Smart City Research Research Articles

Who Uses Smart City Services and What to Make of It: Toward Interdisciplinary Smart Cities Research

As research on smart cities garners increased attention and its status consolidates as one of the fanciest areas of research today, this paper makes a case for a cautious rethink of the very rationale and relevance of the debate. To this end, this paper looks at the smart cities debate from the perspectives of, on the one hand, citizens’ awareness of applications and solutions that are considered ‘smart’ and, on the other hand, their ability to use these applications and solutions. Drawing from a detailed analysis of the outcomes of a pilot international study, this paper showcases that even the most educated users of smart city services, i.e., those arguably most aware of and equipped with skills to use these services effectively, express very serious concerns regarding the utility, safety, accessibility and efficiency of those services. This suggests that more pragmatism needs to be included in smart cities research if its findings are to remain useful and relevant for all stakeholders involved. The discussion in this paper contributes to the smart cities debate in three ways. First, it adds empirical support to the thesis of ‘normative bias’ of smart cities research. Second, it suggests ways of bypassing it, thereby opening a debate on the preconditions of sustainable interdisciplinary smart cities research. Third, it points to new avenues of research.

Rescaling and refocusing smart cities research: from mega cities to smart villages

PurposeThe purpose of this paper is to rethink the focus of the smart cities debate and to open it to policymaking and strategy considerations. To this end, the origins of what is termed normative bias in smart cities research are identified and a case made for a holistic, scalable and human-centred smart cities research agenda. Applicable across the micro, mezzo and macro levels of the context in which smart cities develop, this research agenda remains sensitive to the limitations and enablers inherent in these contexts. Policymaking and strategy consideration are incorporated in the agenda this paper advances, thus creating the prospect of bridging the normative and the empirical in smart cities research.Design/methodology/approachThis paper queries the smart cities debate and, by reference to megacities research, argues that the smart city remains an overly normatively laden concept frequently discussed in separation from the broader socio-political and economic contexts in which it is embedded. By focusing on what is termed the normative bias of smart cities research, this paper introduces the nested clusters model. By advocating the inclusion of policymaking and strategy considerations in the smart cities debate, a case is made for a holistic, scalable and human-centred smart cities agenda focused, on the one hand, on individuals and citizens inhabiting smart cities and, on the other hand, on interdependencies that unfold between a given smart city and the context in which it is embedded.FindingsThis paper delineates the research focus and scope of the megacities and smart cities debates respectively. It locates the origins of normative bias inherent in smart cities research and, by making a case for holistic, scalable and human-centred smart cities research, suggests ways of bypassing that bias. It is argued that smart cities research has the potential of contributing to research on megacities (smart megacities and clusters), cities (smart cities) and villages (smart villages). The notions of policymaking and strategy, and ultimately of governance, are brought into the spotlight. Against this backdrop, it is argued that smart cities research needs to be based on real tangible experiences of individuals inhabiting rural and urban space and that it also needs to mirror and feed into policy-design and policymaking processes.Research limitations/implicationsThe paper stresses the need to explore the question of how the specific contexts in which cities/urban areas are located influence those cities/urban areas’ growth and development strategies. It also postulates new avenues of inter and multidisciplinary research geared toward building bridges between the normative and the empirical in the smart cities debate. More research is needed to advance these imperatives at the micro, mezzo and macro levels.Practical implicationsBy highlighting the connection, relatively under-represented in the literature, between the normative and the empirical in smart cities research, this paper encourages a more structured debate between academia and policymakers focused on the sustainable development of cities/urban areas. In doing so, it also advocates policies and strategies conducive to strengthening individuals’/citizens’ ability to benefit from and contribute to smart cities development, thereby making them sustainable.Social implicationsThis paper makes a case for pragmatic and demand-driven smart cities research, i.e. based on the frequently very basic needs of individuals and citizens inhabiting not only urban but also rural areas. It highlights the role of basic infrastructure as the key enabler/inhibitor of information and communication technology-enhanced services. The nested clusters model introduced in this paper suggests that an intimate connection exists between individuals’ well-being, their active civic engagement and smart cities sustainability.Originality/valueThis paper delineates the relationship between megacities and smart cities research. It identifies the sources of what is termed normative bias in smart cities research. To address the implications of that bias, a nested clusters model for smart cities is introduced, i.e. a conceptual framework that allows us to redraw the debate on smart cities and establish a functional connection between the array of normatively laden ideas of what a smart city could be and what is feasible, and under which conditions at the policymaking level.

