Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Features Exploration from Datasets Vision in Air Quality Prediction Domain

Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data

Monitorización de datos de calidad de aire

Cada vez más somos testigos del despliegue de redes de sensores que miden el estado del entorno en el que vivimos. Estas redes aportan grandes volúmenes de información en formatos y escalas muy diversas. Encontramos ejemplos de datos de diversa naturaleza, desde condiciones climáticas, hasta concentraciones de elementos contaminantes debido a la actividad humana como el transporte, y la industria. En este trabajo describimos como la publicación de estos datos mediante servicios que proporcionen un acceso estructurado y basado en estándares permite una mejor integración de estos datos, tanto para su visualización desde diversas plataformas (web o móvil) como para su consumo mediante procesos de análisis que permitan extraer valor añadido y asistir en la toma de decisiones. Uno de los principales objetivos es incrementar la interoperabilidad de acceso a estos datos y que un usuario a través de diferentes dispositivos pueda conocer en tiempo real las condiciones de una ubicación concreta.We are progressively witnessing how more and more sensor networks measure the environment where we live. These networks provide big data volumes in different formats and scales. We find examples of very diverse data, be it from climatic conditions to the levels of concentration of polluting elements following the human activity, such as those related to transport and industries. This article describes how the publication of this data through services providing a structured and standard-based access allows a better integration of the said data, both for its visualisation from different platforms (web, or mobile devices) and its consumption through analysis processes allowing to extract added value and assist in the decision making process. One of its main aims is to increase the interoperability of access to this data so that any user may know in real time and through different devices the conditions of a given location

Approach to Facilitating Geospatial Data and Metadata Publication Using a Standard Geoservice

Nowadays, the existence of metadata is one of the most important aspects of effective discovery of geospatial data published in Spatial Data Infrastructures (SDIs). However, due to lack of efficient mechanisms integrated in the data workflow, to assist users in metadata generation, a lot of low quality and outdated metadata are stored in the catalogues. This paper presents a mechanism for generating and publishing metadata through a publication service. This mechanism is provided as a web service implemented with a standard interface called a web processing service, which improves interoperability between other SDI components. This work extends previous research, in which a publication service has been designed in the framework of the European Directive Infrastructure for Spatial Information in Europe (INSPIRE) as a solution to assist users in automatically publishing geospatial data and metadata in order to improve, among other aspects, SDI maintenance and usability. Also, this work adds more extra features in order to support more geospatial formats, such as sensor data.Sergio Trilles has been funded by the postdoctoral programme Vali+d (GVA) (grant number APOSTD/2016/058). This work has been funded by the European Commission through the GEO-C project (H2020-MSCA-ITN-2014, Grant Agreement number 642332, http://www.geo-c.eu/)

Passive mobile data for studying seasonal tourism mobilities: an application in a Mediterranean Coastal destination

The article uses passive mobile data to analyse the complex mobilities that occur in a coastal region characterised by seasonal patterns of tourism activity. A large volume of data generated by mobile phone users has been selected and processed to subsequently display the information in the form of visualisations that are useful for transport and tourism research, policy, and practice. More specifically, the analysis consisted of four steps: (1) a dataset containing records for four days—two on summer days and two in winter—was selected, (2) these were aggregated spatially, temporally, and differentiating trips by local residents, national tourists, and international tourists, (3) origindestination matrices were built, and (4) graph-based visualisations were created to provide evidence on the nature of the mobilities affecting the study area. The results of our work provide new evidence of how the analysis of passive mobile data can be useful to study the effects of tourism seasonality in local mobility patterns

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

Reconstructing Secondary Data based on Air Quality, Meteorological and Traffic Data Considering Spatiotemporal Components

This paper introduces the reconstructed dataset along with procedures to implement air quality prediction, which consists of air quality, meteorological and traffic data over time, and their monitoring stations and measurement points. Given the fact that those monitoring stations and measurement points are located in different places, it is important to incorporate their time series data into a spatiotemporal dimension. The output can be used as input for various predictive analyses, in particular, we used the reconstructed dataset as input for grid-based (Convolutional Long Short-Term Memory and Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) machine learning algorithms. The raw dataset is obtained from the Open Data portal of the Madrid City Council

Open City Toolkit: the role of geospatial science in making open and participative cities

