Smart City Framework Research Articles

An evaluation system based on the self-organizing system framework of smart cities: A case study of smart transportation systems in China

Smart cities have been developing aggressively around the globe, especially in China. As of 2017, more than 1000 cities in the world have commenced smart city developments, half of which are in China. However, there is not a uniform and clear understanding of smart city systems; this may affect their evaluation/planning and lead to misguided construction. Smart cities are characterized by complex self-organizing systems, and thus their sustainable and healthy development may require following the evolutionary patterns of such systems. Hence, self-organizing system theory may be useful in explaining such cities. Therefore, this paper first performs secondary qualitative data analyses of previous attempts at characterizing smart city development to identify the most robust aspects tested in previous work. Then, incorporating the results, self-organization theory is used to develop an overall, comprehensive system framework of smart cities. In this framework, all types of smart devices are the basic units of development (defined as “smart cells”). Meanwhile, Information and Communications Technology (ICT) and developmental mechanisms offer technical support and regulatory mechanisms in the spontaneous operation of smart cities. These three dimensions (smart cells, ICT, and developmental mechanisms) are combined into a scalable and distributed smart city evaluation system. Finally, smart transportation systems and the current state of these in China are used as a case study for discussion.

Redefining the Smart City: Culture, Metabolism and Governance

The Smart City concept is still evolving and can be viewed as a branding exercise by big corporations, which is why the concept is not being used by the United Nations (U.N.). Smart Cities tend to represent the information, communication, and technological (ICT) industry alone without considering the values and cultural and historical profiles that some cities hold as legacies. However, the technology inherent in Smart Cities promises efficiencies and options that could allow cities to be more “inclusive, safe, resilient, and sustainable” as required by the U.N. agenda including cultural heritage. There is a notable lack of Smart City application to cultural and historical urban fabrics. Instead, the modernist new town approach has emerged under this new rubric leading to many problems such as urban decay and unsustainable car dependence. This study therefore presents a review of the literature on the nature, challenges, and opportunities of Smart Cities. A new Smart Cities framework is proposed based on the dimensions of culture, metabolism, and governance. These findings seek to inform policy makers of an alternative viewpoint on the Smart City paradigm, which focuses on urban outcomes rather than technology in isolation.

Navigating platform urbanism

The smart city imaginary has become a recurring theme within critical urban geography and entails a distinct set of rationalities. Here, we are interested in grappling with the current ‘place’ of smart cities in the context of what seems to be an emerging platform urbanism, thereby highlighting a complex platform-based ecosystem encompassing private and public organisations and citizens. Our point of departure is the operationalization of three intertwined trends associated with the conceptualizations of participation, mediatisation and (multi-sided) platformisation. Through the examination of (social) traffic and navigation application, Waze, we explore manifestations of (contested) dynamics in mobility practices occurring between commerce and community in the public space of the city. The preliminary findings point to the emergence of new socio-spatial constructs which afford a better frame of ‘what is going on’, challenging the smart city framework as a planning and development paradigm. In putting forward the notion of public value and ownership, it is our aim to prompt a critical debate about platform urbanism made explicit by a driving politics that offers a window to a future driving world, urging cities and governments to anticipate and mitigate (un)intended consequences.

Economic and policy uncertainty in climate change mitigation: The London Smart City case scenario

Despite the overwhelming consensus within the scientific community concerning the causes and effects of climate change, decision-making processes often do not point out in the same direction. In order to effectively and satisfactorily tackle climate change, a legally and politically binding long-term policy architecture is needed. In practice, however, central governments and international policymakers have been unable to provide a successful policy architecture. Yet, city-level initiatives within the Smart City framework are a promising way to tackle climate change. An example of such a Smart City framework is the London Environment Strategy (LES). In this paper, we propose a zero mean reverting model for greenhouse gas emissions to quantitatively analyze its consistency with the 2050 Zero Carbon objectives. We consider different policy scenarios proposed in the LES and the forward-looking policy uncertainty embedded in different economic sectors, primarily domestic, industrial and commercial and transport. We find that, on average, only transport improves the historical greenhouse gas emissions trend, and most of this reduction comes from Smart Mobility and/or Smart Regulation programs focusing on the environment.

