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Cyber-physical systems, encompass the real physical space and virtual cyber space for providing advanced service for humans, which together, are also namely Internet of Things. The complex and large scale relationships among nodes contain the potential information of user, which is required and essential for providing high quality personalized service. Graph is used to represent, make fusion and process the relationships data, which has been used in many domains with traditional small data sets. K -hop reachability query answering in graphs is seeing a growing number of applications, such as ranked keyword search in databases, social networking, ontology reasoning and bioinformatics. Currently, techniques for efficient evaluation of such queries are based on vertex-cover technology. These methods suffer from a lack of scalability; that is, they cannot be applied to very large graphs. To solve these problems, we propose the compound-index method, namely BFSI-B, which uses the special index to decrease the k -hop reachability query time and improve pruning efficiency. The theoretical analysis and experiment results demonstrate that our method has a smaller index size and a faster query time than the k -reach method, in both dense graphs and very large graphs. [ABSTRACT FROM AUTHOR]

Cyber-physical-social systems (CPSSs) represent an emerging paradigm encompassing the cyber world, physical world and social world. One of the main purposes of CPSSs is to provide high-quality, proactive, and personalized services for humans. For CPSSs to realize this purpose, a novel services framework is needed. In this article, we present a tensor-based cloud-edge computing framework that mainly includes the cloud and edge planes. The cloud plane is used to process large-scale, long-term, global data, which can be used to obtain decision making information such as the feature, law, or rule sets. The edge plane is used to process small-scale, short-term, local data, which is used to present the real-time situation. Also, personalized services will be directly provided for humans by the edge plane according to the obtained feature, law, or rule sets and the local high-quality data obtained in the edge plane. Then a tensor-based services model is proposed to match the requirement of users in the local CPSS. Finally, a case study about CPSS services is proposed to demonstrate the application features of the proposed framework. [ABSTRACT FROM PUBLISHER]

The integration of blockchain technology with Cyber-Physical Systems (CPS) has gained significant attention across various industry domains such as manufacturing, healthcare, transportation, and energy management. The consensus mechanism serves as the fundamental component of decentralized blockchain systems, and the efficiency of the consensus algorithm greatly impacts the practicality of the entire system. One of the most well-known representative algorithms in the field of Byzantine Fault Tolerance (BFT) consensus is PBFT, which operates through successive views where a replica is designated as the leader in each view. The leader assumes the responsibility of proposing new requests and gathering votes from other replicas. However, in cases where the leader becomes Byzantine or faulty, a view-change process is triggered after a Δ timer expires, transitioning to the next view. Requests that failed to commit in a previous view may experience a delay of up to Δ + 6 δ (δ representing the actual network delay) before being committed in the new view. Given that Δ is typically set to a large value, this latency becomes unacceptable for CPS, which necessitates real-time transaction processing or data exchange. Following PBFT, several works, including SBFT, have aimed to improve consensus performance. However, all these works primarily focus on reducing latency in the normal case and overlook the aforementioned issue that existed during the view-change process. To address this issue, we propose Doppel, an approach that reduces the latency caused by a faulty leader. Doppel achieves this by introducing two leaders, namely L 1 and L 2 , in each view and enabling request commitment during the view-change process. These leaders are assigned different priorities. If L 1 is non-faulty, Doppel can commit requests within 5 δ , which is equivalent to PBFT and SBFT. Conversely, if L 1 is faulty but L 2 is non-faulty, Doppel significantly reduces latency to 7 δ , which is notably smaller than Δ + 6 δ in PBFT and SBFT. We have implemented a system prototype of and conducted multiple experiments to evaluate its performance. The experimental results consistently demonstrate Doppel's outperformance over PBFT and SBFT. [ABSTRACT FROM AUTHOR]

The digital deep integration of cyber space, physical space and social space facilitates the formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be capable of solving complex tasks and providing intelligent services. Knowledge representation, fusion, and reasoning are complementary in the knowledge graph and worthy of continuous exploration and in-depth research. Since the concept of knowledge representation learning has been proposed, various models have been developed and demonstrated their superior ability in knowledge fusion and reasoning. In this paper, we investigate the progress and function of representation learning models adopted in knowledge fusion and reasoning, and extract their commonalities and characteristics, which is a perspective that has never been developed before, to provide new ideas for scholars and realize the mutual promotion of knowledge fusion and reasoning. This paper comprehensively reviews classic methods and investigates advanced and emerging works, which are published in authoritative journals or conferences. Furthermore, to overcome the limitations of existing methods, we propose an integrated knowledge representation learning and application framework for CPSS and a series of tensor-based knowledge fusion and reasoning models. Finally, four future directions are discussed in this paper, we analyze the challenges faced by them and suggest promising solutions. • Extensive coverage of emerging advances about knowledge representation learning. • Targeted investigations and new perspectives about knowledge fusion and reasoning. • Tensor-enabled representation learning framework and promising models for CPSS. • Insightful prospects on representation learning-based knowledge reasoning and fusion. [ABSTRACT FROM AUTHOR]