380 research outputs found

    Distributing intelligence among cloud, fog and edge in industrial cyber-physical systems

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    The 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive.info:eu-repo/semantics/publishedVersio

    A microservice architecture for predictive analytics in manufacturing

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    Abstract This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the platform to be deployed in a multi-tenant environment, while facilitating resource isolation and independency from specific technologies or services. Moreover, the proposed platform supports scalable data storage supporting an effective and efficient management of large volume of Industry 4.0 data. Methodologies of data-driven predictive maintenance are provided to the user as-a-service, facilitating offline training and online execution of pre-trained analytics models, while the connection of the raw data to contextual information support their understanding and interpretation, while guaranteeing interoperability across heterogeneous systems. A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform

    Developing IoT applications in the Fog:a distributed dataflow approach

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    In this paper we examine the development of IoT applications from the perspective of the Fog Computing paradigm, where computing infrastructure at the network edge in devices and gateways is leverage for efficiency and timeliness. Due to the intrinsic nature of the IoT: heterogeneous devices/resources, a tightly coupled perception-action cycle and widely distributed devices and processing, application development in the Fog can be challenging. To address these challenges, we propose a Distributed Dataflow (DDF) programming model for the IoT that utilises computing infrastructures across the Fog and the Cloud. We evaluate our proposal by implementing a DDF framework based on Node-RED (Distributed Node-RED or D-NR), a visual programming tool that uses a flow-based model for building IoT applications. Via demonstrations, we show that our approach eases the development process and can be used to build a variety of IoT applications that work efficiently in the Fog

    Quantifying the latency benefits of near-edge and in-network FPGA acceleration

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    Transmitting data to cloud datacenters in distributed IoT applications introduces significant communication latency, but is often the only feasible solution when source nodes are computationally limited. To address latency concerns, cloudlets, in-network computing, and more capable edge nodes are all being explored as a way of moving processing capability towards the edge of the network. Hardware acceleration using Field Programmable Gate Arrays (FPGAs) is also seeing increased interest due to reduced computation latency and improved efficiency. This paper evaluates the the implications of these offloading approaches using a case study neural network based image classification application, quantifying both the computation and communication latency resulting from different platform choices. We consider communication latency including the ingestion of packets for processing on the target platform, showing that this varies significantly with the choice of platform. We demonstrate that emerging in-network accelerator approaches offer much improved and predictable performance as well as better scaling to support multiple data sources

    Towards Trusted Seamless Reconfiguration of IoT Nodes

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    IoT networks are growing rapidly with the addition of new sensors, nodes and devices to existing IoT networks. Due to the ever-increasing demand for IoT nodes to adapt to changing environment conditions and application requirements, the need for reconfiguring these already existing IoT nodes is increasing rapidly. A reconfiguration of an IoT network includes alterations to the devices connected, changing the behavioural patterns of the devices and modifying the software modules that control the IoT network and devices. Reconfiguring an already existing IoT network is a challenge due to the amount of data loss and network downtime faced when carrying out a reconfiguration procedure in a limited power supply environment. This paper proposes an architecture for trusted dynamic reconfiguration of IoT nodes with the least amount of data loss and downtime. The proposed approach uses multiple IoT nodes to facilitate dynamic reconfiguration
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