A Type System for First-Class Layers with Inheritance, Subtyping, and Swapping

Context-Oriented Programming (COP) is a programming paradigm to encourage modularization of context-dependent software. Key features of COP are layers---modules to describe context-dependent behavioral variations of a software system---and their dynamic activation, which can modify the behavior of multiple objects that have already been instantiated. Typechecking programs written in a COP language is difficult because the activation of a layer can even change objects' interfaces. Inoue et al. have informally discussed how to make JCop, an extension of Java for COP by Appeltauer et al., type-safe. In this article, we formalize a small COP language called ContextFJ$_{<:}$ with its operational semantics and type system and show its type soundness. The language models main features of the type-safe version of JCop, including dynamically activated first-class layers, inheritance of layer definitions, layer subtyping, and layer swapping

Modeling and Simulation of NFC Logical Layer Peer-to-Peer Mode using CPN and TA

Network communication technologies have been growing explosively due to the increasing demand on faster and simpler communication; hence, providing new communication technologies is a challenging task. To make this task easy, many researchers have developed different network modeling and simulation tools with different characteristics. In this paper, simulation of Near Field Communication (NFC) logical layer control protocol is proposed to investigate efficiency of NFC device in peer-to-peer mode. For this purpose, Colored Petri Net (CPN) and Timed Automata (TA) have been used for analyses. According to the results, CPN was better than TA for simulating NFC logical layer control protocol because it could provide more details on complex communication network.DOI:http://dx.doi.org/10.11591/ijece.v4i2.515

ContextErlang: A language for distributed context-aware self-adaptive applications

Self-adaptive software modifies its behavior at run time to satisfy changing requirements in a dynamic environment. Context-oriented programming (COP) has been recently proposed as a specialized programming paradigm for context-aware and adaptive systems. COP mostly focuses on run time adaptation of the application’s behavior by supporting modular descriptions of behavioral variations. However, self-adaptive applications must satisfy additional requirements, such as distribution and concurrency, support for unforeseen changes and enforcement of correct behavior in the presence of dynamic change. Addressing these issues at the language level requires a holistic design that covers all aspects and takes into account the possibly cumbersome interaction of those features, for example concurrency and dynamic change. We present ContextErlang, a COP programming language in which adaptive abstractions are seamlessly integrated with distribution and concurrency. We define ContextErlang’s formal semantics, validated through an executable prototype, and we show how it supports formal proofs that the language design ensures satisfaction of certain safety requirements. We provide empirical evidence that ContextErlang is an effective solution through case studies and a performance assessment. We also show how the same design principles that lead to the development of ContextErlang can be followed to systematically design contextual extensions of other languages. A concrete example is presented concerning ContextScala

Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation

A novel approach to hardware fault tolerance is demonstrated that takes inspiration from the human immune system as a method of fault detection. The human immune system is a remarkable system of interacting cells and organs that protect the body from invasion and maintains reliable operation even in the presence of invading bacteria or viruses. This paper seeks to address the field of electronic hardware fault tolerance from an immunological perspective with the aim of showing how novel methods based upon the operation of the immune system can both complement and create new approaches to the development of fault detection mechanisms for reliable hardware systems. In particular, it is shown that by use of partial matching, as prevalent in biological systems, high fault coverage can be achieved with the added advantage of reducing memory requirements. The development of a generic finite-state-machine immunization procedure is discussed that allows any system that can be represented in such a manner to be "immunized" against the occurrence of faulty operation. This is demonstrated by the creation of an immunized decade counter that can detect the presence of faults in real tim

Time-Sensitive Networking for Industrial Automation: Challenges, Opportunities, and Directions