A bibliometric perspective of learning analytics research landscape

ABSTRACTLearning analytics is an emerging field of research, motivated by the wide spectrum of the available educational information that can be analysed to provide a data-driven decision about various learning problems. This study intends to examine the research landscape of learning analytics to deliver a comprehensive understanding of the research activities in this multidisciplinary field, using scientific literature from the Scopus database. An array of state-of-the-art bibliometric indices is deployed on 2811 procured publication datasets: publication counts, citation counts, co-authorship patterns, citation networks and term co-occurrence. The results indicate that the field of learning analytics appears to have been instantiated around 2011; thus, before this time period no significant research activity can be observed. The temporal evolution indicates that the terms ‘students’, ‘teachers’, ‘higher education institutions’ and ‘learning process’ appear to be the major components of the field. More recent trends in the field are the tools that tap into Big Data analytics and data mining techniques for more rational data-driven decision-making services. A future direction research depicts a need to integrate learning analytics research with multidisciplinary smart education and smart library services. The vision towards smart city research requires a meta-level of smart learning analytics value integration and policy-making.

Functionality between the size and indicators of smart cities: A research challenge with policy implications

The paper focusses on the concept of a smart city and its specific components in relation to size of the city. Smart cities are a topic whose key importance is being increasingly recognised across both academic disciplines and urban planning. The idea of a smart city is a dream of urban planners all over the world, and a subject of many research and business initiatives as well as policy debates. As cities vary considerably in size, it is important to ask if the size influences the level of selected indicators of smart cities. Our main presumption is that the development level of indicators of smart cities varies in cities of different size. Our scientific objective is to find a simple understandable model linking the categorical variable “city size” to a group of smart city indicators. Our data set contains 26 smart city indicators for 158 European smart cities, divided into two sizes: medium-sized cities and larger cities. We draw from the methodology of “European Smart Cities” elaborated by the Vienna University of Technology (Project ID: 314704) that classify European smart cities and smart city indicators by considering their size.Analysing the statistics by using decision tree modelling, we identify the most significant indicators of smart cities that can divide smart cities into size categories with impressive 96.2% correct classification. Besides excellent classification result based on real empirical data, several research results overturn common assumptions about smart cities.Based on the research results the paper also highlights intriguing future challenges in smart city research and policy development. Several research results have policy implications and might be useful for urban planners, policy representatives and decision makers.

Soft Sensing in Smart Cities: Handling 3Vs Using Recommender Systems, Machine Intelligence, and Data Analytics

Today's existing smart city research involves many overtly futuristic applications such as smart transportation, in which smart roads warn drivers of bad traffic conditions ahead, smart parking, which communicates the location of unoccupied parking spaces to drivers, and smart environment, which enables fully automated homes and workplaces to adjust their temperature to conserve energy. The realization of these applications hinges on a data acquisition structure that gathers its data from a countless number of sensors, either deployed for predefined tasks (hard sensing) or built into the mobile devices of smart city residents (soft sensing). At the core of this big data infrastructure lie the 5Vs: veracity, volume, velocity, variety, and value. The soft sensing component of a smart city sensing network is particularly affected by 3Vs: veracity, volume, and velocity. To address the unique challenges of big data, recommender systems, statistical reputation systems, and context analysis are used to ensure the veracity of acquired data, machine learning algorithms are applied to handle the data volume, and data analytics algorithms are implemented to manage data velocity. Despite its seemingly insurmountable size, the acquired data is highly redundant, and systematic use of machine intelligence and data analytics can facilitate processing by extracting only the relevant information; in this article, we study the role of these algorithms through the lens of the 3Vs in facilitating soft sensing within the framework of smart city applications.

