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Analysis of Key Supporting Technologies and Applications of Smart City Construction
Abstract
Smart city is an inevitable development trend in the future, which has a strong role in promoting urban development. Promoting the construction of smart city can not only improve people’s living standards and quality of life, but also effectively promote urban development. This paper first gives an overview of smart city, then briefly introduces the characteristics of smart city, and finally analyzes the key supporting technologies and applications of smart city construction, including cloud computing technology, big data technology, Internet of things technology, artificial intelligence technology and 3D Printing technology. Smart city is a new urban form. In the process of building a smart city, the core is the key supporting technology. Therefore, it is necessary to strengthen the research on the key supporting technology and combine it with the actual situation of the city to achieve effective use.
This paper focuses on the research of object detection technology in Smart Campus scene. Firstly, an object detection algorithm is constructed based on Faster R-CNN two-stage detector, which is trained on COCO2017 and VOC0712 public datasets, and the performance of the trained detector is evaluated. On this basis, the actual image data is collected from the campus scene, and divided into different groups according to the shooting angle, lighting conditions, occlusion, etc. After using the algorithm to detect, the results are analyzed systematically. In general, occlusion between objects is more likely to lead to unsatisfactory detection results. In the future, we still need to find a non-maximum suppression method which can effectively distinguish the same object repeated detection and occlusion between objects.
In the last few years, cities have become engines of wealth creation thanks to theadvent of the new information and communication [...]
Smart cities use digital technologies such as cloud computing, Internet of Things, or open data in order to overcome limitations of traditional representation and exchange of geospatial data. This concept ensures a significant increase in the use of data to establish new services that contribute to better sustainable development and monitoring of all phenomena that occur in urban areas. The use of the modern geoinformation technologies, such as sensors for collecting different geospatial and related data, requires adequate storage options for further data analysis. In this paper, we suggest the biG dAta sMart cIty maNagEment SyStem (GAMINESS) that is based on the Apache Spark big data framework. The model of the GAMINESS management system is based on the principles of the big data modeling, which differs greatly from standard databases. This approach provides the ability to store and manage huge amounts of structured, semi-structured, and unstructured data in real time. System performance is increasing to a higher level by using the process parallelization explained through the five V principles of the big data paradigm. The existing solutions based on the five V principles are focused only on the data visualization, not the data themselves. Such solutions are often limited by different storage mechanisms and by the ability to perform complex analyses on large amounts of data with expected performance. The GAMINESS management system overcomes these disadvantages by conversion of smart city data to a big data structure without limitations related to data formats or use standards. The suggested model contains two components: a geospatial component and a sensor component that are based on the CityGML and the SensorThings standards. The developed model has the ability to exchange data regardless of the used standard or the data format into proposed Apache Spark data framework schema. The verification of the proposed model is done within the case study for the part of the city of Novi Sad.
A smart city is an infrastructure to develop, deploy, and promote sustainable development to address the challenges of rapid urbanization. The implementation of a Smart City across the world is due to various applications of Internet of Things. To monitor and manage the devices, IoT have the ability with actionable information from streams of real-time data. A smart city should include innovative features to improve the quality of life of a citizen with high degree of accuracy and integration of various technologies. The things which sense, measure, interpret, connect and analyze are the instruments that reside on the IoT System which enhances the infrastructure of a Smart City. In this review paper, we discuss the need of IoT towards the improved infrastructure in the implementation of a Smart City.
Globalization, integration and liberalism are concepts that have been used since ancient history and have influenced urban governance to this day. The aim of the article is to find out, based on the historical development of globalization, Friedmann's urban concept and Sassen's global theory of cities-how world cities reflect the new challenges of 21st century globalization. In the recent past, building of the global urban network has been influenced by factors such as the growth of populism, neoliberalism, migration, the existence of exploitative centers, urbanization and changes in the demographic curve. Similar to the year 2020, also in 2021 cities must face a single global challenge posed by the Covid-19 pandemic. In this article the authors used methods of comparative analysis of global Smart Cities such as New York, London and Tokyo. The discussion section includes a summary of results of the analysis, and a design of a new general model for managing global challenges in cities is introduced. The results of the article point towards the role and influence of cultural differences of global cities and this also relates to the approach to managing the new challenges of current times. New York and London are culturally closer and also showed similar results, whereas Tokyo differs across all analyzed elements. The main result of the article are the answers to the research questions and the design of a new general model which involves various elements of globalization management and which is based on the world best practices.
