Device failure detection is one of most essential problems in industrial internet of things (IIoT). However, in conventional IIoT device failure detection, client devices need to upload raw data to the central server for model training, which might lead to disclosure of sensitive business data. Therefore, in this paper, to ensure client data privacy, we propose a blockchain-based federated learning approach for device failure detection in IIoT. First, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT, which enables verifiable integrity of client data. In the architecture, each client periodically creates a Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain. Further, to address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid distance weighted federated averaging (CDW_FedAvg) algorithm taking into account the distance between positive class and negative class of each client dataset. In addition, to motivate clients to participate in federated learning, a smart contact based incentive mechanism is designed depending on the size and the centroid distance of client data used in local model training. A prototype of the proposed architecture is implemented with our industry partner, and evaluated in terms of feasibility, accuracy and performance. The results show that the approach is feasible, and has satisfactory accuracy and performance.

It is a challenge to ensure data integrity for cloud-based Internet of Things (IoT) applications because of the inherently dynamic nature of IoT data. The available frameworks of data integrity verification with public auditability cannot avoid the Third Party Auditors (TPAs). However, in a dynamic environment, such as the IoT, the reliability of the TPA-based frameworks is far from being satisfactory. In this paper, we propose a blockchain-based framework for Data Integrity Service. Under such framework, a more reliable data integrity verification can be provided for both the Data Owners and the Data Consumers, without relying on any Third Party Auditor (TPA). In this paper, the relevant protocols and a subsequent prototype system, which is implemented to evaluate the feasibility of our proposals, are presented. The performance evaluation of the implemented prototype system is conducted, and the test results are discussed. The work lays a foundation for our future work on dynamic data integrity verification in a fully decentralized environment.

In recent years, High Performance Computing (HPC) systems have been shifting from expensive massively parallel architectures to clusters of commodity PCs to take advantage of cost and performance benefits. Fault tolerance in such systems is a growing concern for long-running applications. In this paper, we briefly review the failure rates of HPC systems and also survey the fault tolerance approaches for HPC systems and issues with these approaches. Rollback-recovery techniques which are most often used for long-running applications on HPC clusters are discussed because they are widely used for long-running applications on HPC systems. Specifically, the feature requirements of rollback-recovery are discussed and a taxonomy is developed for over twenty popular checkpoint/restart solutions. The intent of this paper is to aid researchers in the domain as well as to facilitate development of new checkpointing solutions.

The next-generation blockchain ecosystem is expected to integrate both homogeneous and heterogeneous distributed ledgers. These systems require operations across multiple blockchains to enrich advanced functionalities for future applications. However, the development of blockchain interoperability involves much more complexity regarding the variety of underlying architectures. Guaranteeing the properties of ACID (Atomicity, Consistency, Isolation, Durability) across diverse blockchain systems remains challenging. To clear the fog, this article accordingly provides a comprehensive review of the current progress of blockchain interoperability. We explore the general principles and procedures for interoperable blockchain systems to highlight their design commons. Then, we survey practical instances and compare state-of-the-art systems to present their unique features between distinct solutions. Finally, we discuss critical challenges and point out potential research directions. We believe our work can provide an intuitive guideline for newcomers and also promote rapid development in terms of blockchain interoperability.

Limitations on high latency and low scalability of classical blockchain systems retard their adoptions and applications. Reconstructed blockchain systems have been proposed to avoid the consumption of competitive transactions caused by linear sequenced blocks. These systems, instead, structure transactions/blocks in the form of Directed Acyclic Graph (DAG) and consequently rebuild upper layer components. The promise of DAG-based blockchain systems is to enable fast confirmation (complete transactions within million seconds) and high scalability (attach transactions in parallel) without significantly compromising security. However, this field still lacks systematic work that summarises DAG techniques. To bridge the gap, this Systematization of Knowledge (SoK) provides a comprehensive analysis of ever-existing and ongoing DAG-based blockchain systems. We abstract a general model to capture the main features and identify six types of design patterns. Then, we evaluate these systems from the perspectives of structure, consensus, property, security, and performance. We further discuss the trade-off between different factors, open challenges, and the potentiality of DAG-based solutions, indicating their promising directions for future research.