In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.
The process of modeling a city is complex. The intrinsic complexity of interactions between city entities make it very difficult to map relevant sets of dynamic aspects that are often used to characterize a city. Moreover, these entity interactions, along with the numerous entities and processes, differ from one city to another. Thus, the process of modeling the city is typically use-case centered, where the modeling is performed towards a specified goal. This approach, hence, streamlines the process, identifying which characteristics need to be modeled. The work in @cite_1 proposes a core conceptual model for the Domain Knowledge Model of a Smart City, which originally involves multiple domains and cities. The proposed work aims to support cross-domain and cross-city interoperability by specifying terms from different stakeholders. Ontologies play a big role in enabling cross-city comparison. The Semantic Web has been used in the Open Government Data (OGD) approach to make it possible for cities to share information and knowledge under a common vocabulary. Pushing this further, the GCI (Global City Indicators) Ontology @cite_7 is an effort for the modeling of city entities that covers the concepts used by global indicators using Semantic Web technologies.
@cite_1 @cite_7
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As the emergence of the requirement of building Smart Cities across many countries, the Domain Knowledge Model of Smart City (Short as DKM4SC) becomes necessary. The Domain Knowledge Model of Smart City has abundant contents and involves many domains and cities. In order to support cross-domain and cross-city interoperation of knowledge, we should concise the common concepts and their relationship from domains and cities and construct a core concept model (short as CCM of DKM4SC) that can specify terms from different stakeholders, support semantic understanding and give standard knowledge expression. The model is proposed to represent the core concept of Smart City for exchanging data, service. A case study is presented to illustrate the usage of the model. Abstract This paper addresses the problem of how city indicators and their supporting data are to be published on the Semantic Web so that automated analysis can be performed. With the publishing of ISO 37120, cities have a standard set of indicators that can be used to compare their performance. The problem is that no standards exist for publishing indicators on the Semantic Web. In this paper we introduce the Global City Indicator Ontology (GCIO). The GCIO addresses five issues: 1) how is meta-data associated with a single indicator value represented? 2) how are indicator definitions represented? 3) how is the data used to derive an indicator value represented? 4) how is indicator theme specific knowledge represented, and 5) how is city specific knowledge represented? The GCIO has been implemented and validated using the City of Toronto ISO 37120 indicators reported for 2013. Research continues in developing ontologies specifically for each of the indicator themes such as: Education, Shelter, Health, Transportation and Innovation. The Smart City concept is being developed from a lot of different axes encompassing multiple areas of social and technical sciences. However, something that is common to all these approaches is the central role that the capacity of sharing information has. Hence, Information and Communication Technologies ICT are seen as key enablers for the transformation of urban regions into Smart Cities. Two of these technologies, namely Internet of Things and Big Data, have a predominant position among them. The capacity to "sense the city" and access all this information and provide added-value services based on knowledge derived from it are critical to achieving the Smart City vision. This paper reports on the specification and implementation of a software platform enabling the management and exposure of the large amount of information that is continuously generated by the IoT deployment in the city of Santander. Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges. With the growing popularity of Internet of Things (IoT) technologies and sensors deployment, more and more cities are leaning towards the initiative of smart cities. Smart city applications are mostly developed with aims to solve domain-specific problems. Hence, lacking the ability to automatically discover and integrate heterogeneous sensor data streams on the fly. To provide a domain-independent platform and take full benefits from semantic technologies, in this paper we present an Automated Complex Event I mplementation System (ACEIS), which serves as a middleware between sensor data streams and smart city applications. ACEIS discovers and integrates IoT streams in urban infrastructures for users' requirements expressed as complex event requests, based on semantic IoT stream descriptions. It also processes complex event patterns on the fly using semantic data streams. This chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns. Thanks to an unprecedented collection of counter data recording the number of calls, SMS, and data transfers resolved both in time and space, we confirm the connection between temporal activity profile and land usage in three global cities: New York, London, and Hong Kong. By comparing whole cities’ typical patterns, we provide insights on how cultural, technological, and economical factors shape human dynamics. At a more local scale, we use clustering analysis to identify locations with similar patterns within a city. Our research reveals a universal structure of cities, with core financial centers all sharing similar activity patterns and commercial or residential areas with more city-specific patterns. These findings hint that as the economy becomes more global, common patterns emerge in business areas of different cities across the globe, while the impact of local conditions still remains recognizable on the level of routine people activity. With the growing popularity of Internet of Things (IoT) and IoT-enabled smart city applications, RDF stream processing (RSP) is gaining increasing attention in the Semantic Web community. As a result, several RSP engines have emerged, which are capable of processing semantically annotated data streams on the fly. Performance, correctness and technical soundness of few existing RSP engines have been evaluated in controlled settings using existing benchmarks like LSBench and SRBench. However, these benchmarks focus merely on features of the RSP query languages and engines, and do not consider dynamic application requirements and data-dependent properties such as changes in streaming rate during query execution or changes in application requirements over a period of time. This hinders wide adoption of RSP engines for real-time applications where data properties and application requirements play a key role and need to be characterised in their dynamic setting, such as in the smart city domain. Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases. Instead of collecting a large number of annotated images of each city of interest to train or refine the segmenter, we propose an unsupervised learning approach to adapt road scene segmenters across different cities. By utilizing Google Street View and its timemachine feature, we can collect unannotated images for each road scene at different times, so that the associated static-object priors can be extracted accordingly. By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that city can be achieved without the need of any user annotation or interaction. We show that our method improves the performance of semantic segmentation in multiple cities across continents, while it performs favorably against state-of-the-art approaches requiring annotated training data. Smart cities are a hot topic nowadays for several reasons. For city managers and citizens a smart city is a concept that should allow providing better services and or with more efficiency. For technicians a smart city represents one of the large-scale deployments of equipment for data capturing, storage and processing. For Internet researchers a smart city can become the first massive installments of the Internet of Things concept. Smart cities are commonly implemented in several parts: the data acquisition and actuation, the aggregation networks that connect sensors actuators with the general purpose network, the platform where data is stored and processed and the application that use the information for informing the city managers and citizens. The most costly parts of a smart city are the sensor actuator and the aggregation network. This cost is due mainly due to the deployment and maintenance of the equipment on the city street. This paper describes the different alternatives for building the aggregation networks and provides a novel approach to facilitate the deployment of a smart city. This new solution has been tested in a real scenario achieving satisfactory results. © 2014 IEEE. Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases. Instead of collecting a large number of annotated images of each city of interest to train or refine the segmenter, we propose an unsupervised learning approach to adapt road scene segmenters across different cities. By utilizing Google Street View and its time-machine feature, we can collect unannotated images for each road scene at different times, so that the associated static-object priors can be extracted accordingly. By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that city can be achieved without the need of any user annotation or interaction. We show that our method improves the performance of semantic segmentation in multiple cities across continents, while it performs favorably against state-of-the-art approaches requiring annotated training data. We compare the structural properties of the street networks of ten different European cities using their primal representation. We investigate the properties of the geometry of the networks and a set of centrality measures highlighting differences and similarities between cases. In particular, we found that cities share structural similarities due to their quasiplanarity but that there are also several distinctive geometrical properties. A principal component analysis is performed on the distributions of centralities and their respective moments, which is used to find distinctive characteristics by which we can classify cities into families. We believe that, beyond the improvement of the empirical knowledge on streets' network properties, our findings can open new perspectives into the scientific relationship between city planning and complex networks, stimulating the debate on the effectiveness of the set of knowledge that statistical physics can contribute for city planning and urban-morphology studies. The Internet of Things (IoT) is being adopted in different application domains and is recognized as one of the key enablers of the Smart City vision. Despite the standardization efforts and wide adoption of Web standards and cloud computing technologies, however, building large-scale Smart City IoT platforms in practice remains challenging. The dynamically changing IoT environment requires these systems to be able to scale and evolve over time adopting new technologies and requirements. In response to the similar challenges in building large-scale distributed applications and platforms on the Web, micro service architecture style has emerged and gained a lot of popularity in the industry in recent years. In this work, we share our early experience of applying the micro service architecture style to design a Smart City IoT platform. Our experience suggests significant benefits provided by this architectural style compared to the more generic Service-Oriented Architecture (SOA) approaches, as well as highlights some of the challenges it introduces. Vehicular Networks implementation has become a necessity in Smart Cities, due to the opportunistic network connectivity it offers within the city, where researches has began to focus more on Vehicular Networks, to improve the control over traffics and road safety inside the city; other researches on Smart Cities are more concerned about the transmission of the data collected by sensors, nodes, devices installed around the city to the back-end servers where they can be processed and analyzed. In this paper, we propose possible alternative network for Smart Cities, to forward informative data stored in stations implemented around the city without the needs of pre-installed infrastructures, by relying on public and semi-public vehicle transports for network connectivity between the stations and the back-end server. Abstract Street networks are complex networks that represent the topology and geometry of cities; as so, they can be used to solve problems related to ill-designed urban structures. This application, in real urban scenarios, has been the focus of several types of research, from cities characterization to transportation enhancement. Nevertheless, these works lack a clear and in-depth methodology to characterize the urban space by means of complex networks. Aided by topo-geometrical measures from street networks, we present a methodology to identify what we call urban inconsistencies, which are characterized by low-access regions containing nodes (crossing streets) that lack efficient access from or to other regions in a city. We devised algorithms capable of preprocessing and analyzing street networks, pointing to existing mobility problems in a city. We identify inconsistencies that pertain to a given node where a facility of interest is currently placed; the results introduce ways to assist in the urban planning and design processes. Our techniques are discussed through the analysis of a real-world city. In a real context, the methods provide basis for analyzing and improving the placement of facilities. Traces of moving objects in a city, which depict lots of semantics concerning human mobility and city dynamics, are becoming increasingly important. In this article, we first give a brief introduction to trace data; then we present six research issues in trace analysis and mining, and survey the state-of-the-art methods; finally, five promising application domains in smart cities are discussed. After hundreds of years of human settlement, each city has formed a distinct identity, distinguishing itself from other cities. In this work, we propose to characterize the identity of a city via an attribute analysis of 2 million geo-tagged images from 21 cities over 3 continents. First, we estimate the scene attributes of these images and use this representation to build a higher-level set of 7 city attributes, tailored to the form and function of cities. Then, we conduct the city identity recognition experiments on the geo-tagged images and identify images with salient city identity on each city attribute. Based on the misclassification rate of the city identity recognition, we analyze the visual similarity among different cities. Finally, we discuss the potential application of computer vision to urban planning. Terms used to describe city centers, such as Downtown, are key concepts in everyday or vernacular language. Here, we explore such language by harvesting georeferenced and tagged metadata associated with 8 million Flickr images and thus consider how large numbers of people name city core areas. The nature of errors and imprecision in tagging and georeferencing are quantified, and automatically generated precision measures appear to mirror errors in the positioning of images. Users seek to ascribe appropriate semantics to images, though bulk-uploading and bulk-tagging may introduce bias. Between 0.5–2 of tags associated with georeferenced images analyzed describe city core areas generically, while 70 of all georeferenced images analyzed include specific place name tags, with place names at the granularity of city names being by far the most common. Using Flickr metadata, it is possible not only to describe the use of the term Downtown across the USA, but also to explore the borders of city center neighborhoods at the level of individual cities, whilst accounting for bias by the use of tag profiles. Significant advancements in various technologies such as Cyber-Physical systems (CPS), Internet of Things (IoT), Wireless Sensor Networks (WSNs), Cloud Computing, and Unmanned Aerial Vehicles (UAVs) have taken place lately. These important advancements have led to their adoption in the smart city model, which is used by many organizations for large cities around the world to significantly enhance and improve the quality of life of the inhabitants, improve the utilization of city resources, and reduce operational costs. However, in order to reach these important objectives, efficient networking and communication protocols are needed in order to provide the necessary coordination and control of the various system components. In this paper, we identify the networking characteristics and requirements of smart city applications and identify the networking protocols that can be used to support the various data traffic flows that are needed between the different components in such applications. In addition, we provide an illustration of networking architectures of selected smart city systems, which include pipeline monitoring and control, smart grid, and smart water systems. Increasing population density in urban centers demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. The utilization of information and communications technologies to achieve this objective presents an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. This paper presents a framework for the realization of smart cities through the Internet of Things (IoT). The framework encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system. This IoT vision for a smart city is applied to a noise mapping case study to illustrate a new method for existing operations that can be adapted for the enhancement and delivery of important city services. “Smart City” is a powerful paradigm that applies the most advanced communication technologies to urban environments, with the final aim of enhancing the quality of life in cities and provide a wide set of value-added services to both citizens and administration. A fundamental step towards the practical realization of the Smart City concept consists in the development of a communication infrastructure capable of collecting data from a large variety of different devices in a mostly uniform and seamless manner, according to the Internet of Things (IoT) paradigm. While the scientific and commercial interest in IoT has been constantly growing in the last years, practical experimentation of IoT systems has just begun. In this paper, we present and discuss the Padova Smart City system, an experimental realization of an urban IoT system designed within the Smart City framework and deployed in the city of Padova, Italy. We describe the system architecture and discuss the fundamental technical choices at the base of the project. Then, we analyze the data collected by the system and show how simple data processing techniques can be used to gain insights on the functioning of the monitored system, public traffic lighting in our specific case, as well as other information concerning the urban environment.