Blockchain-based Digital Twins:Research Trends, Issues, and Future Challenges

Industrial processes rely on sensory data for decision-making processes, risk assessment, and performance evaluation. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the dissemination of trustworthy data. For the physical data to be trustworthy, it needs to be cross validated through multiple sensor sources with overlapping fields of view. Cross-validated data can then be stored on the blockchain, to maintain its integrity and trustworthiness. Once trustworthy data is recorded on the blockchain, product lifecycle events can be fed into data-driven systems for process monitoring, diagnostics, and optimized control. In this regard, digital twins (DTs) can be leveraged to draw intelligent conclusions from data by identifying the faults and recommending precautionary measures ahead of critical events. Empowering DTs with blockchain in industrial use cases targets key challenges of disparate data repositories, untrustworthy data dissemination, and the need for predictive maintenance. In this survey, while highlighting the key benefits of using blockchain-based DTs, we present a comprehensive review of the state-of-the-art research results for blockchain-based DTs. Based on the current research trends, we discuss a trustworthy blockchain-based DTs framework. We also highlight the role of artificial intelligence in blockchain-based DTs. Furthermore, we discuss the current and future research and deployment challenges of blockchain-supported DTs that require further investigation.</p

Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters

Cloud data centers require an operating system to manage resources and satisfy operational requirements and management objectives. The growth of popularity in cloud services causes the appearance of a new spectrum of services with sophisticated workload and resource management requirements. Also, data centers are growing by addition of various type of hardware to accommodate the ever-increasing requests of users. Nowadays a large percentage of cloud resources are executing data-intensive applications which need continuously changing workload fluctuations and specific resource management. To this end, cluster computing frameworks are shifting towards distributed resource management for better scalability and faster decision making. Such systems benefit from the parallelization of control and are resilient to failures. Throughout this thesis we investigate algorithms, protocols and techniques to address these challenges in large-scale data centers. We introduce a distributed resource management framework which consolidates virtual machine to as few servers as possible to reduce the energy consumption of data center and hence decrease the cost of cloud providers. This framework can characterize the workload of virtual machines and hence handle trade-off energy consumption and Service Level Agreement (SLA) of customers efficiently. The algorithm is highly scalable and requires low maintenance cost with dynamic workloads and it tries to minimize virtual machines migration costs. We also introduce a scalable and distributed probe-based scheduling algorithm for Big data analytics frameworks. This algorithm can efficiently address the problem job heterogeneity in workloads that has appeared after increasing the level of parallelism in jobs. The algorithm is massively scalable and can reduce significantly average job completion times in comparison with the-state of-the-art. Finally, we propose a probabilistic fault-tolerance technique as part of the scheduling algorithm

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

Dependable and Scalable Public Ledger for Policy Compliance, a Blockchain Based Approach

Policies and regulations, such as the European Union General Data Protection Regulation (EU GDPR), have been enforced to protect personal data from abuse during storage and processing. We design and implement a prototype scheme that could 1) provide a public ledger of policy compliance to help the public make informative decisions when choosing data services; 2) provide support to the organizations for identifying violations and improve their ability of compliance. Honest organizations could then benefit from their positive records on the public ledger. To address the scalability problem inherent in the Blockchain-based systems, we develop algorithms and leverage state channels to implement an on-chain-hash-off-chain data structure. We identify the verification of the information from the external world as a critical problem when using Blockchains as public ledgers, and address this problem by the incentive-based trust model implied by state channels. We propose the Verifiable Off-Chain Message Channel as the integrated solution for leveraging blockchain technology as a general-purpose recording mechanism and support our thesis with performance experiments. Finally, we suggest a sticky policy mechanism as the evidence source for the public ledger to monitor cross-boundary policy compliance

Internet architecture, freedom of expression and social responsibility: Critical realism and proposals for a better future

The article opens by explaining the architecture of the Internet. Given its present raison d'être, a free highway allowing maximum freedom, one may argue that the bounds of free expression are broader in scope on the Net compared with the bounds of legitimate speech allowed on other forms of communication. Contesting this assertion, it is argued that legally speaking, there is no difference between electronic communication and other forms of communication. I probe some problematic forms of expression: terrorism, criminal activity, and cyberbullying, arguing that freedom of expression is important but so is social responsibility. The article concludes by offering a new paradigm Internet for the future called CleaNet©. CleaNet© will be sensitive to prevailing cultural norms of each and every society and will be clean of content that the society deems to be dangerous and antisocial. No cyberbullying, child pornography, hateful bigotry, criminal activity, and terrorist material will be available on the new Net. Netusers, with the cooperation of ISPs and web-hosting companies, will together decide which content will be considered illegitimate and unworthy to be excluded from CleaNet©

