Analysis and assessment of a knowledge based smart city architecture providing service APIs

Abstract The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs. Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented

Distributed Data-Gathering and -Processing in Smart Cities: An Information-Centric Approach

The technological advancements along with the proliferation of smart and connected devices (things) motivated the exploration of the creation of smart cities aimed at improving the quality of life, economic growth, and efficient resource utilization. Some recent initiatives defined a smart city network as the interconnection of the existing independent and heterogeneous networks and the infrastructure. However, considering the heterogeneity of the devices, communication technologies, network protocols, and platforms the interoperability of these networks is a challenge requiring more attention. In this paper, we propose the design of a novel Information-Centric Smart City architecture (iSmart), focusing on the demand of the future applications, such as efficient machineto-machine communication, low latency computation offloading, large data communication requirements, and advanced security. In designing iSmart, we use the Named-Data Networking (NDN) architecture as the underlying communication substrate to promote semantics-based communication and achieve seamless compute/data sharing

Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encour-aging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility‐as‐a‐service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope

A digital twin decision support system for the urban facility management process

The ever increasing pace of IoT deployment is opening the door to concrete implementa-tions of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and environments. This digital replica could serve as the basis of a decision support system, providing insights into possible optimizations of resources in a smart city scenario. In this article, we discuss an extension of a prior work, presenting a detailed proof-of-concept implementation of a Digital Twin solution for the Urban Facility Management (UFM) process. The Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO) is a distributed geographical system, fed with and ingesting heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and build synthetic indexes measuring the activity level inside an area of interest. IPPODAMO takes into account potential interference from other stakeholders in the urban environment, enabling the informed scheduling of operations, aimed at minimizing interference and the costs of operations

Distributed cloud-edge analytics and machine learning for transportation emissions estimation

