Special Issue on Smart Data and Semantics in a Sensor World

Introduction Since its first inception in 2001, the application of the Semantic Web [1, 2] has carried out an extensive use of ontologies [3–5], reasoning, and semantics in diverse fields, such as Information Integration, Software Engineering, Bioinformatics, eGovernment, eHealth, and social networks. This widespread use of ontologies has led to an incredible advance in the development of techniques to manipulate, share, reuse, and integrate information across heterogeneous data sources. In recent years, the growth of the IoT (Internet of Things) required to face the challenges of “Big Data” [6–10]. The cost of sensors is decreasing, while their use is expanding. Moreover, the use of multiple personal smart devices is an emerging trend and all of them can embed sensors to monitor the surrounding environment. Therefore, the number of available sensors is exploding. On the one hand, the flows of sensor data are massive and continuous, and the data could be obtained in real time or with a delay of just a few seconds. Then, the volume of sensor data is increasing continuously every day. On the other hand, the variety of data being generated is also increasing, due to plenty of different devices and different measures to record. There are many kinds of structured and unstructured sensor data in diverse formats. Moreover, data veracity, which is the degree of accuracy or truthfulness of a data set, is an important aspect to consider. In the context of sensor data, it represents the trustworthiness of the data source and the processing of data. The need for more accurate and reliable data was always declared, but often overlooked for the sake of larger and cheaper..

Location-aware recommendation systems: Where we are and where we recommend to go

Recommendation systems have been successfully used to provide items of interest to the users (e.g., movies, music, books, news, images). However, traditional recommenda- tion systems do not take into account the location as a relevant factor when providing suggestions. On the other hand, nowadays, there exist an increasing amount of geo- referenced data and users are usually interested only in nearby items (e.g., restaurants, museums, cinemas). Hence, the emergence of location-aware recommendation systems have acquired a great attention by the research community in the last decade. In this paper, we provide a survey of location-aware rec- ommendation systems in mobile computing scenarios. Firstly, we describe briefly the fundamentals of recommendation sys- tems. Then, we introduce some of the most relevant existing approaches for location-aware recommendation. Moreover, we present the main applications of this type of systems in several recommendation scenarios, such as music, news, restaurants, etc. Finally, we discuss new avenues and open issues in the area

Towards Trajectory-Based Recommendations in Museums: Evaluation of Strategies Using Mixed Synthetic and Real Data

Recommendation systems, which suggest items that are of potential interest to the user (e.g., regarding which books to read, which movies to watch, etc.) have grown in popularity due to the ever-increasing amount of data available, that can lead to significant user''s overload. In particular, in recent years, extensive research has focused on the so-called Context-Aware Recommender Systems (CARS), which exploit context data to offer more relevant recommendations. In this paper, we study this problem with a use case scenario: recommending items to observe in a museum. We propose a trajectory-based and user-based collaborative filtering approach, that considers context data such as the location of the user and his/her trajectory to offer personalized recommendations. Besides, we exploit DataGenCARS, a dataset synthetic generator designed to construct datasets for the evaluation of context-aware recommendation systems, to build a mixed scenario based on both real and synthetic data. The experimental results show the advantages of the proposed approach and the usefulness of DataGenCARS for practical evaluation with a real use-case scenario. Peer-review under responsibility of the Conference Program Chairs

An Integrated Smart City Platform

Smart Cities aim to create a higher quality of life for their citizens, improve business services and promote tourism experience. Fostering smart city innovation at local and regional level requires a set of mature technologies to discover, integrate and harmonize multiple data sources and the exposure of eective applications for end-users (citizens, administrators, tourists...). In this context, Semantic Web technologies and Linked Open Data principles provide a means for sharing knowledge about cities as physical, economical, social, and technical systems, enabling the development of smart city services. Despite the tremendous effort these communities have done so far, there exists a lack of comprehensive and effective platforms that handle the entire process of identication, ingestion, consumption and publication of data for Smart Cities. In this paper, a complete open-source platform to boost the integration, semantic enrichment, publication and exploitation of public data to foster smart cities in local and national administrations is proposed. Starting from mature software solutions, we propose a platform to facilitate the harmonization of datasets (open and private, static and dynamic on real time) of the same domain generated by dierent authorities. The platform provides a unied dataset oriented to smart cities that can be exploited to offer services to the citizens in a uniform way, to easily release open data, and to monitor services status of the city in real time by means of a suite of web applications

Semantic traffic sensor data: The TRAFAIR experience

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city''s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective

Internet of things (IoT) as sustainable development goals (SDG) enabling technology towards smart readiness indicators (SRI) for university buildings

Non-residential buildings contribute to around 20% of the total energy consumed in Europe. This consumption continues to increase globally. Smart building proposals (focused on Nearly Zero Energy Building (NZEB), air quality monitoring, energy saving with thermal comfort, etc.) were already necessary before 2020, and the pandemic has made this research and development area more essential. Furthermore, the need to meet the Sustainable Development Goals (SDG) and obtain technological solutions based on the Internet of Things (IoT) requires holistic contributions through real installations that serve as spaces for measuring, testing, study and research. This article proposes a “measure–analyse–decide and act” methodology to quantify the Smart Readiness Indicator (SRI) for university buildings as a reference environment for energy efficiency and COVID-19 prevention models. Two conceptual spaces (physical and digital) within two dimensions (users and infrastructures) are designated over an IoT three-level model (information acquisition, interoperable communication, and data-driven decision). An IoT ecosystem (sensoriZAR) was implemented as a proof-of-concept of a smart campus at the University of Zaragoza, Spain. Focused on CO2 and energy consumption monitoring, the results showed effectiveness through real installations, demonstrating the IoT potential as SDG-enabling technologies. These contributions allow not only experimental lab tests (from the authors’ expertise in several specialties of Industrial, Mechanical, Design, Thermal, Electrical, Electronic, Computer and Telecommunication Engineering) but also a reference model for direct application in academic works, research projects and institutional initiatives, extendable to professional environments, buildings and cities. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)

Automatic Publication of Open Data from OGC Services: the Use Case of TRAFAIR Project

This work proposes a workflow for the publication of Open Spatial Data. The main contribution of this work is the automatic generation of metadata extracted from OGC spatial services providing access to feature types and coverages. Besides, this work adopts the GeoDCAT-AP metadata profile for the description of datasets because it allows for an appropriate crosswalk between the annotation requirements in the spatial domain and the metadata models accepted in general Open Data portals. The feasibility of the proposed workflow has been tested within the framework of the TRAFAIR project to publish monitoring and forecasting air quality data

TAQE: A Data Modeling Framework for Traffic and Air Quality Applications in Smart Cities

Air quality and traffic monitoring and prediction are critical problems in urban areas. Therefore, in the context of smart cities, many relevant conceptual models and ontologies have already been proposed. However, the lack of standardized solutions boost development costs and hinder data integration between different cities and with other application domains. This paper proposes a classification of existing models and ontologies related to Earth observation and modeling and smart cities in four levels of abstraction, which range from completely general-purpose frameworks to application-specific solutions. Based on such classification and requirements extracted from a comprehensive set of state-of-the-art applications, TAQE, a new data modeling framework for air quality and traffic data, is defined. The effectiveness of TAQE is evaluated both by comparing its expressiveness with the state-of-the-art of the same application domain and by its application in the ``TRAFAIR -- Understanding traffic flows to improve air quality" EU project