Smart City Ontologies and Their Applications: A Systematic Literature Review

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation

Data Platforms and Cities

This section offers a series of joint reflections on (open) data platform from a variety of cases, from cycling, traffic and mapping to activism, environment and data brokering. Data platforms play a key role in contemporary urban governance. Linked to open data initiatives, such platforms are often proposed as both mechanisms for enhancing the accountability of administrations and performing as sites for 'bottom-up' digital invention. Such promises of smooth flows of data, however, rarely materialise unproblematically. The development of data platforms is always situated in legal and administrative cultures, databases are often built according to the standards of existing digital ecologies, access always involves processes of social negotiation, and interfaces (such as sensors) may become objects of public contestation. The following contributions explore the contested and mutable character of open data platforms as part of heterogeneous publics and trace the pathways of data through different knowledge, skills, public and private configurations. They also reflect on the value of STS approaches to highlight issues and tensions as well as to shape design and governance

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Towards Data Sharing across Decentralized and Federated IoT Data Analytics Platforms

In the past decade the Internet-of-Things concept has overwhelmingly entered all of the fields where data are produced and processed, thus, resulting in a plethora of IoT platforms, typically cloud-based, that centralize data and services management. In this scenario, the development of IoT services in domains such as smart cities, smart industry, e-health, automotive, are possible only for the owner of the IoT deployments or for ad-hoc business one-to-one collaboration agreements. The realization of "smarter" IoT services or even services that are not viable today envisions a complete data sharing with the usage of multiple data sources from multiple parties and the interconnection with other IoT services. In this context, this work studies several aspects of data sharing focusing on Internet-of-Things. We work towards the hyperconnection of IoT services to analyze data that goes beyond the boundaries of a single IoT system. This thesis presents a data analytics platform that: i) treats data analytics processes as services and decouples their management from the data analytics development; ii) decentralizes the data management and the execution of data analytics services between fog, edge and cloud; iii) federates peers of data analytics platforms managed by multiple parties allowing the design to scale into federation of federations; iv) encompasses intelligent handling of security and data usage control across the federation of decentralized platforms instances to reduce data and service management complexity. The proposed solution is experimentally evaluated in terms of performances and validated against use cases. Further, this work adopts and extends available standards and open sources, after an analysis of their capabilities, fostering an easier acceptance of the proposed framework. We also report efforts to initiate an IoT services ecosystem among 27 cities in Europe and Korea based on a novel methodology. We believe that this thesis open a viable path towards a hyperconnection of IoT data and services, minimizing the human effort to manage it, but leaving the full control of the data and service management to the users' will

European Handbook of Crowdsourced Geographic Information

This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research

Ontology-driven urban issues identification from social media.

As cidades em todo o mundo enfrentam muitos problemas diretamente relacionados ao espaço urbano, especialmente nos aspectos de infraestrutura. A maioria desses problemas urbanos geralmente afeta a vida de residentes e visitantes. Por exemplo, as pessoas podem relatar um carro estacionado em uma calçada que está forçando os pedestres a andar na via, ou um enorme buraco que está causando congestionamento. Além de estarem relacionados com o espaço urbano, os problemas urbanos geralmente demandam ações das autoridades municipais. Existem diversas Redes Sociais Baseadas em Localização (LBSN, em inglês) no domínio das cidades inteligentes em todo o mundo, onde as pessoas relatam problemas urbanos de forma estruturada e as autoridades locais tomam conhecimento para então solucioná-los. Com o advento das redes sociais como Facebook e Twitter, as pessoas tendem a reclamar de forma não estruturada, esparsa e imprevisível, sendo difícil identificar problemas urbanos eventualmente relatados. Dados de mídia social, especialmente mensagens do Twitter, fotos e check-ins, tem desempenhado um papel importante nas cidades inteligentes. Um problema chave é o desafio de identificar conversas específicas e relevantes ao processar dados crowdsourcing ruidosos. Neste contexto, esta pesquisa investiga métodos computacionais a fim de fornecer uma identificação automatizada de problemas urbanos compartilhados em mídias sociais. A maioria dos trabalhos relacionados depende de classificadores baseados em técnicas de aprendizado de máquina, como SVM, Naïve Bayes e Árvores de Decisão; e enfrentam problemas relacionados à representação do conhecimento semântico, legibilidade humana e capacidade de inferência. Com o objetivo de superar essa lacuna semântica, esta pesquisa investiga a Extração de Informação baseada em ontologias, a partir da perspectiva de problemas urbanos, uma vez que tais problemas podem ser semanticamente interligados em plataformas LBSN. Dessa forma, este trabalho propõe uma ontologia no domínio de Problemas Urbanos (UIDO) para viabilizar a identificação e classificação dos problemas urbanos em uma abordagem automatizada que foca principalmente nas facetas temática e geográfica. Uma avaliação experimental demonstra que o desempenho da abordagem proposta é competitivo com os algoritmos de aprendizado de máquina mais utilizados, quando aplicados a este domínio em particular.The cities worldwide face with many issues directly related to the urban space, especially in the infrastructure aspects. Most of these urban issues generally affect the life of both resident and visitant people. For example, people can report a car parked on a footpath which is forcing pedestrians to walk on the road or a huge pothole that is causing traffic congestion. Besides being related to the urban space, urban issues generally demand actions from city authorities. There are many Location-Based Social Networks (LBSN) in the smart cities domain worldwide where people complain about urban issues in a structured way and local authorities are aware to fix them. With the advent of social networks such as Facebook and Twitter, people tend to complain in an unstructured, sparse and unpredictable way, being difficult to identify urban issues eventually reported. Social media data, especially Twitter messages, photos, and check-ins, have played an important role in the smart cities. A key problem is the challenge in identifying specific and relevant conversations on processing the noisy crowdsourced data. In this context, this research investigates computational methods in order to provide automated identification of urban issues shared in social media streams. Most related work rely on classifiers based on machine learning techniques such as Support Vector Machines (SVM), Naïve Bayes and Decision Trees; and face problems concerning semantic knowledge representation, human readability and inference capability. Aiming at overcoming this semantic gap, this research investigates the ontology-driven Information Extraction (IE) from the perspective of urban issues; as such issues can be semantically linked in LBSN platforms. Therefore, this work proposes an Urban Issues Domain Ontology (UIDO) to enable the identification and classification of urban issues in an automated approach that focuses mainly on the thematic and geographical facets. Experimental evaluation demonstrates the proposed approach performance is competitive with most commonly used machine learning algorithms applied for that particular domain.CNP

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Mobile Sensing Systems

[EN] Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing. There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc. The object of sensing can be people-centered or environment-centered. The sensing domain can be home, urban, vehicular Currently there are barriers that limit the social acceptance of mobile sensing systems. Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign. Several technical barriers are phone energy savings and the variety of sensors and software for their management. Some existing surveys partially tackle the topic of mobile sensing systems. Published papers theoretically or partially solve the above barriers. We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges. The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high.This work has been partially supported by the "Ministerio de Ciencia e Innovacion", through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental", project TEC2011-27516, and by the Polytechnic University of Valencia, through the PAID-05-12 multidisciplinary projects.Macias Lopez, EM.; Suarez Sarmiento, A.; Lloret, J. (2013). Mobile Sensing Systems. Sensors. 13(12):17292-17321. https://doi.org/10.3390/s131217292S1729217321131