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Industrial Internet of Things (IIoT) nowadays represents a fundamental technology for the Industry 4.0 initiatives for connecting devices, operators and processes so as to eventually fuel digital transformation. However, if based exclusively on Cloud Computing, the IIoT faces inevitable limitations and barriers. Therefore, in this paper we deal with the Edge Intelligence (EI), a novel paradigm at the confluence of Edge Computing and artificial Intelligence, and, particularly, we analyze its impact on the processing of the IIoT data in the proximity of the events of interest rather than on Cloud servers. Therefore, a general discussion on the most relevant benefits, limitations and open challenges of the IIoT-EI duo is proposed hereinafter and exemplified through an emblematic IIoT use case: this is related to safety-critical tasks based on video analytics and provides a full-fledged comparison between Cloud-and EI-based IIoT deployments (in terms of reliability, responsiveness, bandwidth usage, energy footprint and usability), finally outlining a general strategy for large-scale EI-aided IIoT systems engineering. [ABSTRACT FROM AUTHOR]

With the recent progress of science and the development of society, the development of artificial intelligence technology and robot theory have become increasingly mature. Compared to the development of Internet, robot industry is a branch of traditional industry, and the popularization of social robots has always been the bottleneck. Robot intelligence is an important part of artificial intelligence, which brings new challenges to human production and life for it usually needs cloud computing system for the systematic implementations. Compared to cloud computing, edge computing brings powerful computing resources and efficient services to the network edge, which has lower latency, lower bandwidth consumption, higher energy efficiency and better privacy protection. Data envelopment analysis (DEA) enriches theory of production function and its application technology in microeconomics. Meanwhile, it has advantages in avoiding subjective factors, simplifying algorithm and reducing errors. Based on these novel technologies, this paper analyzes development trend of intelligent social robot with specialzed analysis of the electric engineering and data analytic perspectives. The sample expeirment has shown rapid development. Compared with the other state-of-the-art methodologies, the proposed framework is efficient. [ABSTRACT FROM AUTHOR]

The amount of data generated by internet of things devices multiplied day after day, and the use of cloud computing to store and process the data is very challenging due to the congestion of network, which negatively affects the processing time. The thing that led to the emergence of edge computing to solve these problems besides with the use of recent technologies such as artificial intelligence, blockchain and many more. The objective of this paper is to present the comparative study of the several proposed architectures on smart farming based on IoT according to different features since protocols, technologies, security, interoperability, accessibility, resilience etc., the advantages and limitations of each solution. Index Terms [ABSTRACT FROM AUTHOR]

The traditional cloud computing paradigm faces challenges with the increasing number of Artificial Intelligence of Things (AIoT) devices generated at the network edge. Edge computing provides a novel approach to overcoming the limitations of cloud computing by delivering lower service latency and higher quality of service (QoS) to AIoT devices. However, edge computing encounters constraints due to scarce resources on edge servers and the long distance between AIoT devices and remote cloud servers. To ensure high QoS for AIoT devices, a balance is required between limited computing resources on the network edge and high latency caused by the geographic distance on the cloud side. In this paper, we propose an edge-cloud architecture that achieves optimal QoS for AIoT devices. We employ a federated learning-based architecture to train AIoT devices' data locally, thereby ensuring privacy. We evaluate the effectiveness and efficiency of our proposed approach by comparing it with the centralized approach from the state-of-the-art using two widely used datasets. The experimental results demonstrate that our architecture achieves higher effectiveness and efficiency in improving AIoT devices' QoS. Overall, our proposed edge-cloud architecture overcomes the limitations of traditional cloud computing, enhances user privacy, and delivers high QoS to AIoT devices. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence techniques have been adopted for many real-time applications. The Internet of Things (IoT) network comprises many sensing devices and physical objects for information gathering and further transmission. In addition to being sent to the receiving nodes, the collected data also needs to be received promptly. Also, many solutions have been proposed for IoT-based embedded systems using edge computing but they are not fully protected against unidentified communication threats. In such circumstances, such systems decrease the trust ratio, and communication performance is compromised. In this research, we describe an optimization model based on IoT-edged technology that incorporates cloud computational intelligence. Furthermore, edge nodes employ artificial intelligence algorithms to provide the optimal outcome for selecting trustworthy forwarded data and lengthen the connected time for smart devices. Firstly, the edge devices extract useful information from the IoT nodes, and accordingly, it provides a decision module based on optimization computing. Secondly, utilizing cryptographic approaches, edge technology secures the multi-layers of the IoT system and ensures data privacy with integrity. Finally, the proposed model is tested and verified for its performance than other related studies in terms of energy consumption, packet delivery ratio, and data delay. [ABSTRACT FROM AUTHOR]