A novel big data analytics framework for smart cities

The emergence of smart cities aims at mitigating the challenges raised due to the continuous urbanization development and increasing population density in cities. To face these challenges, governments and decision makers undertake smart city projects targeting sustainable economic growth and better quality of life for both inhabitants and visitors. Information and Communication Technology (ICT) is a key enabling technology for city smartening. However, ICT artifacts and applications yield massive volumes of data known as big data. Extracting insights and hidden correlations from big data is a growing trend in information systems to provide better services to citizens and support the decision making processes. However, to extract valuable insights for developing city level smart information services, the generated datasets from various city domains need to be integrated and analyzed. This process usually referred to as big data analytics or big data value chain. Surveying the literature reveals an increasing interest in harnessing big data analytics applications in general and in the area of smart cities in particular. Yet, comprehensive discussions on the essential characteristics of big data analytics frameworks fitting smart cities requirements are still needed. This paper presents a novel big data analytics framework for smart cities called “Smart City Data Analytics Panel — SCDAP”. The design of SCDAP is based on answering the following research questions: what are the characteristics of big data analytics frameworks applied in smart cities in literature and what are the essential design principles that should guide the design of big data analytics frameworks have to serve smart cities purposes? In answering these questions, we adopted a systematic literature review on big data analytics frameworks in smart cities. The proposed framework introduces new functionalities to big data analytics frameworks represented in data model management and aggregation. The value of the proposed framework is discussed in comparison to traditional knowledge discovery approaches.

Mobile health applications during epidemic management in India: a review

INTRODUCTION: Smart cities endeavour to provide a good quality of life to its inhabitants. The COVID-19 pandemic necessitates redrawing the framework of epidemic management in India. Information, Communication & Technology (ICT) solutions such as mobile health (mHealth) can complement this. OBJECTIVES: To review ICT and mHealth used for epidemic management in smart cities of India. METHODS: A systematic review was conducted to identify the use of ICT or mHealth applications for epidemic management in smart cities. A predefined search strategy and a predefined eligibility criterion to search for articles published in English on Medline were used. RESULTS: Our study showed ICT and mHealth use has increased during the recent COVID-19 pandemic in India and available solutions can be applied in smart city framework to improve epidemic management and achieve mHealth targets. CONCLUSION: We conclude that there have been many advances in the provisions of ICT and mHealth interventions in India in context to smart cities and scope for improvements abound.

Intelligent Transportation System for Smart-Cities using Fuzzy Logic

According to United Nations population statistics 2017, the world population is 7.6 billion and is growing rapidily alomost 11 billion by end of 21 century with a 70% chance of continued growth, this rapid increasing population have created low standards of living in cities. Smart Cities are facing pressures associated with due innovations and globalization to improve their citizens life. Computational intelligence is the study of adaptive mechanism to facilitate intelligent behavior in changing and complex environments. Traffic congestion and monitoring has become one of the critical issues in big cities. The adaptive mechanism of computational intelligence in changing the behavior of complex environments of smart city is very effective. The developing framework and services for smart-city requires sound infrastructure, latest current technology adoption. A framework model with the integration of cloud-data, social network (SN) services that is collecting stream data with smart sensors in the context of smart cities is proposed. The adaptive mechanism of computational intelligence in changing thebehavior of complex environments of smart city is very effective. A radical framework that enables the analysis of big-data sets stemming from Social Networking (SN) sites. Smart cities understanding is a broad concept only city transportation sector is focused in this article. Fuzzy logic modeling techniques are used in many fields i.e. medical, engineering. business and computing related problems. To solve various traffic management issues in cities a detailed analysis of fuzzy logic system is proposed. This paper presents an analysis of the results achieved using Fuzzy Logic System (FLS) for smart cities. The results are verified using MATLAB Simulation.

A Management Decision Support Model of Smart Campus Based on Big Data Center

Smart campus is an important part of smart city. The modeling ideas of smart campus are discussed firstly. A management decision support model of smart campus is then put forward. It connects with other complex information systems through education data center in the framework of smart city. Based on a big data center and a decision support system for smart education management, it provides decision support for campus operation status monitoring, discipline construction, major settings, faculty and staff management, talents cultivation, teaching evaluation, scientific and technological innovation, financial management, resources utilization, and service guarantee. Finally, the composition of the big data center is also discussed which contains at least thirteen basic databases.

Privacy Breach: A Concern of Cloud Integrated IoT Framework for Smart City

A network of billions of devices connected over the Internet is known as Internet of Things (IoT). These IoT devices serve many applications and one such application is smart city. Smart city requires huge amount of data and provide services to large number of users. This necessitates integration of cloud with IoT. One of the services of cloud integrated IoT is device registration and identification. Personal information of clients and devices is registered to cloud so as to authorize them for other services. User’s privacy is compromised when user’s personal data is shared through cloud to other organisations for business purpose. This paper discusses the problems of privacy breach and states a solution of reducing its chances by using fog nodes at the edge of cloud integrated IoT framework.