(English) In recent years IoT and Smart Cities have become a popular paradigm of computing that is based on network-enabled devices connected providing different functionalities, from sensor measures to domotic actions. With this paradigm, it is possible to provide to the stakeholders near-realtime information of the field, e.g. the current pollution of the city. Along with the mentioned paradigms, Fog Computing enables computation near the sensors where the data is produced, i.e. Edge nodes. This paradigm provides low latency and fault tolerance given the possible independence of the sensor devices. Moreover, pushing this computation enables derived results in a near-realtime fashion. This ability to push the computation to where the data is produced can be beneficial in many situations, however it also requires to include in the Edge the data preparation processes that ensure the fitness for use of the data as the incoming data can be erroneous. Given this situation, Machine Learning can be useful to correct data and also to produce predictions of the future values. Even though there have been studies regarding on the uses of data at the Edge, to our knowledge there is no evaluation of the different modeling situations and the viability of the approach. Therefore, this thesis aims to evaluate the possibility of building a distributed system that ensures the fitness for use of the incoming data through Machine Learning enabled Data Preparation, estimates the emissions and predicts the future status of the city in a near-realtime fashion. We evaluate the viability through three contributions. The first contribution focuses on forecasting in a distributed scenario with road traffic dataset for evaluation. It provides a robust solution to build a central model. This approach is based on Federated Learning, which allows training models at the Edge nodes and then merging them centrally. This way the models in the Edge can be independent but also can be synchronized. The results show the trade-off between accuracy versions training time and a comparison between low-powered devices versus server-class machines. These analyses show that it is viable to use Machine Learning with this paradigm. The second contribution focuses on a particular use case of ship emission estimation. To estimate exhaust emissions data must be correct, which is not always the case. This contribution explores the different techniques available to correct ship registry data and proposes the usage of simple Machine Learning techniques to do imputation of missing or erroneous values. This contribution analyzes the different variables and their relationship to provide the practitioners with guidelines for correction and data treatment. The results show that with classical Machine Learning it is possible to improve the state-of-the-art results. Moreover, as these algorithms are simple enough, they can be used in an Edge device if required. The third contribution focuses on generating new variables from the ones available with a ship trace dataset obtained from the Automatic Identification System (AIS). We use a pipeline of two different methods, a Neural Networks and a clustering algorithm, to group movements into movement patterns or \emph{behaviors}. We test the predicting power of these behaviors to predict ship type, main engine power, and navigational status. The prediction of the main engine power is compared against the standard technique used in ship emission estimation when the ship registry is missing. Our approach was able to detect 45\% of the otherwise undetected emissions if the baseline method was to be used. As ship navigational status is prone to error, the behaviors found are proposed as an alternative variable based in robust data. These contributions build a framework that can distribute the learning processes and that resists network failures in low-powered devices.(Español) En los últimos años, IoT y las Smart Cities se han convertido en un paradigma popular de computación que se basa en dispositivos conectados a la red que proporcionan diferentes funcionalidades, desde medidas de sensores hasta acciones domóticas. Con este paradigma, es posible tener información en casi tiempo real, como por ejemplo la contaminación actual de la ciudad. Junto con los paradigmas mencionados, Fog Computing permite computar cerca de donde se producen los datos, es decir, los nodos Edge. Este paradigma proporciona baja latencia y tolerancia a fallos dada la posible independencia de los dispositivos sensores. Esta posibilidad puede ser beneficiosa en muchas situaciones, sin embargo, requiere incluir en el Edge los procesos de preparación de datos que aseguran la idoneidad para su uso, ya que los datos entrantes pueden ser erróneos. Ante esta situación, el Machine Learning es útil para corregir datos y también para producir predicciones de los valores futuros. A pesar de que se han realizado estudios sobre los usos de los datos en el Edge, hasta donde sabemos, no hay una evaluación de las diferentes situaciones de modelado y la viabilidad del enfoque. Por lo tanto, esta tesis tiene como objetivo evaluar la posibilidad de construir un sistema distribuido que garantice que los datos sean correctos a través de su preparación con Machine Learning. También el sistema deberá estimar las emisiones y predecir el estado futuro de la ciudad de una manera casi en tiempo real. La viabilidad se evalúa a través a través de tres contribuciones. La primera contribución se centra en escenario distribuido con un conjunto de datos de tráfico vial que proporciona una solución robusta para construir un modelo central. Este enfoque se basa en Federated Learning, que permite entrenar modelos en los nodos Edge y luego fusionarlos de forma centralizada. De esta manera, los modelos en el Edge pueden ser independientes, pero también se pueden sincronizar. Los resultados muestran la comparación de la precisión con un modelo central y uno distribuido y una comparación con dispositivos de bajo consumos contra servidores. Estos análisis muestran que es viable utilizar el Machine Learning en este paradigma. La segunda contribución se centra en un caso de uso particular de estimación de las emisiones de barcos. Para estimar las emisiones, los datos deben ser correctos, cosa que no siempre pasa. Esta contribución explora las diferentes técnicas disponibles para corregir los datos del registro de barcos y propone el uso de técnicas simples de Machine Learning para hacer imputación de valores faltantes o erróneos. Esta contribución analiza las diferentes variables y su relación para proporcionar a los profesionales pautas para la corrección y el tratamiento de datos. Los resultados muestran que con el Machine Learning clásico es posible mejorar los resultados frente a métodos del estado del arte. Además, como estos algoritmos son lo suficientemente simples como para poder utilizarse en dispositivos Edge. La tercera contribución se centra en generar nuevas variables a partir de las disponibles con un conjunto de datos de trazabilidad de barcos obtenido del Sistema AIS. Esto se hace utilizando en conjunto una red neuronal y un algoritmo de agrupación para agrupar los movimientos en patrones de movimiento o comportamientos. Se evalúa su funcionamiento para predecir el tipo de barco, la potencia del motor principal y el estado de navegación. Con esta predicción, nuestro sistema es capaz de detectar el 45% de las emisiones que no se detectan con métodos standard. Como el estado de navegación del barco es propenso a errores, los comportamientos encontrados se proponen como una variable alternativa basada en datos robustos. Estas contribuciones constituyen un marco para distribuir los procesos de aprendizaje y que resiste errores en la red con dispositivos de bajo consumo.Arquitectura de computador

Orchestration of distributed ingestion and processing of IoT data for fog platforms