With the advent of the Internet of Everything, the combination of AI (Artificial Intelligence) and edge computing has become a new research hotspot, and edge intelligence has emerged, which enables network edge devices to analyze data through AI algorithms. Since the edge computing environment is more complex and variable than cloud computing, there are many issues in building edge intelligence, such as lack of quantitative evaluation criteria, heterogeneous computing platforms, complex network topologies, and changing user requirements. To analyze the performance of edge intelligence workloads running on heterogeneous hardware platform, we target machine learning workloads in edge intelligence and analyze the impact of algorithm model complexity, edge data characteristics and heterogeneous platform differences in edge intelligence in terms of relative performance. By analyzing the machine learning workload in edge intelligence, we find that the inference time and memory usage of a model can be predicted based on the amount of computation and number of parameters of the model. Moreover, image complexity, edge data network features, and batch size all affect the performance of edge intelligence workloads. Furthermore, the upper limit of model performance on the same computing platform is limited by hardware resources. And finally, the model performance of a platform depends on its own computing power and bandwidth. [ABSTRACT FROM AUTHOR]

Thanks to the explosive evolution of 6G, Artificial intelligence, Blockchain, Cloud computing, Data, and Edge computing (6G+ABCDE), it is possible to make Intelligent Transportation Systems (ITS) more autonomous, intelligent, and efficient. Conventional ITS technologies have been applied and deployed worldwide, and autonomous vehicles have also made a qualitative leap. The revolution of ITS magnitude is on the horizon, and the singularity may happen in the 6G Autonomous Intelligent Transportation Systems (6G-AITS). We indicate that the human-centric transportation system will still be the key to 6G-AITS. The 6G-AITS is a three-dimensional intelligent system, which is driven by data and intelligence, and characterized by sharing and collaboration, thereby achieving the service-oriented demand response. To support this vision, we explore the 6G-AITS changes in terms of system architecture, communication, and resource allocation, as well as its key features. We elaborate on the mechanism and three applications of 6G-AITS. Furthermore, we explore the challenges of 6G-AITS and consider some issues that go beyond technologies that could affect the development of 6G-AITS. Key objective of this work is to provide an exploration of the imperative requirements to develop 6G-AITS and guidance for the research and development of 6G-AITS. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) tsunami, public embracement, and the ubiquitous adoption of smart devices are affecting virtually every industry, directly or indirectly. The success of the current and future landscape of IoT and connected devices requires service provision characterized by scalability, ubiquity, reliability, and high-performance, among others. In order to achieve this attribution, the integration of IoT and Cloud Computing (CC), known as cloud IoT, has emerged as a new paradigm providing advanced services specific to aggregating, storing, and processing data generated by IoT. While the convergence of IoT and Cloud brings opportunities, it suffers from specific limitations such as bandwidth, latency, and connectivity. The increasing need for supporting interaction between cloud and IoT led to Edge and Fog Computing (FC) in which computing and storage resources are located not only in the cloud but also at the edges near the source of data. The hierarchical and collaborative edge–fog–cloud architecture brings tremendous benefits as it enables us to distribute the intelligence and computation – including Artificial Intelligence (AI), Machine Learning (ML), and big data analytics – to achieve an optimal solution while satisfying the given constraints e.g., delay-energy tradeoff. Due to the hierarchical, cross-layer, and distributed nature of this model, achieving an osmotic and effective convergence of IoT, edge, fog, and cloud computing requires overcoming many challenges with respect to design and implementation, as well as deployment and evaluation. This paper provides a comprehensive insight into the edge-fog-cloud computing paradigm by providing a blend of discussions on all important aspects of the underlying technologies to offer opportunities for more holistic studies and to accelerate knowledge acquisition. To gain a deep understanding of edge–fog–cloud, we will begin this paper by providing an in-depth tutorial and presenting the main requirements, state-of-the-art reference architectures, building blocks, components, protocols, applications, and other similar computing paradigms, including their similarities and differences. Following this, a holistic reference architecture for edge–fog–cloud IoT is presented and the major corresponding design and deployment considerations (e.g., service models, infrastructure design, provisioning, resource allocation, offloading, service migration, performance evaluation, and security concerns) are discussed. Next, we will take a look at the role of privacy-preserving, distributed, and collaborative analytics as well as the interaction between edge, fog, and cloud computing. Finally, we will overview the main challenges in the field of edge–fog–cloud computing that need to be tackled to realize the full potential of IoT. • IoT architectures: SDN, Fog Computing, Multi-Access Edge Computing, Cloudlet, Transparent Computing, Extreme Edge, etc. • Edge–fog–cloud Interplay: benefits, challenges, and reference architecture. • Task offloading. • CNN-assisted ECG-based heart anomaly detection with RL-learning task offloading. [ABSTRACT FROM AUTHOR]