Understanding ‘smart cities’: Intertwining development drivers with desired outcomes in a multidimensional framework

The convergence of technology and the city is commonly referred to as the ‘smart city’. It is seen as a possible remedy for the challenges that urbanisation creates in the age of global climate change, and as an enabler of a sustainable and liveable urban future. A review of the abundant but fragmented literature on smart city theories and practices, nevertheless, reveals that there is a limited effort to capture a comprehensive understanding on how the complex and multidimensional nature of the drivers of smart cities are linked to desired outcomes. The paper aims to develop a clearer understanding on this new city model by identifying and linking the key drivers to desired outcomes, and then intertwining them in a multidimensional framework. The methodological approach of this research includes a systematic review of the literature on smart cities, focusing on those aimed at conceptual development and provide empirical evidence base. The review identifies that the literature reveals three types of drivers of smart cities—community, technology, policy—which are linked to five desired outcomes—productivity, sustainability, accessibility, wellbeing, liveability, governance. These drivers and outcomes altogether assemble a smart city framework, where each of them represents a distinctive dimension of the smart cities notion. This paper helps in expanding our understanding beyond a monocentric technology focus of the current common smart city practice.

The SELFIE façade system. From Smart Buildings to Smart grid

The aim of the SELFIE (Smart and Efficient Layers for Innovative Envelope) project was to develop novel adaptive envelope systems for nZEBs, within the framework of a smart city, in order to facilitate the exploitation of RES at building scale and simultaneously decrease the energy consumptions and improve indoor environmental quality in non-residential buildings. The process of designing innovative technological systems, capable of integrating smart materials and novel technologies, into the new construction and/or renovation of buildings, will be described in this paper. The objective is demonstrated how it is possible transferring know-how from different production sectors aimed to reduction of environmental impact of the built environment of our cities.

Adaptive and Cost-Effective Collection of High-Quality Data for Critical Infrastructure and Emergency Management in Smart Cities—Framework and Challenges

Adaptive and Cost-Effective Collection of High-Quality Data for Critical Infrastructure and Emergency Management in Smart Cities—Framework and Challenges

Challenges in Governing the Digital Transportation Ecosystem in Jakarta: A Research Direction in Smart City Frameworks

Mobility is one of the most difficult domains of the smart city to face. In fact, most large cities in the world are still facing urban mobility problems, especially traffic congestion. Particularly, in Jakarta, Indonesia, traffic congestion is a major issue that negatively affects productivity and the overall living quality of the citizens. Along with the development of the information communication and technology (ICT), the transportation domain in Jakarta has formed a digital transportation ecosystem, shown by the emergence of innovative digital-based transportation services. In line with this current condition, this paper hopes to contribute to the improvement of urban traffic in Jakarta by proposing research directions to govern the digital transportation ecosystem within a smart city framework. The significance of the research directions is reviewed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology in a systematic review of previous studies. Ultimately, the research directions proposed in this paper lead to the necessity for an architectural perspective and relevant big data analytical tools to improve the digital transportation ecosystem in Jakarta.

An experimental framework for future smart cities using data fusion and software defined systems: The case of environmental monitoring for smart healthcare

With the evolution of the concept of Internet into the Internet-of-Things (IoT), in which sensors, devices, and everyday objects (things), are interconnected through the Internet, a large number of interconnected things are expected to produce massive amounts of data, which needs efficient processing in order to provide various services. IoT is of little worth without the ability to process, manage, and control a large amount of heterogeneous data in a timely and efficient manner. The concept of Data Fusion (DF) is based on combining heterogeneous multi-source data in order to produce high quality information, and reduce network traffic. On the other hand, the highly dynamic and heterogeneous nature of IoT requires very flexible and efficient management. The recent software defined systems paradigm is an appealing solution to reduce complexity and overhead in managing complex systems such as IoT by separating the control plane from the data plane. In this work, we present a novel experimental framework for IoT based environmental monitoring applications using concepts from DF and software defined systems (SDS). It is built on top of the software defined networking platform where the core components (the host, switch and the controller) are expanded to support other software defined systems components (such as software defined storage and security) and enable the applications of different DF techniques in IoT environments. We conduct several experiments to highlight features of the presented framework.