In recent years there has been an extraordinary growth of the Internet of Things (IoT) and its protocols. The increasing diffusion of electronic devices with identification, computing and communication capabilities is laying ground for the emergence of a highly distributed service and networking environment. The above mentioned situation implies that there is an increasing demand for advanced IoT data management and processing platforms. Such platforms require support for multiple protocols at the edge for extended connectivity with the objects, but also need to exhibit uniform internal data organization and advanced data processing capabilities to fulfill the demands of the application and services that consume IoT data. One of the initial approaches to address this demand is the integration between IoT and the Cloud computing paradigm. There are many benefits of integrating IoT with Cloud computing. The IoT generates massive amounts of data, and Cloud computing provides a pathway for that data to travel to its destination. But today’s Cloud computing models do not quite fit for the volume, variety, and velocity of data that the IoT generates. Among the new technologies emerging around the Internet of Things to provide a new whole scenario, the Fog Computing paradigm has become the most relevant. Fog computing was introduced a few years ago in response to challenges posed by many IoT applications, including requirements such as very low latency, real-time operation, large geo-distribution, and mobility. Also this low latency, geo-distributed and mobility environments are covered by the network architecture MEC (Mobile Edge Computing) that provides an IT service environment and Cloud-computing capabilities at the edge of the mobile network, within the Radio Access Network (RAN) and in close proximity to mobile subscribers. Fog computing addresses use cases with requirements far beyond Cloud-only solution capabilities. The interplay between Cloud and Fog computing is crucial for the evolution of the so-called IoT, but the reach and specification of such interplay is an open problem. This thesis aims to find the right techniques and design decisions to build a scalable distributed system for the IoT under the Fog Computing paradigm to ingest and process data. The final goal is to explore the trade-offs and challenges in the design of a solution from Edge to Cloud to address opportunities that current and future technologies will bring in an integrated way. This thesis describes an architectural approach that addresses some of the technical challenges behind the convergence between IoT, Cloud and Fog with special focus on bridging the gap between Cloud and Fog. To that end, new models and techniques are introduced in order to explore solutions for IoT environments. This thesis contributes to the architectural proposals for IoT ingestion and data processing by 1) proposing the characterization of a platform for hosting IoT workloads in the Cloud providing multi-tenant data stream processing capabilities, the interfaces over an advanced data-centric technology, including the building of a state-of-the-art infrastructure to evaluate the performance and to validate the proposed solution. 2) studying an architectural approach following the Fog paradigm that addresses some of the technical challenges found in the first contribution. The idea is to study an extension of the model that addresses some of the central challenges behind the converge of Fog and IoT. 3) Design a distributed and scalable platform to perform IoT operations in a moving data environment. The idea after study data processing in Cloud, and after study the convenience of the Fog paradigm to solve the IoT close to the Edge challenges, is to define the protocols, the interfaces and the data management to solve the ingestion and processing of data in a distributed and orchestrated manner for the Fog Computing paradigm for IoT in a moving data environment.En els últims anys hi ha hagut un gran creixement del Internet of Things (IoT) i els seus protocols. La creixent difusió de dispositius electrònics amb capacitats d'identificació, computació i comunicació esta establint les bases de l’aparició de serveis altament distribuïts i del seu entorn de xarxa. L’esmentada situació implica que hi ha una creixent demanda de plataformes de processament i gestió avançada de dades per IoT. Aquestes plataformes requereixen suport per a múltiples protocols al Edge per connectivitat amb el objectes, però també necessiten d’una organització de dades interna i capacitats avançades de processament de dades per satisfer les demandes de les aplicacions i els serveis que consumeixen dades IoT. Una de les aproximacions inicials per abordar aquesta demanda és la integració entre IoT i el paradigma del Cloud computing. Hi ha molts avantatges d'integrar IoT amb el Cloud. IoT genera quantitats massives de dades i el Cloud proporciona una via perquè aquestes dades viatgin a la seva destinació. Però els models actuals del Cloud no s'ajusten del tot al volum, varietat i velocitat de les dades que genera l'IoT. Entre les noves tecnologies que sorgeixen al voltant del IoT per proporcionar un escenari nou, el paradigma del Fog Computing s'ha convertit en la més rellevant. Fog Computing es va introduir fa uns anys com a resposta als desafiaments que plantegen moltes aplicacions IoT, incloent requisits com baixa latència, operacions en temps real, distribució geogràfica extensa i mobilitat. També aquest entorn està cobert per l'arquitectura de xarxa MEC (Mobile Edge Computing) que proporciona serveis de TI i capacitats Cloud al edge per la xarxa mòbil dins la Radio Access Network (RAN) i a prop dels subscriptors mòbils. El Fog aborda casos d?us amb requisits que van més enllà de les capacitats de solucions només Cloud. La interacció entre Cloud i Fog és crucial per a l'evolució de l'anomenat IoT, però l'abast i especificació d'aquesta interacció és un problema obert. Aquesta tesi té com objectiu trobar les decisions de disseny i les tècniques adequades per construir un sistema distribuït escalable per IoT sota el paradigma del Fog Computing per a ingerir i processar dades. L'objectiu final és explorar els avantatges/desavantatges i els desafiaments en el disseny d'una solució des del Edge al Cloud per abordar les oportunitats que les tecnologies actuals i futures portaran d'una manera integrada. Aquesta tesi descriu un enfocament arquitectònic que aborda alguns dels reptes tècnics que hi ha darrere de la convergència entre IoT, Cloud i Fog amb especial atenció a reduir la bretxa entre el Cloud i el Fog. Amb aquesta finalitat, s'introdueixen nous models i tècniques per explorar solucions per entorns IoT. Aquesta tesi contribueix a les propostes arquitectòniques per a la ingesta i el processament de dades IoT mitjançant 1) proposant la caracterització d'una plataforma per a l'allotjament de workloads IoT en el Cloud que proporcioni capacitats de processament de flux de dades multi-tenant, les interfícies a través d'una tecnologia centrada en dades incloent la construcció d'una infraestructura avançada per avaluar el rendiment i validar la solució proposada. 2) estudiar un enfocament arquitectònic seguint el paradigma Fog que aborda alguns dels reptes tècnics que es troben en la primera contribució. La idea és estudiar una extensió del model que abordi alguns dels reptes centrals que hi ha darrere de la convergència de Fog i IoT. 3) Dissenyar una plataforma distribuïda i escalable per a realitzar operacions IoT en un entorn de dades en moviment. La idea després d'estudiar el processament de dades a Cloud, i després d'estudiar la conveniència del paradigma Fog per resoldre el IoT prop dels desafiaments Edge, és definir els protocols, les interfícies i la gestió de dades per resoldre la ingestió i processament de dades en un distribuït i orquestrat per al paradigma Fog Computing per a l'IoT en un entorn de dades en moviment