• Data is a business asset and this must be properly governed. • Data governance is critical for Industry 4.0. • Specification and description of data governance requirements for Industry 4.0. • Proposal of a reference framework for constructing data governance systems. • Data governance reference framework based on TOGAF, ISO-42010 and RAS standards. The fourth industrial revolution, or Industry 4.0, represents a new stage of evolution in the organization, management and control of the value chain throughout the product or service life cycle. This is mainly based on the digitalization of the industrial environment by means of the convergence of Information Technologies (IT) and operational Technologies (OT) through cyber-physical systems and the Industrial IoT (IIoT) and the use of data generated in real time for gaining insights and making decisions. Therefore data becomes a critical asset for Industry 4.0 and must be managed and governed like a strategic asset. We rely on Data Governance (DG) as a key instrument for carrying out this transformation. This paper presents the design of a specific governance framework for Industry 4.0. First, this contextualizes data governance for Industry 4.0 environments and identifies the requirements that this framework must address, which are conditioned by the specific features of Industry 4.0, among others, the intensive use of big data, the cloud and edge computing, the artificial intelligence and the current regulations. Next, we formally define a reference framework for the implementation of Data Governance Systems for Industry 4.0 using international standards and providing several examples of architecture building blocks. [ABSTRACT FROM AUTHOR]

The current dependency of Artificial Intelligence (AI) systems on Cloud computing implies higher transmission latency and bandwidth consumption. Moreover, it challenges the real-time monitoring of physical objects, e.g., the Internet of Things (IoT). Edge systems bring computing closer to end devices and support time-sensitive applications. However, Edge systems struggle with state-of-the-art Deep Neural Networks (DNN) due to computational resource limitations. This paper proposes a technology framework that combines the Edge-Cloud architecture concept with BranchyNet advantages to support fault-tolerant and low-latency AI predictions. The implementation and evaluation of this framework allow assessing the benefits of running Distributed DNN (DDNN) in the Cloud-to-Things continuum. Compared to a Cloud-only deployment, the results obtained show an improvement of 45.34% in the response time. Furthermore, this proposal presents an extension for Kafka-ML that reduces rigidness over the Cloud-to-Things continuum managing and deploying DDNN. [ABSTRACT FROM AUTHOR]