Study on high-performance and dependable blockchain-based computing

Blockchain technology is undergoing a tremendous growth choking itself up to its capacity and performance limits. It is exigently sought to address and respond to these issues, being mostly concerned about the scalability, performance and dependability of the blockchain system. This research will address and investigate on various, yet critical performance and dependability issues and problems as identified in blockchain-based crypto computing with specific respect to the on/off-balanced chain, the real-time chain; the slim chain; and a hybrid chain as to be proposed in this research. A hypothetical and theoretical design of each proposed crypto computing solution is developed in order to establish an engine for preliminary yet extensive parametric simulation, and the results are demonstrated and validated through an isolated testing built on Ethereum and Hyperledger open source-based prototype. With dependability referred to as the likelihood to be performed as desired, each hypothetical and theoretical model is built centered around the dependability of each proposed crypto solution to accommodate capabilities of the on/off-balanced crypto computing, the real-time computing, the slim-computing and the hybrid computing. A dependability model for each proposed crypto solution has been identified and defined along with various performance variables, and has ultimately provided a theoretical yet practical understanding of each crypto solution. A prototype, to demonstrate each proposed crypto solution and to validate its hypothetical and theoretical results, has been built by identifying and isolating the insertion points for necessary technology modification within Ethereum and Hyperledger open source to start out with and to ultimately realize a new core blockchain for optimal crypto computing focused on performance and dependability

Study on quantitative design for dynamic blockchain-based computing

This research proposes novel embedded Markovian queueing model-based quantitative models in order to establish a theoretical foundation to design a dynamic blockchain-based computing system with a specific interest in Ethereum. The proposed models commonly assume variable bulk arrivals of transactions in Poisson distribution, i.e., M^(1,n), where n the number of slots across all the mined transactions to be posted in a block or the current block. Firstly, a baseline model is proposed to have a static bulk service of transactions in exponential time, i.e., M^n, for posting the transactions in the current block, referred to as Variable Bulk Arrival and Static Bulk Service (VBASBS) queueing model of the M^(1,n)/M^n/1 type, in which note that n is fixed in order to demonstrate a static chain in terms of the size of the block. Secondly, an adaptive chain model, as a solution of dynamic blockchain in a reactive manner, is proposed based on a Variable Bulk Arrival and Variable Bulk Service (VBAVBS) queueing model of the M^(1,n)/M^(1,i,n)/1 type to provide a quantitative approach to design an adaptive chain that dynamically adapts the size of the block to varying performance trends, in which a state transitions from i back to 0, where 0<i</=n, are tracked in order to demonstrate the dynamically adaptive size of the block. Lastly, an asynchronous chain model, as a solution of dynamic blockchain in a proactive manner, is proposed based on a Variable Bulk Arrival and Asynchronous Bulk Service (VBAABS) queueing model is developed and presented to study and demonstrate the fully asynchronous and staged asynchronous chains. The analytical models are simulated extensively to compare the basic performances of the proposed models such as the average transaction waiting time, the average number of slots per block, and throughput. Further, extensive experiments are conducted in order to validate the analytical results by redesigning the source code of Ethereum to implement and demonstrate each of the proposed chains such as the baseline, the adaptive, the fully-asynchronous and the staged-asynchronous chains. The analytical results and the experimental results will be compared and discussed extensively

