for decision support in socio-cyber-physical systems.Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots.Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.

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