BIG DATA IN SMART CITIES: A SYSTEMATIC MAPPING REVIEW

Big data is an emerging area of research and its prospective applications in smart cities are extensively recognized. In this study, we provide a breadth-first review of the domain “Big Data in Smart Cities” by applying the formal research method of systematic mapping. We investigated the primary sources of publication, research growth, maturity level of the research area, prominent research themes, type of analytics applied, and the areas of smart cities where big data research is produced. Consequently, we identified that empirical research in the domain has been progressing since 2013. The IEEE Access journal and IEEE Smart Cities Conference are the leading sources of literature containing 10.34% and 13.88% of the publications, respectively. The current state of the research is semi-matured where research type of 46.15% of the publications is solution and experience, and contribution type of 60% of the publications is architecture, platform, and framework. Prescriptive is least whereas predictive is the most applied type of analytics in smart cities as it has been stated in 43.08% of the publications. Overall, 33.85%, 21.54%, 13.85%, 12.31%, 7.69%, 6.15%, and 4.61% of the research produced in the domain focused on smart transportation, smart environment, smart governance, smart healthcare, smart energy, smart education, and smart safety, respectively. Besides the requirement for producing validation and evaluation research in the areas of smart transportation and smart environment, there is a need for more research efforts in the areas of smart healthcare, smart governance, smart safety, smart education, and smart energy. Furthermore, the potential of prescriptive analytics in smart cities is also an area of research that needs to be explored

The Hitchhiker’s Guide to the Metaverse

Non peer reviewe

A Novel Real-Time Edge-Cloud Big Data Management and Analytics Framework for Smart Cities

Exposing city information to dynamic, distributed, powerful, scalable, and user-friendly big data systems is expected to enable the implementation of a wide range of new opportunities; however, the size, heterogeneity and geographical dispersion of data often makes it difficult to combine, analyze and consume them in a single system. In the context of the H2020 CLASS project, we describe an innovative framework aiming to facilitate the design of advanced big-data analytics workflows. The proposal covers the whole compute continuum, from edge to cloud, and relies on a well-organized distributed infrastructure exploiting: a) edge solutions with advanced computer vision technologies enabling the real-time generation of “rich” data from a vast array of sensor types; b) cloud data management techniques offering efficient storage, real-time querying and updating of the high-frequency incoming data at different granularity levels. We specifically focus on obstacle detection and tracking for edge processing, and consider a traffic density monitoring application, with hierarchical data aggregation features for cloud processing; the discussed techniques will constitute the groundwork enabling many further services. The tests are performed on the real use-case of the Modena Automotive Smart Area (MASA)