DECENTRALIZING THE INTERNET OF MEDICAL THINGS: THE INTERPLANETARY HEALTH LAYER

Medical mobile applications have the potential to revolutionize the healthcare industry by providing patients with easy access to their personal health information, enabling them to communicate with healthcare providers remotely and consequently improving patient outcomes by providing personalized health information. However, these applications are usually limited by privacy and security issues. A possible solution is to exploit decentralization distributing privacy concerns directly to users. Solutions enabling this vision are closely linked to Distributed Ledger Technologies that have the potential to revolutionize the healthcare industry by creating a secure and transparent system for managing patient data without a central authority. The decentralized nature of the technology allows for the creation of an international data layer that is accessible to authorized parties while preserving patient privacy. This thesis envisions the InterPlanetary Health Layer along with its implementation attempt called Halo Network and an Internet of Medical Things application called Balance as a use case. Throughout the thesis, we explore the benefits and limitations of using the technology, analyze potential use cases, and look out for future directions.Medical mobile applications have the potential to revolutionize the healthcare industry by providing patients with easy access to their personal health information, enabling them to communicate with healthcare providers remotely and consequently improving patient outcomes by providing personalized health information. However, these applications are usually limited by privacy and security issues. A possible solution is to exploit decentralization distributing privacy concerns directly to users. Solutions enabling this vision are closely linked to Distributed Ledger Technologies that have the potential to revolutionize the healthcare industry by creating a secure and transparent system for managing patient data without a central authority. The decentralized nature of the technology allows for the creation of an international data layer that is accessible to authorized parties while preserving patient privacy. This thesis envisions the InterPlanetary Health Layer along with its implementation attempt called Halo Network and an Internet of Medical Things application called Balance as a use case. Throughout the thesis, we explore the benefits and limitations of using the technology, analyze potential use cases, and look out for future directions

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions

Security Risk Management for the Internet of Things

In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot

Predictive Modeling for Fair and Efficient Transaction Inclusion in Proof-of-Work Blockchain Systems

This dissertation investigates the strategic integration of Proof-of-Work(PoW)-based blockchains and ML models to improve transaction inclusion, and consequently molding transaction fees, for clients using cryptocurrencies such as Bitcoin. The research begins with an in-depth exploration of the Bitcoin fee market, focusing on the interdependence between users and miners, and the emergence of a fee market in PoW-based blockchains. Our observations are used to formalize a transaction inclusion pattern. To support our research, we developed the Blockchain Analytics System (BAS) to acquire, store, and pre-process a local dataset of the Bitcoin blockchain. BAS employs various methods for data acquisition, including web scraping, web browser APIs, and direct access to the blockchain using Bitcoin Core software. We utilize time-series data analysis as a tool for predicting future trends, and transactions are sampled on a monthly basis with a fixed interval, incorporating a notion of relative time represented by block-creation epochs. We create a comprehensive model for transaction inclusion in a PoW-based blockchain system, with a focus on factors of revenue and fairness. Revenue serves as an incentive for miners to participate in the network and validate transactions, while fairness ensures equal opportunity for all users to have their transactions included upon paying an adequate fee value. The ML architecture used for prediction consists of three critical stages: the ingestion engine, the pre-processing stage, and the ML model. The ingestion engine processes and transforms raw data obtained from the blockchain, while the pre-processing phase transforms the data further into a suitable form for analysis, including feature extraction and additional data processing to generate a complete dataset. Our ML model showcases its effectiveness in predicting transaction inclusion, with an accuracy of more than 90%. Such a model enables users to save at least 10% on transaction fees while maintaining a likelihood of inclusion above 80%. Furthermore, adopting such model based on fairness and revenue, demonstrates that miners' average loss is never higher than 1.3%. Our research proves the efficacy of a formal transaction inclusion model and ML prototype in predicting transaction inclusion. The insights gained from our study shed light on the underlying mechanisms governing miners' decisions, improving the overall user experience, and enhancing the trust and reliability of cryptocurrencies. Consequently, this enables Bitcoin users to better select suitable fees and predict transaction inclusion with notable precision, contributing to the continued growth and adoption of cryptocurrencies

Cloud technology options towards Free Flow of Data

This whitepaper collects the technology solutions that the projects in the Data Protection, Security and Privacy Cluster propose to address the challenges raised by the working areas of the Free Flow of Data initiative. The document describes the technologies, methodologies, models, and tools researched and developed by the clustered projects mapped to the ten areas of work of the Free Flow of Data initiative. The aim is to facilitate the identification of the state-of-the-art of technology options towards solving the data security and privacy challenges posed by the Free Flow of Data initiative in Europe. The document gives reference to the Cluster, the individual projects and the technologies produced by them

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress