A reference architecture for federating IoT infrastructures supporting semantic interoperability

: The Internet-of-Things (IoT) is unanimously identified as one of the main pillars of future smart scenarios. However, despite the growing number of IoT deployments, the majority of IoT applications tend to be self-contained, thereby forming vertical silos. Indeed, the ability to combine and synthesize data streams and services from diverse IoT platforms and testbeds, holds the promise to increase the potential of smart applications in terms of size, scope and targeted business context. This paper describes the system architecture for the FIESTA-IoT platform, whose main aim is to federate a large number of testbeds across the planet, in order to offer experimenters the unique experience of dealing with a large number of semantically interoperable data sources. This system architecture was developed by following the Architectural Reference Model (ARM) methodology promoted by the IoT-A project (FP7 “light house” project on Architecture for the Internet of Things). Through this process, the FIESTAIoT architecture is composed of a set of Views that deals with a “logical” functional decomposition (Functional View, FV) and data structuring and annotation, data flows and inter-functional component interactions (Information View, IV)

Cross-domain interoperability using federated interoperable semantic IoT/Cloud testbeds and applications: The FIESTA-IoT approach

This work is funded by the European Commission under the EU-H2020 Project Grant ”Federated Interoperable Semantic IoT/cloudTestbeds andApplications (FIESTA)” with the Grant Agreement No. CNECT-ICT-643943

Specification, verification et raffinement de reseaux de processus communicants

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 77709 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Predictive Analytics for Complex IoT Data Streams

The requirements of analyzing heterogeneous data streams and detecting complex patterns in near real-time have raised the prospect of Complex Event Processing (CEP) for many internet of things (IoT) applications. Although CEP provides a scalable and distributed solution for analyzing complex data streams on the fly, it is designed for reactive applications as CEP acts on near real-time data and does not exploit historical data. In this regard, we propose a proactive architecture which exploits historical data using machine learning (ML) for prediction in conjunction with CEP. We propose an adaptive prediction algorithm called Adaptive Moving Window Regression (AMWR) for dynamic IoT data and evaluated it using a real-world use case with an accuracy of over 96%. It can perform accurate predictions in near real-time due to reduced complexity and can work along CEP in our architecture. We implemented our proposed architecture using open source components which are optimized for big data applications and validated it on a use-case from Intelligent Transportation Systems (ITS). Our proposed architecture is reliable and can be used across different fields in order to predict complex events

FIESTA-IoT meta-cloud architecture: D2.4

This deliverable describes the System Architecture for the FIESTA-IoT platform aiming at federating a large number of test-bed across the planet in order to offer experimenters with a unique experience of dealing and experimenting with a large number of semantically interoperable data sources. The architecting process leading to this document followed the Architectural reference Model methodology promoted by the IoT-A project (FP7 “light house” project on Architecture for the Internet of Things). It therefore consists of a set of Views that are in tern dealing with “logical” functional decomposition (Functional View - FV), data structuring and annotation, data flows and inter-functional Component interactions (Information View - IV) and ultimately the deployment of those logical components onto concrete software components (Deployment View). Design Choices pertaining to Non-Functional requirements will be covered in the up-coming WP deliverables providing detailed interfaces description that will guide he implemented work on each WP. The architecture describe din this document is inclusive in the sense it can accommodate under its federation a large number of test-beds with various capabilities (some being semantic-enabled already, some not). It offers full semantic interoperability: all assets of the test-bed (resources, IoT Services, Virtual Entities) are semantically annotated and described; they are searchable using either powerful data query languages or simpler APIs. FIESTA-IoT is therefore able to offer the greatest test-bed agnostic experience to both expert users (semantically skilled) and more basic experimenters as well

Dynamic Scheduler Management Using Deep Learning

The ability to manage the distributed functionality of large multi-vendor networks will be an important step towards ultra-dense 5G networks. Managing distributed scheduling functionality is particularly important, due to its influence over inter-cell interference and the lack of standardization for schedulers. In this paper, we formulate a method of managing distributed scheduling methods across a small cluster of cells by dynamically selecting schedulers to be implemented at each cell. We use deep reinforcement learning methods to identify suitable joint scheduling policies, based on the current state of the network observed from data already available in the RAN. Additionally, we also explore three methods of training the deep reinforcement learning based dynamic scheduler selection system. We compare the performance of these training methods in a simulated environment against each other, as well as homogeneous scheduler deployment scenarios, where each cell in the network uses the same type of scheduler. We show that, by using deep reinforcement learning, the dynamic scheduler selection system is able to identify scheduler distributions that increase the number of users that achieve their quality of service requirements in up to 77% of the simulated scenarios when compared to homogeneous scheduler deployment scenarios

Dynamic Scheduler Management Using Deep Learning

The ability to manage the distributed functionality of large multi-vendor networks will be an important step towards ultra-dense 5G networks. Managing distributed scheduling functionality is particularly important, due to its influence over inter-cell interference and the lack of standardization for schedulers. In this paper, we formulate a method of managing distributed scheduling methods across a small cluster of cells by dynamically selecting schedulers to be implemented at each cell. We use deep reinforcement learning methods to identify suitable joint scheduling policies, based on the current state of the network observed from data already available in the RAN. Additionally, we also explore three methods of training the deep reinforcement learning based dynamic scheduler selection system. We compare the performance of these training methods in a simulated environment against each other, as well as homogeneous scheduler deployment scenarios, where each cell in the network uses the same type of scheduler. We show that, by using deep reinforcement learning, the dynamic scheduler selection system is able to identify scheduler distributions that increase the number of users that achieve their quality of service requirements in up to 77% of the simulated scenarios when compared to homogeneous scheduler deployment scenarios

Numerical study of the mechanical properties of Mg2SiO4 glasses

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