Personalized accessible wayfinding for people with disabilities through standards and open geospatial platforms in smart cities

\n Smart City technologies emerge as a potential solution to tackle common problems in large urban centers by using city resources efficiently and providing quality services for citizens. Despite the various advances in middleware technologies to support future smart cities, there are yet no widely accepted platforms. Most of the existing solutions do not provide the required flexibility to be shared across cities. Moreover, the extensive use and development of non-open-source software leads to interoperability issues and limits the collaboration among R&D groups. Our research explores the use of a microservices architecture to address key practical challenges in smart city platforms. More specifically, we are concerned with the impact of microservices on addressing the key non-functional requirements to enable the development of smart cities such as supporting different scalability demands and providing a flexible architecture which can easily evolve over time. To this end, we are developing InterSCity, a microservice-based open source smart city platform that aims at supporting the development of sophisticated, cross- domain applications and services. Our early experience shows that microservices can be properly used as building blocks to achieve a loosely coupled, flexible architecture. Experimental results point towards the applicability of our approach in the context of smart cities since the platform can support multiple scalability demands. We expect to enable collaborative, novel smart city research, development, and deployment initiatives through the InterSCity platform. The full validation of the platform will be conducted using different smart city scenarios and workloads. Future work comprises the ongoing design and development effort on data processing services as well as more comprehensive evaluation of the proposed platform through scalability experiments.\n

The increasing urbanization results in a rising demand for smart city platforms that optimize limited resources and thus save resources and increase the quality of living at the same time. Several systems have evolved in the past to address such issues by providing platforms for collecting, sharing, and processing urban data. However, these existing platforms only partially address challenges like fostering the participation of citizens, protecting their privacy, and assuring certain levels of quality of information. In this paper, we present the architecture of SANE as an open, citizen-centric, scalable, and privacy-preserving smart city platform. SANE is intended as open platform on which citizens can contribute data but also hardware, without any central authority or control. Moreover, citizens maintain full control on their data and its usage. SANE comes with rich and distributed data analytics functionality that is intended to help citizens to answer data-related question in the context of smart cities.

Current navigation systems help urban mobility and logistics by using average traffic information, although such operations are facilitated if paths to destination are chosen taking into account current traffic flows and weather conditions. Also, personal and social data dealing with the current user status/task and citizen preferences should be taken into account by the navigation software to suggest the most appropriate paths. Real time decision support systems (DSSs) may help even better the mobile users if they integrate all the information available at urban scale to provide effectively the services required by the users and if they are accessed through an open platform, i.e., by any type of mobiles. To this aim, the paper proposes a DSS based on a semantic layer put on the top of the proprietary datasets so that the suggested paths may take into account all the city datasets. Also, an open platform based on JQMobile and Flash Builder frameworks is illustrated to support the users independently on the mobile used.

Smart cities have been a very active research area in the past 20 years, while continuously adapting to new technological advancements and keeping up with the times regarding sustainability and climate change. In this context, there have been numerous proposals to expand the scope of smart cities, focusing on resilience and sustainability, among other aspects, resulting in terms like smart sustainable cities. At the same time, there is an ongoing discussion regarding the degree in which smart cities put people at their centre. In this work, we argue toward expanding the current smart city definition by integrating the circular economy as one of its central pillars and adopting the term smart (and) circular city. We discuss the ways a smart and circular city encompasses both sustainability and smartness in an integral manner, while also being well-positioned to foster novel business activity and models and helping to place citizens at the heart of the smart city. In this sense, we also argue that previous research in smart cities and technologies, such as those related to Industry 4.0, can serve as a cornerstone to implement circular economy activities within cities, at a scale that exceeds current activities that are based on more conventional approaches. We also outline current open challenges in this domain and research questions that still need to be addressed.

Smart cities have been drawing attention of researchers as seen in recent intensive studies. In associated with this fact, this situation is expected to continue in future works. In other side, smart vehicles are an indispensable part of smart cities. Scientists have been researching vehicles and transportation in order to reach safe and comfortable mobility. Among these vehicles, cars are the first ones that affect human life. In this study, smart cars and their drivers are elaborated in behavioral aspect. Existing works have been discussed to figure out futuristic driving behavior in smart city environment. In order to understand human thought system, additional studies have been given and recommendations have been provided. As seen in the researches conducted in recent years, researchers have been tried to interpret behavior of drivers by examining data taken by smart phones and vehicle OBD output. Evaluations are conducted by result of the specified methods. In recent decade, it has been observed that these behaviors are not only estimations; but also systems mounted on vehicles learn overall driving behavior. Hence, developed systems should work online while drivers on steering wheel. Consequently, this study will enlighten existing trends for different types of learning schemes. Future studies are expected to combine car, driver's biologic, psychological, and environmental data. Thus, in the near future, systems that understand the human thought will be developed.

