380 research outputs found
Recommended from our members
Platform as a service gateway for the Fog of Things
Internet of Things (IoT), one of the key research topics in recent years, together with concepts from Fog Computing, brings rapid advancements in Smart City, Monitoring Systems, industrial control, transportation and other fields. These applications require a reconfigurable sensor architecture that can span multiple scenarios, devices and use cases that allow storage, networking and computational resources to be efficiently used on the edge of the network. There are a number of platforms and gateway architectures that have been proposed to manage these components and enable application deployment. These approaches lack horizontal integration between multiple providers as well as higher order functionalities like load balancing and clustering. This is partly due to the strongly coupled nature of the deployed applications, a lack of abstraction of device communication layers as well as a lock-in for communication protocols. This is a major obstacle for the development of a protocol agnostic application environment that allows for single application to be migrated and to work with multiple peripheral devices with varying protocols from different local gateways. This research looks at existing platforms and their shortcomings as well as proposes a messaging based modular gateway platform that enables clustering of gateways and the abstraction of peripheral communication protocols. This allows applications to send and receive messages regardless of their location and destination device protocol, creating a more uniform development environment. Furthermore, it results in a more streamlined application development and testing while providing more efficient use of the gateways resources. Our evaluation of a prototype for the system shows the need for the migration of resources and the QoS advantages of such a system. The presented use-case scenarios show that clustering can prove to be an advantage in certain use-cases as well as the deployment of a larger testing and control environment through the platform
Distributing intelligence among cloud, fog and edge in industrial cyber-physical systems
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
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
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
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
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
- …