Special Issue "Smart Data and Semantics in a Sensor World"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 April 2019

Special Issue Editors

Guest Editor
Dr. Sergio Ilarri

University of Zaragoza, Spain
Website | E-Mail
Interests: Data Management, Mobile Computing, Recommendation Systems, Vehicular Networks
Guest Editor
Dr. Laura Po

“Enzo Ferrari” Engineering Department - University of Modena and Reggio Emilia
Website | E-Mail
Phone: +39 059 205 6255
Interests: Linked Data, Open Data, Semantic Web, Recommendation Systems, Ontologies, Web of Data, Smart Cities, eGovernment
Guest Editor
Dr. Raquel Trillo

University of Zaragoza, Spain
Website | E-Mail
Interests: Semantic Web, Information Systems, Data Management

Special Issue Information

Dear Colleagues,

The goal of this Special Issue is to provide a venue to show the practical progress made in the area of data management for sensors, particularly regarding the use of semantic techniques to obtain and exploit smart data from the raw sensor data. The terms “semantic web” and "sensor web" were firstly used more than 15 years ago. Despite this, they are mature topics on which the research community is still very active and with significant research challenges to address.

Since its first inception in 2001, the application of the Semantic Web has carried out an extensive use of ontologies, reasoning, and semantics in diverse field, 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, to face the “Big Data” problem for sensor data, research areas like NLP (Natural Language Processing), ontology matching, and ontology alignment, are providing efficient methodologies based on the RDF and OWL languages to provide standard ways to convert such datasets into Linked Data sources. Due to its interest, in several fields, such as social networks, smart cities, or context-aware mobile applications, it is very relevant to publish the data available as Linked Data. Therefore, the development of techniques to enable users to publish, visualize and manipulate data in an easy way is in high demand. Besides, the standardization of this linked format has completely revolutionized the way of representing and analyzing data, which requires new graph-based machine learning and data mining techniques to explore such representation.

On the other hand, sensing technologies have become an important field for computer scientists. Sensors are sparsely distributed across the globe, leading to an overwhelming amount of data about our environment. Sensors can range from stationary environmental sensors to drones or autonomous vehicles collecting data, or even to humans acting as sensors using smartphones, and can be used to detect a multitude of observations, from simple phenomena to complex events. Moreover, the Sensor Web has realized the idea of a standardized, interoperable platform for everyone to easily share, find, and access sensor data.

However, the various characteristics of sensor data and their corresponding processing requirements, such as their multisource, heterogeneous, real-time, voluminous, streaming, and spatio-temporal features, has led many traditional data processing and integration approaches to show their limitations. Moreover, the lack of integration and communication between sensor networks often isolates important data streams and intensifies the existing problem of having too much data and not enough knowledge.

In this area, Semantic Web technologies have provided particular means to achieve these aims. Specifically, the Semantic Sensor Web (SSW) proposes that sensor data be annotated with semantic metadata, which will increase the interoperability and provide contextual information essential for situational knowledge. Social applications, ubiquitous and pervasive computing are examples of areas making use of semantic measured or processed data. Semantisation, context awareness, community management, and data visualization are core issues related to this area.

This Special Issue intends to provide insights on recent advances in these topics by soliciting original scientific contributions in the form of theoretical foundations, models, experimental research and case studies for developing semantic Web-based applications. We aim to bring together research related to several disciplines, such as Data Management, Knowledge Representation and Engineering, Web of Data, and Sensor Networks, among others. We invite original research contributions on all aspects of the Semantic Web and Sensor Web, as well as their applications. We encourage theoretical, methodological, empirical, and application papers. The submitted papers should describe original work, present significant results, and provide rigorous, principled, and repeatable evaluation. Besides, we appreciate the submission of papers incorporating links to data sets and other material used for evaluation, as well as to live demos and software source code.

Particularly, we encourage submissions focusing on the following themes for this Special Issue.

1. Semantics and Sensor Data
  • Real-time sensor data streams

  • Data management for sensor data

  • Obtention of smart data from sensors

  • Analytics of sensor data streams

  • Semantic modelling and annotation of sensor data

  • Scaling sematic sensor systems

  • Sensor data representation, acquisition, and cleaning

  • Semantic integration of heterogeneous data sources

  • Semantic data management technologies for sensor data

  • Challenges with managing and integrating real-time and historical sensor data

  • Provenance, access control and privacy-preserving issues in semantic data and sensor data

2. Linked Data

  • Linked Data applications and case studies

  • Visualizations and user interfaces for ontologies, sensor data and linked data

  • Machine learning and data mining for the Web of Data

3. Sensor-Based Applications

  • Semantic modelling of Smart City data

  • Mobile web, sensors and semantic streams

  • Smart cities, urban and geospatial data

  • Semantics and sensor data for smart cities

  • Semantics and eGovernment

  • Managing sensor data in transportation applications

  • Collaborative sensing and spatial crowdsourcing

Dr. Sergio Ilarri
Dr. Laura Po
Dr. Raquel Trillo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

Open AccessArticle Ontological Representation of Smart City Data: From Devices to Cities
Appl. Sci. 2019, 9(1), 32; https://doi.org/10.3390/app9010032
Received: 31 October 2018 / Revised: 24 November 2018 / Accepted: 14 December 2018 / Published: 22 December 2018
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Abstract
Existing smart city ontologies allow representing different types of city-related data from cities. They have been developed according to different ontological commitments and hence do not share a minimum core model that would facilitate interoperability among smart city information systems. In this work, [...] Read more.
Existing smart city ontologies allow representing different types of city-related data from cities. They have been developed according to different ontological commitments and hence do not share a minimum core model that would facilitate interoperability among smart city information systems. In this work, a survey has been carried out in order to study available smart city ontologies and to identify the domains they are representing. Taking into account the findings of the survey and a set of ontological requirements for smart city data, a list of ontology design patterns is proposed. These patterns aim to be easily replicated and provide a minimum set of core concepts in order to guide the development of smart city ontologies. Full article
(This article belongs to the Special Issue Smart Data and Semantics in a Sensor World)
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Open AccessArticle Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting
Appl. Sci. 2018, 8(7), 1193; https://doi.org/10.3390/app8071193
Received: 9 June 2018 / Revised: 6 July 2018 / Accepted: 17 July 2018 / Published: 20 July 2018
Cited by 1 | PDF Full-text (3296 KB) | HTML Full-text | XML Full-text
Abstract
Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different [...] Read more.
Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of “isolated information” for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media “heat” for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance. Full article
(This article belongs to the Special Issue Smart Data and Semantics in a Sensor World)
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