Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance

The paper presents a Stateflow based network test-bed to validate real-time optimal control algorithms. Genetic Algorithm (GA) based time domain performance index minimization is attempted for tuning of PI controller to handle a balanced lag and delay type First Order Plus Time Delay (FOPTD) process over network. The tuning performance is validated on a real-time communication network with artificially simulated stochastic delay, packet loss and out-of order packets characterizing the network.Comment: 6 pages, 12 figure

Reinforcement learning for efficient network penetration testing

Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The proposed system is named Intelligent Automated Penetration Testing System (IAPTS) consisting of a module that integrates with industrial PT frameworks to enable them to capture information, learn from experience, and reproduce tests in future similar testing cases. IAPTS aims to save human resources while producing much-enhanced results in terms of time consumption, reliability and frequency of testing. IAPTS takes the approach of modeling PT environments and tasks as a partially observed Markov decision process (POMDP) problem which is solved by POMDP-solver. Although the scope of this paper is limited to network infrastructures PT planning and not the entire practice, the obtained results support the hypothesis that RL can enhance PT beyond the capabilities of any human PT expert in terms of time consumed, covered attacking vectors, accuracy and reliability of the outputs. In addition, this work tackles the complex problem of expertise capturing and re-use by allowing the IAPTS learning module to store and re-use PT policies in the same way that a human PT expert would learn but in a more efficient way

Software engineering: Testing real-time embedded systems using timed automata based approaches

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Real-time Embedded Systems (RTESs) have an increasing role in controlling society infrastructures that we use on a day-to-day basis. RTES behaviour is not based solely on the interactions it might have with its surrounding environment, but also on the timing requirements it induces. As a result, ensuring that an RTES behaves correctly is non-trivial, especially after adding time as a new dimension to the complexity of the testing process. This research addresses the problem of testing RTESs from Timed Automata (TA) specification by the following. First, a new Priority-based Approach (PA) for testing RTES modelled formally as UPPAAL timed automata (TA variant) is introduced. Test cases generated according to a proposed timed adequacy criterion (clock region coverage) are divided into three sets of priorities, namely boundary, out-boundary and in-boundary. The selection of which set is most appropriate for a System Under Test (SUT) can be decided by the tester according to the system type, time specified for the testing process and its budget. Second, PA is validated in comparison with four well-known timed testing approaches based on TA using Specification Mutation Analysis (SMA). To enable the validation, a set of timed and functional mutation operators based on TA is introduced. Three case studies are used to run SMA. The effectiveness of timed testing approaches are determined and contrasted according to the mutation score which shows that our PA achieves high mutation adequacy score compared with others. Third, to enhance the applicability of PA, a new testing tool (GeTeX) that deploys PA is introduced. In its current version, GeTeX supports Control Area Network (CAN) applications. GeTeX is validated by developing a prototype for that purpose. Using GeTeX, PA is also empirically validated in comparison with some TA testing approaches using a complete industrial-strength test bed. The assessment is based on fault coverage, structural coverage, the length of generated test cases and a proposed assessment factor. The assessment is based on fault coverage, structural coverage, the length of generated test cases and a proposed assessment factor. The assessment results confirmed the superiority of PA over the other test approaches. The overall assessment factor showed that structural and fault coverage scores of PA with respect to the length of its tests were better than the others proving the applicability of PA. Finally, an Analytical Hierarchy Process (AHP) decision-making framework for our PA is developed. The framework can provide testers with a systematic approach by which they can prioritise the available PA test sets that best fulfils their testing requirements. The AHP framework developed is based on the data collected heuristically from the test bed and data collected by interviewing testing experts. The framework is then validated using two testing scenarios. The decision outcomes of the AHP framework were significantly correlated to those of testing experts which demonstrated the soundness and validity of the framework.This study is funded by Damascus University, Syri

Harnessing the Power of Digital Twins for Enhanced Material Behavior Prediction and Manufacturing Process Optimization in Materials Engineering

The advent of Industry 4.0 and the digital revolution have brought forth innovative technologies such as digital twins, which have the potential to redefine the landscape of materials engineering. Digital twins, virtual representations of physical entities, can model and predict material behavior, enabling enhanced design, testing, and manufacturing of materials. However, the comprehensive utilization of digital twins for predictive analysis and process optimization in materials engineering remains largely uncharted. This research intends to delve into this intriguing intersection, investigating the capabilities of digital twins in predicting material behavior and optimizing manufacturing processes, thereby contributing to the evolution of advanced materials manufacturing. Our study will commence with a detailed exploration of the concept of digital twins and their specific applications in materials engineering, emphasizing their ability to simulate intricate material behaviors and processes in a virtual environment. Subsequently, we will focus on exploiting digital twins for predicting diverse material behaviors such as mechanical properties, failure modes, and phase transformations, demonstrating how digital twins can utilize a combination of historical data, real-time monitoring, and sophisticated algorithms to predict outcomes accurately. Furthermore, we will delve into the role of digital twins in optimizing materials manufacturing processes, including casting, machining, and additive manufacturing, illustrating how digital twins can model these processes, identify potential issues, and suggest optimal parameters. We will present detailed case studies to provide practical insights into the implementation of digital twins in materials engineering, including the advantages and challenges. The final segment of our research will address the current challenges in implementing digital twins, such as data quality, model validation, and computational demands, proposing potential solutions and outlining future directions. This research aims to underline the transformative potential of digital twins in materials engineering, thereby paving the way for more efficient, sustainable, and intelligent material design and manufacturing processes