Abstract. One of the main concept objectives of smart cities is to create a quality living environment that is long-term sustainable and economically justified. In that context, modern cities are aware of the exposure to various forms of physical and non-physical pollution that needs to be remediated, eliminated or reduced. To achieve that it is necessary to quality determine the sources and reasons of each pollution. The most prominent examples of physical pollution that affects the quality of life of citizens in cities are light and noise pollution. Noise pollution or noise, is mostly the consequence of road and rail traffic in cities and it directly affects the health of citizens. Traffic control, reduction of peak congestion, dispersion and traffic redirection or building protective barriers, are ways that cities use to reduce the amount of noise or its effects. To make these measures efficient it is necessary to obtain the information related to the level of noise in certain areas, streets, cities. To achieve this, smart cities use noise mapping. The city of Zagreb since 2012, participates in the i-SCOPE project (interoperable Smart City services trough Open Platform for urban Ecosystems). i-SCOPE delivers an open platform on top of which it develops, three "smart city" services: optimization of energy consumption through a service for accurate assessment of solar energy potential and energy loss at building level, environmental monitoring through a real-time environmental noise mapping service leveraging citizen's involvement will who act as distributed sensors city-wide measuring noise levels through an application on their mobile phones and improved inclusion and personal mobility of aging and diversely able citizens through an accurate personal routing service. The students of Faculty of Geodesy University of Zagreb, who enrolled in the course Thematic Cartography, were actively involved in the voluntary data acquisition in order to monitor the noise in real time. In this paper are presented the voluntary acquisitioned data of noise level measurement in Zagreb through a mobile application named Noise Tube, which were used as the basis for creating the dynamic noise map. The paper describes how citizens through voluntarily collected geoinformation can directly influence decision-making in their community, which certainly affects the quality of life.

One of the main concept objectives of smart cities is to create a quality living environment that is long-term sustainable and economically justified. In that context, modern cities are aware of the exposure to various forms of physical and non-physical pollution that needs to be remediated, eliminated or reduced. To achieve that it is necessary to quality determine the sources and reasons of each pollution. The most prominent examples of physical pollution that affects the quality of life of citizens in cities are light and noise pollution. Noise pollution or noise, is mostly the consequence of road and rail traffic in cities and it directly affects the health of citizens. Traffic control, reduction of peak congestion, dispersion and traffic redirection or building protective barriers, are ways that cities use to reduce the amount of noise or its effects. To make these measures efficient it is necessary to obtain the information related to the level of noise in certain areas, streets, cities. To achieve this, smart cities use noise mapping. <br><br> The city of Zagreb since 2012, participates in the i-SCOPE project (interoperable Smart City services trough Open Platform for urban Ecosystems). i-SCOPE delivers an open platform on top of which it develops, three "smart city" services: optimization of energy consumption through a service for accurate assessment of solar energy potential and energy loss at building level, environmental monitoring through a real-time environmental noise mapping service leveraging citizen's involvement will who act as distributed sensors city-wide measuring noise levels through an application on their mobile phones and improved inclusion and personal mobility of aging and diversely able citizens through an accurate personal routing service. The students of Faculty of Geodesy University of Zagreb, who enrolled in the course Thematic Cartography, were actively involved in the voluntary data acquisition in order to monitor the noise in real time. In this paper are presented the voluntary acquisitioned data of noise level measurement in Zagreb through a mobile application named Noise Tube, which were used as the basis for creating the dynamic noise map. <br><br> The paper describes how citizens through voluntarily collected geoinformation can directly influence decision-making in their community, which certainly affects the quality of life.