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) into industrial applications, industrial automation is undergoing tremendous change, especially with regard to improving efficiency and reducing the cost of products. Industrial automation applications are often required to transmit time- and safety-critical data to monitor and control industrial processes, especially for critical control systems. There are a number of solutions to meet these requirements (e.g., priority-based real-time schedules and closed-loop feedback control systems). However, due to their different processing capabilities (e.g., in the end devices and network switches), different vendors may come out with distinct solutions, and this makes the large-scale integration of devices from different vendors difficult or impossible. IEEE 802.1 Time-Sensitive Networking (TSN) is a standardization group formed to enhance and optimize the IEEE 802.1 network standards, especially for Ethernet-based networks. These solutions can be evolved and adapted into a cross-industry scenario, such as a large-scale distributed industrial plant, which requires multiple industrial entities working collaboratively. This paper provides a comprehensive review on the current advances in TSN standards for industrial automation. We present the state-of-the-art IEEE TSN standards and discuss the opportunities and challenges when integrating each protocol into the industry domains. Finally, we discuss some promising research about applying the TSN technology to industrial automation applications

A qualitative cybersecurity analysis of time-triggered communication networks in automotive systems

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/).Security is gaining increasing importance in automotive systems, driven by technical innovations. For example, automotive vehicles become more open systems, allowing the communication with other traffic participants and road infrastructure. Also, automotive vehicles are provided with increased autonomy which raises severe safety concerns, and consequently also security concerns – both concerns that interweave in such systems. In this paper we present a qualitative cybersecurity analysis by comparing different time-triggered (TT) communication networks. While TT communication networks have been analysed extensively for dependability, the contribution of this work is to identify security-related benefits that TT communication networks can provide. In particular, their mechanisms for spacial and temporal encapsulation of network traffic are instrumental to improve network security. The security arguments can be used as a design guide for implementing critical communication in flexible network standards like TSN.Peer reviewe

Design of module for demonstration and testing of system basis chips NCV7471

Práce se zabývá návrhem automobilové elektronické řídicí jednotky (ECU) s funkcí partial networking definovanou normou ISO 11898-6. Cílem je navrhnout a vytvořit demonstrační ECU s použitím system basis chip NCV7471. Protože NCV7471 obsahuje standardní CAN transceiver, funkce partial networking je realizována pouze softwarem řídicí jednotky. Práce zvažuje možné způsoby realizace jak HW, tak SW části, tak aby byla zajištěna nízká spotřeba ECU v různých operačních módech, a snaží se sledovat současné trendy v automobilovém průmyslu.The thesis deals with the design of automotive ECU with partial networking (PN) functionality according to ISO 11898-6. Aim is to design and create evaluation electronic control unit (ECU) using system basis chip NCV7471. Since NCV7471 integrates standard CAN transceiver without HW PN support, the PN functionality is realized by ECU software. This thesis considers possible ways of realization in HW and SW domain to maintain low power consumption of the ECU in different operational modes in order to follow current trends in automotive industry.

Improved RBF Network Intrusion Detection Model Based on Edge Computing with Multi-algorithm Fusion

Edge computing is difficult to deploy a complete and reliable security strategy due to its distributed computing architecture and inherent heterogeneity of equipment and limited resources. When malicious attacks occur, the loss will be immeasurable. RBF neural network has strong nonlinear representation ability and fast learning convergence speed, which is suitable for intrusion detection of edge detection industrial control network. In this paper, an improved RBF network intrusion detection model based on multi-algorithm fusion is proposed. kernel principal component analysis (KPCA) is used to extract data dimension and simplify data representation. Then subtractive clustering algorithm(SCM) and grey wolf algorithm(GWO) are used to jointly optimize RBF neural network parameters to avoid falling into local optimum, reduce the calculation of model training and improve the detection accuracy. The algorithm can better adapt to the edge computing platform with weak computing ability and bearing capacity, and realize real-time data analysis.The experimental results of BATADAL data set and Gas data set show that the accuracy of the algorithm is over 99% and the training time of larger samples is shortened by 50 times for BATADAL data set. The results show that the improved RBF network is effective in improving the convergence speed and accuracy in intrusion detection