MicroServices Suite for Smart City Applications

Badii, Claudio; Bellini, Pierfrancesco; Difino, Angelo; Nesi, Paolo; Pantaleo, Gianni; Paolucci, Michela

doi:10.3390/s19214798

Cited by 37 publications

(30 citation statements)

References 40 publications

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“…All the data collected and indexed can be accessed via APIs and/or Microservices to enable developers and operators to create in a simple manner: IoT applications, mobile and Web app, and dashboards, according to the GDPR (Badii et al 2019). • Data analytics and data transformations processes and tools can be created and shared as the smart core of the smart services, such as: making predictions, signaling early warnings, detecting anomalies, creating analysis, producing interactive heat maps, suggesting decision makers, supporting simulations, etc.…”

Section: Snap4city Architecturementioning

confidence: 99%

Snap4City Platform to Speed Up Policies

Mitolo

Nesi

Pantaleo

et al. 2021

Smart and Sustainable Planning for Cities and Regions

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In the development of smart cities, there is a great emphasis on setting up so-called Smart City Control Rooms, SCCR. This paper presents Snap4City as a big data smart city platform to support the city decision makers by means of SCCR dashboards and tools reporting in real time the status of several of a city’s aspects. The solution has been adopted in European cities such as Antwerp, Florence, Lonato del Garda, Pisa, Santiago, etc., and it is capable of covering extended geographical areas around the cities themselves: Belgium, Finland, Tuscany, Sardinia, etc. In this paper, a major use case is analyzed describing the workflow followed, the methodologies adopted and the SCCR as the starting point to reproduce the same results in other smart cities, industries, research centers, etc. A Living Lab working modality is promoted and organized to enhance the collaboration among municipalities and public administration, stakeholders, research centers and the citizens themselves. The Snap4City platform has been realized respecting the European Data Protection Regulation (GDPR), and it is capable of processing every day a multitude of periodic and real-time data coming from different providers and data sources. It is therefore able to semantically aggregate the data, in compliance with the Km4City multi-ontology and manage data: (i) having different access policies; and (ii) coming from traditional sources such as Open Data Portals, Web services, APIs and IoT/IoE networks. The aggregated data are the starting point for the services offered not only to the citizens but also to the public administrations and public-security service managers, enabling them to view a set of city dashboards ad hoc composed on their needs, for example, enabling them to modify and monitor public transportation strategies, offering the public services actually needed by citizens and tourists, monitor the air quality and traffic status to establish, if impose or not, traffic restrictions, etc. All the data and the new knowledge produced by the data analytics of the Snap4City platform can also be accessed, observing the permissions on each kind of data, thanks to the presence of an APIs complex system.

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Section: Snap4city Architecturementioning

confidence: 99%

Snap4City Platform to Speed Up Policies

Mitolo

Nesi

Pantaleo

et al. 2021

Smart and Sustainable Planning for Cities and Regions

Self Cite

View full text Add to dashboard Cite

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“…The Data Ingestion and Aggregation can be performed by using: IoT Applications in Node-RED, while other tools as CKAN for open data ( ), and Web Scraping tools. The solution supports a large number of protocols [ 26 ] such as MQTT (Message Queuing Telemetry Transport), NGSI (Next Generation Service Interfaces), COAP (Constrained Application Protocol), OneM2M (Machine to Machine), ModBus (Modicon BUS), OPC (Open Communication), and AMQP (Advanced Message Queuing Protocol). Data Transformation tools for manipulating data are mainly developed by using data flow, data driven flows in Node-RED exploiting a suite of Snap4City MicroServices.…”

Section: Snap4city Framework and Developmentmentioning

confidence: 99%

“…The Data Storage is implemented by using NIFI for data integration, Elastic Search for indexing, and the Knowledge Base implemented as an RDF (Resource Description Framework) store which is an index for spatial, relational, and temporal aspects. All the data and services are accessible via the Advanced Smart City API which are used by Front End Tools such as Dashboards, Web and Mobile Apps [ 26 ]. A Living Lab Development and Management layer provides on-line tools to developers and stakeholders to implement ingestion and data processing processes such as data flows, data analytics algorithms, Dashboards, IoT Applications, and Web and Mobile Apps [ 15 ].…”

Section: Snap4city Framework and Developmentmentioning

confidence: 99%

“…This means that a smart service development environment has to provide support for the creation of both scheduled and data driven processes, and that the connection from the final user front-end should be easily realized. From the point of view of data transformation developers, the processes are executed in a cluster of processing containers, in a form of IoT Applications (i.e., data flows’ formalization, periodic or data driven), can access and save data by using MicroServices [ 26 ]. In Snap4City, a collection of more than 150 Smart City MicroServices has been developed as Nodes for the Node-RED programming tool.…”

Section: Snap4city Framework and Developmentmentioning

confidence: 99%

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High Density Real-Time Air Quality Derived Services from IoT Networks

Badii

Bilotta

Cenni

et al. 2020

Sensors

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In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices’ dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

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“…Badii et al [32,33] introduce Snap4City, a visual programming environment along with a suite of microservices allowing users to create event-driven IoT applications in the context of smart cities. The platform runs on top of Node-RED [34] and offers a comprehensive set of visual constructs through which users can assemble complex data flows supporting smart city applications (dashboards, route planning, data analytics, etc.).…”

Section: Big Data Framework For Smart Citiesmentioning

confidence: 99%

Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

Ordonez-Ante

Seghbroeck

Wauters

et al. 2020

Sensors

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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.

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MicroServices Suite for Smart City Applications

Cited by 37 publications

References 40 publications

Snap4City Platform to Speed Up Policies

Snap4City Platform to Speed Up Policies

High Density Real-Time Air Quality Derived Services from IoT Networks

Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

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