On Experimentation in Software-Intensive Systems

Context: Delivering software that has value to customers is a primary concern of every software company. Prevalent in web-facing companies, controlled experiments are used to validate and deliver value in incremental deployments. At the same that web-facing companies are aiming to automate and reduce the cost of each experiment iteration, embedded systems companies are starting to adopt experimentation practices and leverage their activities on the automation developments made in the online domain. Objective: This thesis has two main objectives. The first objective is to analyze how software companies can run and optimize their systems through automated experiments. This objective is investigated from the perspectives of the software architecture, the algorithms for the experiment execution and the experimentation process. The second objective is to analyze how non web-facing companies can adopt experimentation as part of their development process to validate and deliver value to their customers continuously. This objective is investigated from the perspectives of the software development process and focuses on the experimentation aspects that are distinct from web-facing companies. Method: To achieve these objectives, we conducted research in close collaboration with industry and used a combination of different empirical research methods: case studies, literature reviews, simulations, and empirical evaluations. Results: This thesis provides six main results. First, it proposes an architecture framework for automated experimentation that can be used with different types of experimental designs in both embedded systems and web-facing systems. Second, it proposes a new experimentation process to capture the details of a trustworthy experimentation process that can be used as the basis for an automated experimentation process. Third, it identifies the restrictions and pitfalls of different multi-armed bandit algorithms for automating experiments in industry. This thesis also proposes a set of guidelines to help practitioners select a technique that minimizes the occurrence of these pitfalls. Fourth, it proposes statistical models to analyze optimization algorithms that can be used in automated experimentation. Fifth, it identifies the key challenges faced by embedded systems companies when adopting controlled experimentation, and we propose a set of strategies to address these challenges. Sixth, it identifies experimentation techniques and proposes a new continuous experimentation model for mission-critical and business-to-business. Conclusion: The results presented in this thesis indicate that the trustworthiness in the experimentation process and the selection of algorithms still need to be addressed before automated experimentation can be used at scale in industry. The embedded systems industry faces challenges in adopting experimentation as part of its development process. In part, this is due to the low number of users and devices that can be used in experiments and the diversity of the required experimental designs for each new situation. This limitation increases both the complexity of the experimentation process and the number of techniques used to address this constraint

From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation

Context: Competitions for self-driving cars facilitated the development and research in the domain of autonomous vehicles towards potential solutions for the future mobility. Objective: Miniature vehicles can bridge the gap between simulation-based evaluations of algorithms relying on simplified models, and those time-consuming vehicle tests on real-scale proving grounds. Method: This article combines findings from a systematic literature review, an in-depth analysis of results and technical concepts from contestants in a competition for self-driving miniature cars, and experiences of participating in the 2013 competition for self-driving cars. Results: A simulation-based development platform for real-scale vehicles has been adapted to support the development of a self-driving miniature car. Furthermore, a standardized platform was designed and realized to enable research and experiments in the context of future mobility solutions. Conclusion: A clear separation between algorithm conceptualization and validation in a model-based simulation environment enabled efficient and riskless experiments and validation. The design of a reusable, low-cost, and energy-efficient hardware architecture utilizing a standardized software/hardware interface enables experiments, which would otherwise require resources like a large real-scale test track.Comment: 17 pages, 19 figues, 2 table

Cross-Layer Platform for Dynamic, Energy-Efficient Optical Networks

The design of the next-generation Internet infrastructure is driven by the need to sustain the massive growth in bandwidth demands. Novel, energy-efficient, optical networking technologies and architectures are required to effectively meet the stringent performance requirements with low cost and ultrahigh energy efficiencies. In this thesis, a cross-layer communications platform is proposed to enable greater intelligence and functionality on the physical layer. Providing the optical layer with advanced networking capabilities will facilitate the dynamic management and optimization of optical switching based on performance monitoring measurements and higher-layer attributes. The cross-layer platform aims to create a new framework for networks to incorporate packet-scale measurement subsystems and techniques for monitoring the health of the optical channel. This will allow for quality-of-service- and energy-aware routing schemes, as well as an enhanced awareness of the optical data signals. This thesis first presents the design and development of an optical packet switching fabric. Leveraging a networking test-bed environment to validate networking hypotheses, advanced switching functionalities are demonstrated, including the support for quality-of-service based routing and packet multicasting. The investigated cross-layering is based on emerging optical technologies, enabling packet protection techniques and packet-rate switching fabric reconfiguration. Coupled with fast performance monitoring, the platform will achieve significant performance gains within the endeavor of all-optical switching. Allowing for a more intelligent, programmable optical layer aims to support greater flexibility with respect to bandwidth allocation and potentially a significant reduction in the network's energy consumption. The ultimate deliverable of this work is a high-performance, cross-layer enabled optical network node. The experimental demonstration of an initial prototype creates a dynamic network element with distributed control plane management, featuring fast packet-rate optical switching capabilities and embedded physical-layer performance monitoring modules. The cross-layer box enables an intelligent traffic delivery system that can dynamically manipulate optical switching on a packet-granular scale. With the goal of achieving advanced multi-layer routing and control algorithms, the network node requires an intelligent co-optimization across all the layers. The proposed cross-layer design should drive optical technologies and architectures in an innovative way, in order to fulfill the void between the design of basic photonic devices and the networking protocols that use them. The performance of the entire network -- from the optical components, to the routing algorithms and user applications -- should be optimized in concert. This contribution to the area of cross-layer network design creates an adaptable optical pipe that is extremely flexible and intelligent aware of both the physical optical signals and higher-layer requirements. The impact of this work will be seen in the realization of dynamic, energy-efficient optical communication links in future networking infrastructures

Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop

The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcement Learning (RL) has great potential for autonomously training agents to perform complex control tasks with minimal human intervention. Due to costly data generation and safety constraints, however, its application is mostly limited to purely simulated domains. To use RL effectively in embedded system function development, the generated agents must be able to handle real-world applications. In this context, this work focuses on accelerating the training process of RL agents by combining Transfer Learning (TL) and X-in-the-Loop (XiL) simulation. For the use case of transient exhaust gas re-circulation control for an internal combustion engine, use of a computationally cheap Model-in-the-Loop (MiL) simulation is made to select a suitable algorithm, fine-tune hyperparameters, and finally train candidate agents for the transfer. These pre-trained RL agents are then fine-tuned in a Hardware-in-the-Loop (HiL) system via TL. The transfer revealed the need for adjusting the reward parameters when advancing to real hardware. Further, the comparison between a purely HiL-trained and a transferred agent showed a reduction of training time by a factor of 5.9. The results emphasize the necessity to train RL agents with real hardware, and demonstrate that the maturity of the transferred policies affects both training time and performance, highlighting the strong synergies between TL and XiL simulation

Co-design of Security Aware Power System Distribution Architecture as Cyber Physical System

The modern smart grid would involve deep integration between measurement nodes, communication systems, artificial intelligence, power electronics and distributed resources. On one hand, this type of integration can dramatically improve the grid performance and efficiency, but on the other, it can also introduce new types of vulnerabilities to the grid. To obtain the best performance, while minimizing the risk of vulnerabilities, the physical power system must be designed as a security aware system. In this dissertation, an interoperability and communication framework for microgrid control and Cyber Physical system enhancements is designed and implemented taking into account cyber and physical security aspects. The proposed data-centric interoperability layer provides a common data bus and a resilient control network for seamless integration of distributed energy resources. In addition, a synchronized measurement network and advanced metering infrastructure were developed to provide real-time monitoring for active distribution networks. A hybrid hardware/software testbed environment was developed to represent the smart grid as a cyber-physical system through hardware and software in the loop simulation methods. In addition it provides a flexible interface for remote integration and experimentation of attack scenarios. The work in this dissertation utilizes communication technologies to enhance the performance of the DC microgrids and distribution networks by extending the application of the GPS synchronization to the DC Networks. GPS synchronization allows the operation of distributed DC-DC converters as an interleaved converters system. Along with the GPS synchronization, carrier extraction synchronization technique was developed to improve the system’s security and reliability in the case of GPS signal spoofing or jamming. To improve the integration of the microgrid with the utility system, new synchronization and islanding detection algorithms were developed. The developed algorithms overcome the problem of SCADA and PMU based islanding detection methods such as communication failure and frequency stability. In addition, a real-time energy management system with online optimization was developed to manage the energy resources within the microgrid. The security and privacy were also addressed in both the cyber and physical levels. For the physical design, two techniques were developed to address the physical privacy issues by changing the current and electromagnetic signature. For the cyber level, a security mechanism for IEC 61850 GOOSE messages was developed to address the security shortcomings in the standard