En la literatura se distinguen dos enfoques diferentes para la transformación de las actuales ciudades en ciudades inteligentes: (a) ofrecer sistemas más eficientes y autónomos a través del uso de la tecnología, sensores, etc.; o (b) educar a los ciudadanos para que puedan hacer frente a los avances tecnológicos en sus ciudades. En este contexto, el proyecto GEO-C (H2020-MSCA-ITN-2014) tiene como objetivo ofrecer estos dos enfoques. Para ello se ofrece una plataforma de software abierto, llamada Open City Toolkit. Dicha plataforma es considerada como la aglutinación de herramientas para capacitar tanto a ciudadanos y desarrolladores como a administraciones públicas, en la participación ciudadana y lograr ciudades más abiertas e inteligentes. Entre estas herramientas se encuentran: aplicaciones, conjuntos de datos, servicios y guías. La Open City Toolkit tiene como misión integrar los avances de investigación provenientes de diferentes temáticas alrededor de las ciudades inteligentes. Dichos avances son los resultados de los diferentes temas de investigación llevados a cabo por los quince estudiantes de doctorado pertenecientes al proyecto. Por otra parte, la caja de herramientas también tiene como objetivo difundir los avances de la ciencia y tecnología geoespacial a los usuarios detallados, para hacer frente a los retos de las ciudades abiertas y participativas.The current literature points out two main approaches regarding the development and enablement of smart cities: on one hand, a technology-driven approach to make systems more efficient and autonomous through sensing technologies; on the other hand, a citizen-driven strategy to educate people so that they can cope with the technological advances in their cities. In this context, the GEO-C project (H2020-MSCA- ITN2014) aims to combine these two approaches by developing an open software platform, called Open City Toolkit. This platform is a toolbox to train citizens, developers and public administrations, to facilitate citizen participation, and to open up cities. These tools include applications, guidelines, services, and datasets. The Open City Toolkit aims to integrate different research results around smart cities. These scientific results are being generated by fifteen doctoral students who are part of the GEO-C project. Moreover, the Open City Toolkit will disseminate the progress of science and technology to end users, to meet the challenges of open and participatory cities.Este trabajo ha sido financiado por la Comisión Europea a través del proyecto GEO-C (H2020-MSCA-ITN-2014, acuerdo de concesión número 642332, http://www.geo-c.eu/). Carlos Granell ha sido financiado por el programa Ramón y Cajal (ayuda RYC-2014-16913). Sergio Trilles ha sido financiando por el programa postdoctoral Vali+d de la Generalitat Valenciana (APOSTD/2016/058)

A Decentralised Location-Based Reputation Management System in the IoT using Blockchain

Weerapanpisit, P., Trilles, S., Huerta, J., & Painho, M. (2022). A Decentralised Location-Based Reputation Management System in the IoT using Blockchain. IEEE Internet of Things Journal. [Advanced online publication on 31 January 2022]. https://doi.org/10.1109/JIOT.2022.3147478 ------------------------- Funding: This study was supported by the TRUST4IoE project of the Programa Estatal de Proyectos de I+D de Generaci´on de Conocimiento of the Spanish government (grant number PID2019-104065GA-I00). Sergio Trilles has been funded by the postdoctoral Juan de la Cierva fellowship programme of the Spanish Ministry for Science and Innovation (IJC2018- 035017-I).The Internet of Things allows an object to connect to the Internet and observe or interact with a physical phenomenon. The communication technologies allow one IoT device to discover and communicate with another in order to exchange services, in a similar way to what humans do in their social networks. Knowing the reputation of another device is important to consider whether it is trustworthy before establishing a new connection and thus avoid possible unexpected behaviours as a consequence. Trustworthiness, as a property of a device, can be affected by different factors including its geographical location. Hence, this research work proposes an architecture to manage reputation values of end devices in an IoT system based on the area where they are located. A cloud-fog-edge architecture is proposed, where the fog layer uses the Blockchain technology to keep the reputation management system consistent and fault-tolerant across different nodes. The location-based part of the system was done by storing geographical areas in Smart Contracts (coined as Geospatial Smart Contracts) and making the reputation values subject to different regions depending on the geographical location of the device. To reduce the complexity of the spatial computation, the geographical data are geocoded by either one of two different spatial indexing techniques. This work also introduced two different structures for storing geocoded areas based on either cell-list or tree-structure. Finally, three experiments to test the proposed architecture are presented, to deploy the architecture in IoT devices, and to compare the two geocoding techniques in Smart Contracts.authorsversionepub_ahead_of_prin