454 research outputs found

    Survivability modeling for cyber-physical systems subject to data corruption

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    Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii

    Quantitative dependability and interdependency models for large-scale cyber-physical systems

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    Cyber-physical systems link cyber infrastructure with physical processes through an integrated network of physical components, sensors, actuators, and computers that are interconnected by communication links. Modern critical infrastructures such as smart grids, intelligent water distribution networks, and intelligent transportation systems are prominent examples of cyber-physical systems. Developed countries are entirely reliant on these critical infrastructures, hence the need for rigorous assessment of the trustworthiness of these systems. The objective of this research is quantitative modeling of dependability attributes -- including reliability and survivability -- of cyber-physical systems, with domain-specific case studies on smart grids and intelligent water distribution networks. To this end, we make the following research contributions: i) quantifying, in terms of loss of reliability and survivability, the effect of introducing computing and communication technologies; and ii) identifying and quantifying interdependencies in cyber-physical systems and investigating their effect on fault propagation paths and degradation of dependability attributes. Our proposed approach relies on observation of system behavior in response to disruptive events. We utilize a Markovian technique to formalize a unified reliability model. For survivability evaluation, we capture temporal changes to a service index chosen to represent the extent of functionality retained. In modeling of interdependency, we apply correlation and causation analyses to identify links and use graph-theoretical metrics for quantifying them. The metrics and models we propose can be instrumental in guiding investments in fortification of and failure mitigation for critical infrastructures. To verify the success of our proposed approach in meeting these goals, we introduce a failure prediction tool capable of identifying system components that are prone to failure as a result of a specific disruptive event. Our prediction tool can enable timely preventative actions and mitigate the consequences of accidental failures and malicious attacks --Abstract, page iii

    Internet of Things is a revolutionary approach for future technology enhancement: a review

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    Abstract Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world

    Reliability and Maintenance

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    Amid a plethora of challenges, technological advances in science and engineering are inadvertently affecting an increased spectrum of today’s modern life. Yet for all supplied products and services provided, robustness of processes, methods, and techniques is regarded as a major player in promoting safety. This book on systems reliability, which equally includes maintenance-related policies, presents fundamental reliability concepts that are applied in a number of industrial cases. Furthermore, to alleviate potential cost and time-specific bottlenecks, software engineering and systems engineering incorporate approximation models, also referred to as meta-processes, or surrogate models to reproduce a predefined set of problems aimed at enhancing safety, while minimizing detrimental outcomes to society and the environment

    Survivable mesh-network design & optimization to support multiple QoP service classes

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    Every second, vast amounts of data are transferred over communication systems around the world, and as a result, the demands on optical infrastructures are extending beyond the traditional, ring-based architecture. The range of content and services available from the Internet is increasing, and network operations are constantly under pressure to expand their optical networks in order to keep pace with the ever increasing demand for higher speed and more reliable links

    Design and Service Provisioning Methods for Optical Networks in 5G and Beyond Scenarios

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    Network operators are deploying 5G while also considering the evolution towards 6G. They consider different enablers and address various challenges. One trend in the 5G deployment is network densification, i.e., deploying many small cell sites close to the users, which need a well-designed transport network (TN). The choice of the TN technology and the location for processing the 5G protocol stack functions are critical to contain capital and operational expenditures. Furthermore, it is crucial to ensure the resiliency of the TN infrastructure in case of a failure in nodes and/or links while the resource efficiency is maximized.Operators are also interested in 5G networks with flexibility and scalability features. In this context, one main question is where to deploy network functions so that the connectivity and compute resources are utilized efficiently while meeting strict service latency and availability requirements. Off-loading compute resources to large and central data centers (DCs) has some advantages, i.e., better utilization of compute resources at a lower cost. A backup path can be added to address service availability requirements when using compute off-loading strategies. This might impact the service blocking ratio and limit operators’ profit. The importance of this trade-off becomes more critical with the emergence of new 6G verticals.This thesis proposes novel methods to address the issues outlined above. To address the challenge of cost-efficient TN deployment, the thesis introduces a framework to study the total cost of ownership (TCO), latency, and reliability performance of a set of TN architectures for high-layer and low-layer functional split options. The architectural options are fiber- or microwave-based. To address the strict availability requirement, the thesis proposes a resource-efficient protection strategy against single node/link failure of the midhaul segment. The method selects primary and backup DCs for each aggregation node (i.e., nodes to which cell sites are connected) while maximizing the sharing of backup resources. Finally, to address the challenge of resource efficiency while provisioning services, the thesis proposes a backup-enhanced compute off-loading strategy (i.e., resource-efficient provisioning (REP)). REP selects a DC, a connectivity path, and (optionally) a backup path for each service request with the aim of minimizing resource usage while the service latency and availability requirements are met.Our results of the techno-economic assessment of the TN options reveal that, in some cases, microwave can be a good substitute for fiber technology. Several factors, including the geo-type, functional split option, and the cost of fiber trenching and microwave equipment, influence the effectiveness of the microwave. The considered architectures show similar latency and reliability performance and meet the 5G service requirements. The thesis also shows that a protection strategy based on shared connectivity and compute resources can lead to significant cost savings compared to benchmarks based on dedicated backup resources. Finally, the thesis shows that the proposed backup-enhanced compute off-loading strategy offers advantages in service blocking ratio and profit gain compared to a conventional off-loading approach that does not add a backup path. Benefits are even more evident considering next-generation services, e.g., expected on the market in 3 to 5 years, as the demand for services with stringent latency and availability will increase

    An Overview on Application of Machine Learning Techniques in Optical Networks

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    Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

    DeepTech – AI-IoT in smart cities

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    In this keynote, the evolution of intelligent computer systems will be examined. The need for human capital will be emphasised, as well as the need to follow one’s “gut instinct” in problem-solving. We will look at the benefits of combining information and knowledge to solve complex problems and will examine how knowledge engineering facilitates the integration of different algorithms. Furthermore, we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems. It will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities

    A World-Class University-Industry Consortium for Wind Energy Research, Education, and Workforce Development: Final Technical Report

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