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

Robust, Resilient and Reliable Architecture for V2X Communication

The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive these further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plug- in electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everything- to-everything (X2X) communications

A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing

Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users' sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristic. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms that are suitable for FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role to block those cyber attacks. To this end, we highlight some potential solutions towards the coupling of blockchain and federated learning that can offer high levels of reliability, data privacy, and distributed computing performance

An Overview of Blockchain Integration with Robotics and Artificial Intelligence

Blockchain technology is growing everyday at a fast-passed rhythm and it's possible to integrate it with many systems, namely Robotics with AI services. However, this is still a recent field and there isn't yet a clear understanding of what it could potentially become. In this paper, we conduct an overview of many different methods and platforms that try to leverage the power of blockchain into robotic systems, to improve AI services or to solve problems that are present in the major blockchains, which can lead to the ability of creating robotic systems with increased capabilities and security. We present an overview, discuss the methods and conclude the paper with our view on the future of the integration of these technologies.info:eu-repo/semantics/draf

Intégration de la blockchain à l'Internet des objets

L'Internet des objets (IdO) est en train de transformer l'industrie traditionnelle en une industrie intelligente où les décisions sont prises en fonction des données. L'IdO interconnecte de nombreux objets (ou dispositifs) qui effectuent des tâches complexes (e.g., la collecte de données, l'optimisation des services, la transmission de données). Toutefois, les caractéristiques intrinsèques de l'IdO entraînent plusieurs problèmes, tels que la décentralisation, une faible interopérabilité, des problèmes de confidentialité et des failles de sécurité. Avec l'évolution attendue de l'IdO dans les années à venir, il est nécessaire d'assurer la confiance dans cette énorme source d'informations entrantes. La blockchain est apparue comme une technologie clé pour relever les défis de l'IdO. En raison de ses caractéristiques saillantes telles que la décentralisation, l'immuabilité, la sécurité et l'auditabilité, la blockchain a été proposée pour établir la confiance dans plusieurs applications, y compris l'IdO. L'intégration de la blockchain a l'IdO ouvre la porte à de nouvelles possibilités qui améliorent intrinsèquement la fiabilité, la réputation, et la transparence pour toutes les parties concernées, tout en permettant la sécurité. Cependant, les blockchains classiques sont coûteuses en calcul, ont une évolutivité limitée, et nécessitent une bande passante élevée, ce qui les rend inadaptées aux environnements IdO à ressources limitées. L'objectif principal de cette thèse est d'utiliser la blockchain comme un outil clé pour améliorer l'IdO. Pour atteindre notre objectif, nous relevons les défis de la fiabilité des données et de la sécurité de l'IdO en utilisant la blockchain ainsi que de nouvelles technologies émergentes, notamment l'intelligence artificielle (IA). Dans la première partie de cette thèse, nous concevons une blockchain qui garantit la fiabilité des données, adaptée à l'IdO. Tout d'abord, nous proposons une architecture blockchain légère qui réalise la décentralisation en formant un réseau superposé où les dispositifs à ressources élevées gèrent conjointement la blockchain. Ensuite, nous présentons un algorithme de consensus léger qui réduit la puissance de calcul, la capacité de stockage, et la latence de la blockchain. Dans la deuxième partie de cette thèse, nous concevons un cadre sécurisé pour l'IdO tirant parti de la blockchain. Le nombre croissant d'attaques sur les réseaux IdO, et leurs graves effets, rendent nécessaire la création d'un IdO avec une sécurité plus sophistiquée. Par conséquent, nous tirons parti des modèles IA pour fournir une intelligence intégrée dans les dispositifs et les réseaux IdO afin de prédire et d'identifier les menaces et les vulnérabilités de sécurité. Nous proposons un système de détection d'intrusion par IA qui peut détecter les comportements malveillants et contribuer à renforcer la sécurité de l'IdO basé sur la blockchain. Ensuite, nous concevons un mécanisme de confiance distribué basé sur des contrats intelligents de blockchain pour inciter les dispositifs IdO à se comporter de manière fiable. Les systèmes IdO existants basés sur la blockchain souffrent d'une bande passante de communication et d’une évolutivité limitée. Par conséquent, dans la troisième partie de cette thèse, nous proposons un apprentissage machine évolutif basé sur la blockchain pour l'IdO. Tout d'abord, nous proposons un cadre IA multi-tâches qui exploite la blockchain pour permettre l'apprentissage parallèle de modèles. Ensuite, nous concevons une technique de partitionnement de la blockchain pour améliorer l'évolutivité de la blockchain. Enfin, nous proposons un algorithme d'ordonnancement des dispositifs pour optimiser l'utilisation des ressources, en particulier la bande passante de communication.Abstract : The Internet of Things (IoT) is reshaping the incumbent industry into a smart industry featured with data-driven decision making. The IoT interconnects many objects (or devices) that perform complex tasks (e.g., data collection, service optimization, data transmission). However, intrinsic features of IoT result in several challenges, such as decentralization, poor interoperability, privacy issues, and security vulnerabilities. With the expected evolution of IoT in the coming years, there is a need to ensure trust in this huge source of incoming information. Blockchain has emerged as a key technology to address the challenges of IoT. Due to its salient features such as decentralization, immutability, security, and auditability, blockchain has been proposed to establish trust in several applications, including IoT. The integration of IoT and blockchain opens the door for new possibilities that inherently improve trustworthiness, reputation, and transparency for all involved parties, while enabling security. However, conventional blockchains are computationally expensive, have limited scalability, and incur significant bandwidth, making them unsuitable for resource-constrained IoT environments. The main objective of this thesis is to leverage blockchain as a key enabler to improve the IoT. Toward our objective, we address the challenges of data reliability and IoT security using the blockchain and new emerging technologies, including machine learning (ML). In the first part of this thesis, we design a blockchain that guarantees data reliability, suitable for IoT. First, we propose a lightweight blockchain architecture that achieves decentralization by forming an overlay network where high-resource devices jointly manage the blockchain. Then, we present a lightweight consensus algorithm that reduces blockchain computational power, storage capability, and latency. In the second part of this thesis, we design a secure framework for IoT leveraging blockchain. The increasing number of attacks on IoT networks, and their serious effects, make it necessary to create an IoT with more sophisticated security. Therefore, we leverage ML models to provide embedded intelligence in the IoT devices and networks to predict and identify security threats and vulnerabilities. We propose a ML intrusion detection system that can detect malicious behaviors and help further bolster the blockchain-based IoT’s security. Then, we design a distributed trust mechanism based on blockchain smart contracts to incite IoT devices to behave reliably. Existing blockchain-based IoT systems suffer from limited communication bandwidth and scalability. Therefore, in the third part of this thesis, we propose a scalable blockchain-based ML for IoT. First, we propose a multi-task ML framework that leverages the blockchain to enable parallel model learning. Then, we design a blockchain partitioning technique to improve the blockchain scalability. Finally, we propose a device scheduling algorithm to optimize resource utilization, in particular communication bandwidth

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence