IT infrastructure & microservices authentication

Mestrado IPB-ESTGBIOma - Integrated solutions in BIOeconomy for the Mobilization of the Agrifood chain project is structured in 6 PPS (Products, Processes, and Services) out of which, a part of PPS2 is covered in this work. This work resulted in the second deliverable of PPS2 which is defined as PPS2.A1.E2 - IT infrastructure design and graphical interface conceptual design. BIOma project is in the early stage and this deliverable is a design task of the project. For defining the system architecture, requirements, UML diagrams, physical architecture, and logical architecture have been proposed. The system architecture is based on microservices due to its advantages like scalability and maintainability for bigger projects like BIOma where several sensors are used for big data analysis. Special attention has been devoted to the research and study for the authentication and authorization of users and devices in a microservices architecture. The proposed authentication solution is a result of research made for microservices authentication where it was concluded that using a separate microservice for user authentication is the best solution. FIWARE is an open-source initiative defining a universal set of standards for context data management that facilitates the development of Smart solutions for different domains like Smart Cities, Smart Industry, Smart Agrifood, and Smart Energy. FIWARE’s PEP (Policy Enforcement Point) proxy solution has been proposed in this work for the better management of user’s identities, and client-side certificates have been proposed for authentication of IoT (Internet of Things) devices. The communication between microservices is done through AMQP (Advanced Message Queuing Protocol), and between IoT devices and microservices is done through MQTT (Message Queuing Telemetry Transport) protocol

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study

A cloud, microservice-based digital twin for the Oil industry a flow assurance case study

Digital Twins are one of the top recent technology trends, considered a front-runner in enabling the next generation of intelligent, digitalized industries. A Digital Twin is basi cally a real enterprise asset mirrored into the virtual world, created entirely in software, and utilized to innovate business, generate new revenue, and create value-producing op portunities. Naturally, building such demanding systems is not an easy task. Many organi zational and technological concerns should be addressed, like real-time processing, data management, cybersecurity, and low latency communication. Moreover, Digital Twins should be extensible and support data integration with its physical counterpart, allowing enterprises to streamline their operations more safely. Considering there are no standard methodologies or technologies to design a Digital Twin, software developers struggle to develop a robust, reliable Digital Twin architecture that meets customers’ needs. Therefore, in consideration of these technical challenges, we decided to explore solutions in the oil and gas industry. Oil plants are known for having notoriously complex pro cesses and an oppressive ecosystem when it comes to the adoption of new technologies, especially due to its intricate and ever-changing landscape. Despite these difficulties, this industry would certainly benefit from a well-designed Digital Twin. Hence, this work proposes a solution for an Oil Well Digital Twin architecture. We decided to use the flow assurance process of an Oil Well as a base to build a Digital Twin, aiming to study and fulfill those requirements and technical limitations. We implemented and tested several APIs, each one representing the virtual version of a real Oil Well component, enforcing the importance of microservices and cloud-native patterns in its design. When function ing together, these microservices comprise an entire Oil Well Digital twin solution, cov ering basic storage, communication, and monitoring fundamentals. This proof of concept demonstrates how a complex asset — like an Oil Well — can be built and organized in a completely digital manner. Lastly, this design is extensible and opens up further develop ment opportunities.Gêmeos Digitais são uma das principais tendências tecnológicas recentes, considerados pioneiros na habilitação da próxima geração de indústrias inteligentes e digitalizadas. Um Gêmeo Digital é basicamente um ativo corporativo espelhado no mundo virtual, criado inteiramente em software e destinado a inovar os modelos operacionais e de negócios, ao mesmo tempo em que fornece novas oportunidades de geração de receita e valor. Naturalmente, construir sistemas tão exigentes não é uma tarefa fácil. Muitas preocupações organizacionais e tecnológicas devem ser abordadas, como processamento em tempo real, gerenciamento de dados, segurança cibernética e comunicação de baixa latência. Além disso, os Gêmeos Digitais também devem ser extensíveis e oferecer suporte à integração de dados com sua contraparte física, permitindo que as empresas agilizem suas opera ções com mais segurança. Considerando que não existem metodologias ou tecnologias padrão para projetar um Gêmeo Digital, os desenvolvedores de software tem dificuldade em desenvolver uma arquitetura de Gêmeo Digital robusta e confiável que atenda às ne cessidades dos clientes. Além desses desafios técnicos, a indústria de óleo e gás é conhecida por ter processos notoriamente complexos e um ecossistema opressor na adoção de novas tecnologias, prin cipalmente devido ao seu cenário intrincado e em constante mudança. Apesar dessas dificuldades, essa indústria certamente se beneficiaria de um Gêmeo Digital bem proje tado. Assim, este trabalho propõe uma solução de uma arquitetura para um Gêmeo Digital de um Poço de Petróleo. Decidimos usar o processo de garantia de vazão de um poço de petróleo como base para construir um Gêmeo Digital, visando estudar e atender a esses requisitos e limitações técnicas. Implementamos e testamos várias APIs, cada uma repre sentando a versão virtual de um componente real de um poço de petróleo, reforçando a importância de microsserviços e padrões nativos de nuvem em seu design. Ao funciona rem juntos, esses microsserviços compreendem uma solução completa de gêmeos digitais de poços de petróleo, abrangendo os fundamentos básicos de armazenamento, comunica ção e monitoramento. Esta prova de conceito demonstra como um ativo complexo – como um poço de petróleo – pode ser construído e organizado de forma totalmente digital. Por fim, esse design é extensível e abre mais oportunidades de desenvolvimento

Microservice-based Reference Architecture for Semantics-aware Measurement Systems

Cloud technologies have become more important than ever with the rising need for scalable and distributed software systems. A pattern that is used in many such systems is a microservice-based architecture (MSA). MSAs have become a blueprint for many large companies and big software systems. In many scientific fields like energy and environmental informatics, efficient and scalable software systems with a primary focus on measurement data are a core requirement. Nowadays, there are many ways to solve research questions using data-driven approaches. Most of them have a need for large amounts of measurement data and according metadata. However, many measurement systems still follow deprecated guidelines such as monolithic architectures, classic relational database principles and are missing semantic awareness and interpretation of data. These problems and the resulting requirements are tackled by the introduction of a reference architecture with a focus on measurement systems that utilizes the principles of microservices. The thesis first presents the systematic design of the reference architecture by using the principles of Domain-driven Design (DDD). This process ensures that the reference architecture is defined in a modular and sustainable way in contrast to complex monolithic software systems. An extensive scientific analysis leads to the core parts of the concept consisting of the data management and semantics for measurement systems. Different data services define a concept for managing measurement data, according meta data and master data describing the business objects of the application implemented by using the reference architecture. Further concepts allow the reference architecture to define a way for the system to understand and interpret the data using semantic information. Lastly, the introduction of a frontend framework for dashboard applications represents an example for visualizing the data managed by the microservices

BHiveSense: An integrated information system architecture for sustainable remote monitoring and management of apiaries based on IoT and microservices

Precision Beekeeping, a field of Precision Agriculture, is an apiary management strategy based on monitoring honeybee colonies to promote more sustainable resource usage and maximise productivity. The approach related to Precision Beekeeping is based on methodologies to mitigate the stress associated with human intervention in the colonies and the waste of resources. These goals are achieved by supporting the intervention and managing the beekeeper’s timely and appropriate action at the colony’s level. In recent years, the growth of IoT (Internetof-Things) in Precision Agriculture has spurred several proposals to contribute to the paradigm of Precision Beekeeping, built on different technical concepts and with different production costs. This work proposes and describes an information systems architecture concept named BHiveSense, based on IoT and microservices, and different artefacts to demonstrate its concept: (1) a low-cost COTS (Commercial Off-The-Shelf) hive sensing prototype, (2) a REST backend API, (3) a Web application, and (4) a Mobile application. This project delivers a solution for a more integrated and sustainable beekeeping activity. Our approach stresses that by adopting microservices and a REST architecture, it is possible to deal with long-standing problems concerning interoperability, scalability, agility, and maintenance issues, delivering an efficient beehive monitoring system.info:eu-repo/semantics/publishedVersio

An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes

Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform

Design and Implementation of a Scalable Crowdsensing Platform for Geospatial Data

In the recent years smart devices and small low-powered sensors are becoming ubiquitous and nowadays everything is connected altogether, which is a promising foundation for crowdsensing of data related to various environmental and societal phenomena. Very often, such data is especially meaningful when related to time and location, which is possible by already equipped GPS capabilities of modern smart devices. However, in order to gain knowledge from high-volume crowd-sensed data, it has to be collected and stored in a central platform, where it can be processed and transformed for various use cases. Conventional approaches built around classical relational databases and monolithic backends, that load and process the geospatial data on a per-request basis are not suitable for supporting the data requests of a large crowd willing to visualize phenomena. The possibly millions of data points introduce challenges for calculation, data-transfer and visualization on smartphones with limited graphics performance. We have created an architectural design, which combines a cloud-native approach with Big Data concepts used in the Internet of Things. The architectural design can be used as a generic foundation to implement a scalable backend for a platform, that covers aspects important for crowdsensing, such as social- and incentive features, as well as a sophisticated stream processing concept to calculate incoming measurement data and store pre-aggregated results. The calculation is based on a global grid system to index geospatial data for efficient aggregation and building a hierarchical geospatial relationship of averaged values, that can be directly used to rapidly and efficiently provide data on requests for visualization. We introduce the Noisemap project as an exemplary use case of such a platform and elaborate on certain requirements and challenges also related to frontend implementations. The goal of the project is to collect crowd-sensed noise measurements via smartphones and provide users information and a visualization of noise levels in their environment, which requires storing and processing in a central platform. A prototypic implementation for the measurement context of the Noisemap project is showing that the architectural design is indeed feasible to realize

Explora : interactive querying of multidimensional data in the context of smart cities

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving-on ingestion time-synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach

A Service-Oriented Approach to Crowdsensing for Accessible Smart Mobility Scenarios

This work presents an architecture to help designing and deploying smart mobility applications. The proposed solution builds on the experience already matured by the authors in different fields: crowdsourcing and sensing done by users to gather data related to urban barriers and facilities, computation of personalized paths for users with special needs, and integration of open data provided by bus companies to identify the actual accessibility features and estimate the real arrival time of vehicles at stops. In terms of functionality, the first "monolithic" prototype fulfilled the goal of composing the aforementioned pieces of information to support citizens with reduced mobility (users with disabilities and/or elderly people) in their urban movements. In this paper, we describe a service-oriented architecture that exploits the microservices orchestration paradigm to enable the creation of new services and to make the management of the various data sources easier and more effective. The proposed platform exposes standardized interfaces to access data, implements common services to manage metadata associated with them, such as trustworthiness and provenance, and provides an orchestration language to create complex services, naturally mapping their internal workflow to code. The manuscript demonstrates the effectiveness of the approach by means of some case studies