Citizen-centered big data analysis-driven governance intelligence framework for smart cities

Sensors and systems within rapidly expanding smart cities produce citizen-centered big data which have potential value to support citizen-centered urban governance decision-making. There exists a wealth of extant conceptual studies, however, further operational studies are needed to establish a specific path towards implementation of such data to governance decision-making with analytical algorithms that are appropriate for each step of the path. This paper proposes a framework for the use of citizen-centered big data analysis to drive governance intelligence in smart cities from two perspectives: urban governance issues and data-analysis algorithms. The framework consists of three layers: 1) A data-merging layer, which builds a citizen-centered panoramic data set for each citizen by merging citizen-related big data from multiple sources in collaborative urban governance via similarity calculation and conflict resolution; 2) a knowledge-discovery layer, which plots the citizen profile and citizen persona at both individual and group levels in terms of urban public service delivery and citizen participation via simple statistical analysis techniques, machine learning, and econometrics methods; and 3) a decision-making layer, which uses ontology models to standardize urban governance-related attributes, personas, and associations to support governance decision-making via data mining and Bayesian Net techniques. Finally, the proposed framework is validated in a case study on blood donation governance in China. This research highlights the value of citizen-centered big data, pushes data-to-decision research from conceptual to operational, synthesizes previously published frameworks for citizen-centered big data analysis in smart cities, and enhances the mutual supplement cross multiple disciplinaries.

Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges

The development of smart cities and their fast-paced deployment is resulting in the generation of large quantities of data at unprecedented rates. Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards that benefit from the availability of such data. Moreover, the high dynamical nature of smart cities calls for new generation of machine learning approaches that are flexible and adaptable to cope with the dynamicity of data to perform analytics and learn from real-time data. In this article, we shed the light on the challenge of under utilizing the big data generated by smart cities from a machine learning perspective. Especially, we present the phenomenon of wasting unlabeled data. We argue that semi-supervision is a must for smart city to address this challenge. We also propose a three-level learning framework for smart cities that matches the hierarchical nature of big data generated by smart cities with a goal of providing different levels of knowledge abstractions. The proposed framework is scalable to meet the needs of smart city services. Fundamentally, the framework benefits from semi-supervised deep reinforcement learning where a small amount of data that has users' feedback serves as labeled data while a larger amount is without such users' feedback serves as unlabeled data. This paper also explores how deep reinforcement learning and its shift toward semi-supervision can handle the cognitive side of smart city services and improve their performance by providing several use cases spanning the different domains of smart cities. We also highlight several challenges as well as promising future research directions for incorporating machine learning and high-level intelligence into smart city services.

Communication Improvement and Traffic Control Based on V2I in Smart City Framework

This article describes how soft computing techniques are tolerant of imprecision, intended on approximation, focus on uncertainty and are based on partial truth. Current real-world problems pertaining to congested traffic is pervasively imprecise and therefore design of smart traffic control system is a challenging issue. Due to the increasing rate of vehicles at traffic points in smart cities, it creates unexpected delays during transit, chances of accidents are higher, unnecessary fuel consumption is an issue, and unhygienic environment due to pollution also degrades the health condition of general people in a normal city scenario. To avoid such problems many smart cities are currently implementing improved traffic control systems that work on the principle of traffic automation to prevent these issues. The basic challenge lies in the usage of real-time analytics performed with online traffic information and correctly applying it to some traffic flow. In this research article, an enhanced traffic management system called SCICS (Soft Computing based Intelligent Communication System) has been proposed which uses swarm intelligence as a soft computing technique with intelligent communication between smart vehicles and traffic points using the vehicle to infrastructure (V2I) concept of VANET. It uses an improved route diversion mechanism with implemented logic in nanorobots. Under a vehicular ad-hoc network (VANET) scenario, the communication between intelligent vehicles and infrastructure points takes place through nanorobots in a collaborative way. Simulation carried out using Ns2 simulator shows encouraging results in terms of better performance to control the traffic.

Morphology-based visible-infrared image fusion framework for smart city

Morphology-based visible-infrared image fusion framework for smart city

A novel visible-infrared image fusion framework for smart city

A novel visible-infrared image fusion framework for smart city

Morphology-based visible-infrared image fusion framework for smart city

Sparse representation-based approaches are often applied to image fusion. Owing to the difficulties of obtaining a complete and non-redundant dictionary, this paper proposes a hierarchical image fusion framework that applies layer-by-layer deep learning techniques to explore the detailed information of images and extract key information of images for dictionary learning. According to morphological similarities, this paper clusters source image patches into smooth, stochastic, and dominant orientation patch group. High-frequency and low-frequency components of three clustered image-patch groups are fused by max-L1 and L2-norm based weighted average fusion rule respectively. The fused low-frequency and high-frequency components are combined to obtain the final fusion results. The comparison experimentations confirm the feasibility and effectiveness of the proposed image fusion solution.