Cities worldwide are rapidly integrating urban management information technology (IT) systems to address increasing urban challenges effectively. This paper examines the evolution of these systems, from the Ubiquitous City (U-City) to contemporary Smart Cities, and explores emerging technologies like the Metaverse and Digital Twin. Despite their growing relevance, there is ambiguity in their conceptual definitions and how they are applied interchangeably in urban management contexts. This study aims to clarify these technologies by analyzing U-City, Smart City, Metaverse, and Digital Twin within an Analysis Framework of Urban Management IT Systems. Our analysis reveals distinct roles and interconnections among these technologies. U-City initiated the development of essential IT infrastructure, laying the groundwork for subsequent systems. Smart City technology, leveraging the infrastructure provided by U-City, addresses real urban issues but lacks predictive capabilities for future challenges. In contrast, the Metaverse facilitates the simulation of urban changes and planning in a virtual environment, influencing real-world city development. Digital Twin technology enhances the operational efficiency of U-City and Smart City by simulating real-world data and optimizing urban management processes. The study highlights that while U-City and Smart City function in the tangible realm, Metaverse and Digital Twin operate in the virtual domain, yet they are closely integrated. Metaverse assists in urban planning through simulations that inform real-world Smart City designs, while Digital Twin optimizes real-time city operations and informs Metaverse simulations for future planning. The findings underscore the importance of understanding these technologies’ unique contributions and their interplay. Although current urban management IT systems have limitations, especially in technology classification and data accessibility, this research paves the way for future investigations. Further research should focus on refining the definitions and applications of Metaverse and Digital Twin, clarifying their relationships with Smart City and U-City, and addressing security and privacy concerns associated with these technologies. Highlights U-City, Smart City, Metaverse, and Digital Twin are urban management IT technologies U-City is the most basic infrastructure for real-world urban management IT systems Smart City as an IT solution categorizes real urban issues to resolve them Metaverse and Digital Twin predict urban issues in advance in the virtual world U-City, Smart City, Metaverse, and Digital Twin related are interconnected

ABSTRACTThe concept of smart city has emerged worldwide as a feasible answer to the challenges raised by the increasing urbanisation. From the technological point of view, guaranteeing ubiquitous connectivity, reliable communications and seamless integration of multiple network access technologies are mandatory in a smart city. This is in contrast with the current infrastructure deployment in several urban areas, which is characterised by lack of ubiquitous connectivity and coverage and by fragmentation of networks that are usually deployed by different operators and without any centralised control by the city authorities. In this paper, we look at the heterogeneity of devices and network technologies under a different perspective by not perceiving it as a limitation but as a potential to increase the connectivity in a smart city. We propose a new generation of network nodes, called stem nodes, based on the innovative idea of ‘stemness’, which pushes forward the well‐known self‐configuration and self‐management concepts towards the idea of node mutation and evolution. We also deployed prototypes that demonstrate the stem‐node architecture and basic operations in different hardware platforms of common communication devices (an Alix‐based router, a laptop and a smartphone). Copyright © 2014 John Wiley & Sons, Ltd.

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

Internet of Things (IoT) paradigm is making it possible for everyday objects to integrate with the Internet. This has laid the foundations for the inception of future Smart cities based on systems that consist of a plethora of IoT devices to enable novel applications. The smart cities paradigm deals with the public data that is prone to different security and privacy risks at different level of smart cities architecture. Therefore, the importance of ensuring security and privacy is paramount in this paradigm. This paper focuses on the security and privacy issues involved in the smart cities. The paper highlights the key applications of smart cities and then investigates its architecture from security point of view. The paper also reviews the current security and privacy solutions for smart cities and emphasizes the open issues and research challenges that still need to be addressed in this new paradigm.