The continuous growth of cities brings out various concerns for improved development and management of the multifaceted urban systems, including those of resilience and smartness. Despite the many significant efforts in the research field, both notions remain changeable, thus retaining the lack of commonly accepted conceptual and terminological frameworks. The paper’s research goals are to designate the current direct and indirect links in the conceptualizations and research trends of the resilience and smart city frameworks and to prove the potential of the conceptual convergence between them in the context of urban systems. The application of a semi-systematic literature review, including bibliometric evidence and followed by content analysis, has led to the observation that as the resilience discourse opens up to embrace other dimensions, including technology, the smart city research turns its interest to the perspective of urban protection. Therefore, both concepts share the goal for urban sustainability realized through specific capacities and processes and operationalized with the deployment of technology. The paper’s findings suggest that the conceptual and operational foundations of these two concepts could support the emergence of an integrated framework. Such a prospect acknowledges the instrumental role of the smart city approach in the pursuit of urban resilience and unfolds a new model for sustainable city management and development.
Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.
In our current study, we are aiming to explore data management methods in Smart City systems. In data management, AI (Artificial Intelligence) can be used as well. Solutions for the usage of AI and integration into Smart City concept will be researched as well.
Main motivation of the study is to draw attention to one of the most important element of Smart Cities, to the flow of data. Our study provide a possible solution for managing data and keep data up-to-date in such systems with the usage of newest technology possibilities.
While explaining the solution, we will give all the necessary details about the data flow model between the AI based system and humans who are using the Smart City. For managing the data-flow, we would like to use Big Data methods extended with other required methods. We are using the term of Big Data as a technology maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets [1] connecting with AI solutions.
Smart cities are a modern reality in an increasingly digitized and fast changing world; and, as multidimensional, multilayered and interconnected career ecosystems they bring a number of challenges for the development of sustainable careers. What are the systemic roots of these challenges, and how can we deal with them to support the emergence of sustainable careers? We draw on a sociotechnical approach, supplemented by a dynamic person–environment fit perspective, to describe two systemic challenges tied to the development of sustainable careers in smart cities, namely: (1) an unbalanced fit, in that the highly digitized context fits best with highly educated and information and communications technology (ICT) literate citizens working in knowledge intensive organizations; and (2) a volatility of fit, associated with the complex and fast-changing smart urban context. Based on the sociotechnical analysis, we put forth three suggestions for addressing these challenges and creating a sustainable career ecosystem focused on: (1) the continuous development of ICT literacy, knowledge, talents and skills; (2) citizen participation and career communities; and (3) network-centric organizing of sustainable careers that could alleviate some of the challenges associated with the parallel development of sustainable careers and smart cities.
The aim of this paper is to use remote sensing data for developing smart technology in oil cities, Basra city as study area. Smart city is one of smart technology governments need to implement in oil cities to give suitable solution for audience such as easy transportation access between cities. The planning problem in oil production areas of Iraq, which is carried out on the territories of the cities is the lack of balanced policies aimed at their development, taking into account on the one hand, areas disturbed by petroleum activities on the other areas of the existing development in the settlement in other hand. Urban use of the areas disturbed by oil production requires right science-based decisions associated with the development of quantitative methods of accounting and evaluation of territorial -construction resource (TCR) in a particular urban territory. Success smart city in Basra city needs hardware, software, remote sensing data and users, network and good understanding of how smart technology working on. This research defined the required steps and roles to develop smart city technology in Basra city as one of Iraqi oil cities territories.
Smart city is a complex systematic engineering. It not only needs planing, constructing, managing and running, but also requires evaluating, optimizing and adjusting, in order to keep the smart city construction prospective, reasonable and effective. The application of cloud computing platform can effectively improve the intellectualization of urban. According to the problems such as the blind construction and the unsatisfied results during the process of the smart city construction, we carry on the theoretical exploration about smart city evaluation system based on cloud platform and studying the indexes included in smart city and relation among them, and further propose the method of the smart city evaluation system. The core of the method is the smart city evaluation indexes system, evaluation method and optimization strategy and the feasibility of the method is verified by an example. Finally, we propose an application-oriented cloud computing platform architecture, it can improve the evaluation results and maximize the capacity of smart cities.
This study presents a systematic review of the literature of 84 articles and books (1997–2020) dealing with the concept and processes of Smart Cities worldwide. It identifies and analyses the three main perspectives: (a) the techno-centric perspective, (b) the humanist perspective and (c) the collaborative perspective and proposes a novel perspective thereof. It shows that, although there is still a lack of consensus on the subject, some of the definitions of Smart Cities, particularly the more comprehensive ones, seem to be moving in the right direction, in line with our perspective, bringing technology closer to people and at the same time integrating it into a new system of systemic, multi-dimensional, multi-actor and multi-level local governance in a network. Future studies could focus on analysing the results of the processes of transforming cities into Smart Cities and producing more quantitative work, given the predominance of qualitative case studies in this area.
3D printing our way to a greener world
- J Hornick
Career challenges in smart cities: a sociotechnical systems view on sustainable careers
- T Svenja
Discussion on remote sensing big data to promote the development of Smart City
- J Ying
- Y Jian
- Z Libin