Microservices suite for smart city applications

Smart Cities are approaching the Internet of Things (IoT) World. Most of the first-generation Smart City solutions are based on Extract Transform Load (ETL); processes and languages that mainly support pull protocols for data gathering. IoT solutions are moving forward to event-driven processes using push protocols. Thus, the concept of IoT applications has turned out to be widespread; but it was initially “implemented” with ETL; rule-based solutions; and finally; with true data flows. In this paper, these aspects are reviewed, highlighting the requirements for smart city IoT applications and in particular, the ones that implement a set of specific MicroServices for IoT Applications in Smart City contexts. Moreover; our experience has allowed us to implement a suite of MicroServices for Node-RED; which has allowed for the creation of a wide range of new IoT applications for smart cities that includes dashboards, IoT Devices, data analytics, discovery, etc., as well as a corresponding Life Cycle. The proposed solution has been validated against a large number of IoT applications, as it can be verified by accessing the https://www.Snap4City.org portal; while only three of them have been described in the paper. In addition, the reported solution assessment has been carried out by a number of smart city experts. The work has been developed in the framework of the Select4Cities PCP (PreCommercial Procurement), funded by the European Commission as Snap4City platform

Empowering citizens towards the co-creation of sustainable cities

Urban ecosystems are becoming one of the most potentially attractive scenarios for innovating new services and technologies. In parallel, city managers, urban utilities and other stakeholders are fostering the intensive use of advanced technologies aiming at improving present city performance and sustainability. The deployment of such technology entails the generation of massive amounts of information which in many cases might become useful for other services and applications. Hence, aiming at taking advantage of such massive amounts of information and deployed technology as well as breaking down the potential digital barrier, some easy-to-use tools have to be made available to the urban stakeholders. These tools integrated in a platform, operated directly or indirectly by the city, provide a singular opportunity for exploiting the concept of connected city whilst promoting innovation in all city dimensions and making the co-creation concept a reality, with an eventual impact on government policies.This work would not have been possible without the contribution of the OrganiCity team, which has been partially funded by the European Union, under the grant agreement No. 645198 of the Horizon 2020 research and innovation program

Consuming data sources to generate actionable items

Plataforma que consumeixi sensors IoT i sistemes d'alertes per a generar accions de resposta relacionades amb els sistemes d'alerta. Per a demostrar els casos d'ús possibles s'incorporaran funcions requerides per Projectes Europeus, solucions comercials i solucions compatibles amb estàndards

When IoT Meets DevOps: Fostering Business Opportunities

The Internet of Things (IoT) is the new digital revolution for the near-future society, the second after the creation of the Internet itself. The software industry is converging towards the large-scale deployment of IoT devices and services, and there’s broad support from the business environment for this engineering vision. The Development and Operations (DevOps) project management methodology, with continuous delivery and integration, is the preferred approach for achieving and deploying applications to all levels of the IoT architecture. In this paper we also discuss the promising trend of associating devices with microservices, which are further encapsulated into functional packages called containers. Docker is considered the market leader in container-based service delivery, though other important software companies are promoting this concept as part of the technology solution for their IoT customers. In the experimental section we propose a three-layer IoT model, business-oriented, and distributed over multiple cloud environments, comprising the Physical, Fog/Edge, and Application layers.     Keywords: Internet-of-Things, software technologies, project management, business environment Heading

Orchestration of machine learning workflows on Internet of Things data

Applications empowered by machine learning (ML) and the Internet of Things (IoT) are changing the way people live and impacting a broad range of industries. However, creating and automating ML workflows at scale using real-world IoT data often leads to complex systems integration and production issues. Examples of challenges faced during the development of these ML applications include glue code, hidden dependencies, and data pipeline jungles. This research proposes the Machine Learning Framework for IoT data (ML4IoT), which is designed to orchestrate ML workflows to perform training and enable inference by ML models on IoT data. In the proposed framework, containerized microservices are used to automate the execution of tasks specified in ML workflows, which are defined through REST APIs. To address the problem of integrating big data tools and machine learning into a unified platform, the proposed framework enables the definition and execution of end-to-end ML workflows on large volumes of IoT data. In addition, to address the challenges of running multiple ML workflows in parallel, the ML4IoT has been designed to use container-based components that provide a convenient mechanism to enable the training and deployment of numerous ML models in parallel. Finally, to address the common production issues faced during the development of ML applications, the proposed framework used microservices architecture to bring flexibility, reusability, and extensibility to the framework. Through the experiments, we demonstrated the feasibility of the (ML4IoT), which managed to train and deploy predictive ML models in two types of IoT data. The obtained results suggested that the proposed framework can manage real-world IoT data, by providing elasticity to execute 32 ML workflows in parallel, which were used to train 128 ML models simultaneously. Also, results demonstrated that in the ML4IoT, the performance of rendering online predictions is not affected when 64 ML models are deployed concurrently to infer new information using online IoT data