Systems-compatible Incentives

Originally, the Internet was a technological playground, a collaborative endeavor among researchers who shared the common goal of achieving communication. Self-interest used not to be a concern, but the motivations of the Internet's participants have broadened. Today, the Internet consists of millions of commercial entities and nearly 2 billion users, who often have conflicting goals. For example, while Facebook gives users the illusion of access control, users do not have the ability to control how the personal data they upload is shared or sold by Facebook. Even in BitTorrent, where all users seemingly have the same motivation of downloading a file as quickly as possible, users can subvert the protocol to download more quickly without giving their fair share. These examples demonstrate that protocols that are merely technologically proficient are not enough. Successful networked systems must account for potentially competing interests. In this dissertation, I demonstrate how to build systems that give users incentives to follow the systems' protocols. To achieve incentive-compatible systems, I apply mechanisms from game theory and auction theory to protocol design. This approach has been considered in prior literature, but unfortunately has resulted in few real, deployed systems with incentives to cooperate. I identify the primary challenge in applying mechanism design and game theory to large-scale systems: the goals and assumptions of economic mechanisms often do not match those of networked systems. For example, while auction theory may assume a centralized clearing house, there is no analog in a decentralized system seeking to avoid single points of failure or centralized policies. Similarly, game theory often assumes that each player is able to observe everyone else's actions, or at the very least know how many other players there are, but maintaining perfect system-wide information is impossible in most systems. In other words, not all incentive mechanisms are systems-compatible. The main contribution of this dissertation is the design, implementation, and evaluation of various systems-compatible incentive mechanisms and their application to a wide range of deployable systems. These systems include BitTorrent, which is used to distribute a large file to a large number of downloaders, PeerWise, which leverages user cooperation to achieve lower latencies in Internet routing, and Hoodnets, a new system I present that allows users to share their cellular data access to obtain greater bandwidth on their mobile devices. Each of these systems represents a different point in the design space of systems-compatible incentives. Taken together, along with their implementations and evaluations, these systems demonstrate that systems-compatibility is crucial in achieving practical incentives in real systems. I present design principles outlining how to achieve systems-compatible incentives, which may serve an even broader range of systems than considered herein. I conclude this dissertation with what I consider to be the most important open problems in aligning the competing interests of the Internet's participants

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

Towards Optimal and Practical Asynchronous Byzantine Fault Tolerant Protocols

With recent advancements in blockchain technology, people expect Byzantine fault tolerant (BFT) protocols to be deployed more frequently in wide-area networks (WAN) as opposed to conventional in-house settings. Asynchronous BFT protocols, which do not rely on any form of timing assumption, are arguably robust in such a setting. Asynchronous BFT protocols have been studied since the 1980s, but these asynchronous BFT works mainly focus on understanding the theoretical limits and possibilities. Until the recent asynchronous BFT protocol, HoneyBadgerBFT (HBBFT), was proposed, the field received renewed attention. Dumbo family, a series of our works on the asynchronous BFT protocols, significantly pushed those protocols towards practice. First, all complexity metrics are pushed down to asymptotically optimal, simultaneously. Second, we identify the bottleneck in the state of the art and revisit the design methodology, identifying and utilizing the right components, and optimizing the protocol structure in various ways. Last but not least, we also open the box and optimize the critical components themselves. The resulting protocols are indeed significantly more performant, the latest protocol can have 100K tps and a few seconds of latency at a reasonable scale. This thesis focuses on the latest three members of the Dumbo family. To begin, we solved an open problem by proposing an optimal Multi-valued validated asynchronous Byzantine agreement protocol. Next, we present Dumbo-NG to address the challenge of latency-throughput tension by redesigning the methodology of asynchronous BFT protocols. Another benefit of the new methodology is that it can conquer the censorship threat without extra cost. Furthermore, we consider a realistic environment and present Bolt-Dumbo Transformer (BDT), a generic framework for practical optimistic asynchronous BFT to achieve the "best of both worlds" in terms of the advantages of deterministic BFT and randomized (asynchronous) BFT