In recent years, Cloud Computing and Internet of Things (IoT) have been rapidly advancing as the two fundamental technologies of the Future Internet (FI) concept. Different IoT systems are designed and implemented according to the IoT domain requirements, thus not taking into consideration issues of openness, scalability, interoperability, and use case independence. This work focuses on the presentation of a framework that integrates future IoT systems in smart cities by utilizing state-of-the-art architectures, technologies, solutions, and services developed by the IoT-A and FIWARE FP7 projects of the EU. We expect that in future smart city environments, an IoT infrastructure will act as a key enabler for the revolution of smart networked systems with embedded devices. Also, the proposed solution overcomes the fragmentation of vertically oriented closed systems, architectures, and application areas and move towards open systems and platforms that support multiple applications. This is a key requirement for smart city infrastructures that can be reused by a plethora of applications in various domains, such as transportation systems, energy, waste management, environmental monitoring, buildings, etc. The proposed system will encompass FIWARE and IoT-A to develop innovative IoT platforms and services and it will include generic IoT devices that are independent of connectivity modes and are not coupled to specific IoT protocols. It will further supply interoperability with emerging connectivity protocols based on actions regarding standardization and requirements. We expect that future solutions will simplify data transfer by supporting the vast majority of transfer protocols and will allow effective utilization of network capabilities for transition and reception of real-time data. Using FIWARE services will ensure reliability, modularity, and uniform APIs independent of the underlying hardware and it will move beyond current solutions that are platform dependent, and vendor specific. The result will be a dynamic configurable infrastructure, scalable, interoperable, heterogeneous, and secure that could also seamlessly integrate other existing and future platforms and devices. Information can flow among IoT systems in a secure and privacy-preserving way, allowing for extracting context for developing cross-domain applications and breaking the domain silos of today’s IoT world.

One positive impact of smart cities is reducing energy consumption and CO2 emission through the use of information and communication technologies (ICT). Energy transition pursues systematic changes to the low-carbon society, and it can benefit from technological and institutional advancement in smart cities. The integration of the energy transition to smart city development has not been thoroughly studied yet. The purpose of this study is to find empirical evidence of smart cities’ contributions to energy transition. The hypothesis is that there is a significant difference between smart and non-smart cities in the performance of energy transition. The Smart Energy Transition Index is introduced. Index is useful to summarize the smart city component’s contribution to energy transition and to enable comparison among cities. The cities in South Korea are divided into three groups: (1) first-wave smart cities that focus on smart transportation and security services; (2) second-wave smart cities that provide comprehensive urban services; and (3) non-smart cities. The results showed that second-wave smart cities scored higher than first-wave and non-smart cities, and there is a statistically significant difference among city groups. This confirms the hypothesis of this paper that smart city development can contribute to the energy transition.

In July 2021, the World Economic Forum, in collaboration with the G20 Global Smart Cities Alliance, presented a study on the status of technology governance in cities, outlining the fundamental conditions that all smart cities, regardless of their strategic aims, must achieve. A decision support system based on a model that enables interoperability across multiple urban sectors should ensure these requirements, which serve as the foundation for effective technological governance. To accomplish this, many European cities have based their urban strategies on smart specialisation on open web platforms that systematise different sectors to create smart cities. From these premises, the purpose of this paper is threefold: (i) investigate the relationship between smart urban development and the use of open data platforms; (ii) understand how these are useful for defining actions and strategies that facilitate the planning of a smart city, and (iii) understand, if it is possible, to find platform’s common characteristics that allow cooperation of intentions between European Union cities. To this goal, the authors make a systematic and cross-reading of some European platforms’ good practices in smart city solutions and projects, highlighting the scope of the smart city and sustainability/financing, the open data exploitation and the technical characteristics. In this regard, an analytical approach is proposed, underlining the originality and value of this research to strengthen the smart Governance model based on open web platforms.KeywordsOpen data platformsUrban strategiesSmart specialization platforms; Smart cityDecision support systemTechnological governance

Deep learning has increasingly become an essential component of many Smart City functions including smart city lightings, emergency rescues, smart drainage, and smart parking. These functions operate continuously in real-time throughout the day. Thus, excessive energy usage of deep learning computation can negatively impact economic benefits and efficiency of smart cities. The situation can escalate when dealing with resource-constrained large-scale smart cities of huge Internet-of-Things and networks with large numbers and varieties of sensors. To effectively sustain, manage and protect smart cities from failures due to energy overload, the awareness of energy consumption by deep learning computation is unavoidably necessary. Most recent research in smart cities focuses on using deep learning to perform certain tasks but does not address energy issues. This paper presents a formal approach to estimating energy consumption of deep learning and illustrates its use in smart cities. In particular, we develop a fine-grained mathematical model that extends an existing model to include the quantification of MAC (multiply-and-accumulate) operations as well as data access from a memory hierarchy. This paper focuses on deep and convolutional neural networks. We describe the proposed approach and validate the results obtained from our model by comparing them against those of existing work. The proposed approach is applied to three (deep) neural systems in smart cities, namely smart drainage, smart transportation and smart parking systems, all of which yield promising results.