Highly Reliable Decision‐Making Using Reliability Factor Feedback for Factory Condition Monitoring via WSNs

Cooperation among sensors in a wireless sensor network, deployed for industrial monitoring in an indoor scenario, is a topic of interest in the smart factory and smart city research. The indoor wireless communication channel is very harsh and the observations of all the sensors cannot be sent reliably to the base station. Failure to transmit correct sensing results to the base station may result in false alarms or missed detection of events. Therefore, we propose a cooperation scheme for the wireless sensors to send the data reliably to the base station. Our aim is to increase the reliability of the received information, reduce the probability of error, lower the overall power consumption, and keep the latency to an acceptable low level. We propose a reliability factor feedback algorithm to adjust the weight of unreliable sensors in the decision‐making process. The proposed scheme is analyzed based on its latency, power consumption, and packet delivery ratio. Our results show significant improvement in the reliability of the received data, improved packet delivery, and reduced false alarm ratio for full repetition and cluster head‐based cooperation. The power consumption and latency in data transmission are also kept to an acceptable low level.

On the Use of Information and Infrastructure Technologies for the Smart City Research in Europe: A Survey

This work was funded in part by the European Union’s Horizon 2020 Programme of the FESTIVAL project (Federated Interoperable Smart ICT Services Development and Testing Platforms) under grant agreement 643275, and from the Japanese National Institute of Information and Communications Technology.

Future Urban Mobility Group: Smart Cities Research Institute Swinburne University of Technology, Melbourne Australia [ITS Research Lab

Reports on Swinburne University of Technology's Future Urban Mobility [http://www.swin burne.edu.au/research/ourresearch/ institutes/smart-cities/] This group undertakes research into the development and evaluation of new solutions and policy pathways with the likelihood of greatest impact in achieving sustainable urban mobility. The work focuses on making cities accessible to their resident populations through connecting the social, physical, economic, and information infrastructures. Research is primarily industry focused and recognises the role of digital innovations and disruptive technologies in addressing the modern-day demands of urban living in the world’s large and fast growing cities. The Group comprises a multidisciplinary research team that specialises in intelligent transport systems, disruptive mobility,

Development of a multi-purpose infrastructure for sustainable mobility. A case study in a smart cities application

Research in smart cities is characterised by different needs: decrease of polluting emissions, improvement of energy efficiency, optimization of production and consumption of energy are the main action required. According to the last European directives, alternative fuels can play a fundamental role in the future of transportation [1]. In the framework of an Italian research project, called i-NEXT (innovation for greeN Energy and eXchange in Trasportation), CNR-ITAE has developed a microgrid able to receive, as input, the solar energy and to deliver, as output, hydrogen and electricity for electric and hydrogen vehicles [2-6]. A 100 kW photovoltaic plant, installed on the roof of a shed for vehicles recovering, represents the energy source. An electrochemical energy storage of 100 kW and 300 kWh, equipped with 16 sodium nickel chloride high temperature batteries, balances the energy production from PV plant in order to guarantee the energy self-sufficiency of the whole system. Electricity produced supplies both an on-site hydrogen production (by electrolysis), compression and distribution system and an electric charging station needed for Electric Vehicles (EV), Fuel Cell Electric Vehicle (FCEV) and Fuel Cell Hybrid Electric Vehicle (FCHEV). The hydrogen produced supplies two FCE bikes and a FCHE minibus developed in the framework of the project while a last mile delivery van (EV) is fed through a fast charging station. An ICT platform allows to monitor, control and connect plants and vehicles in order to manage energy flows, fault and warning events. The paper reports the results of the research activities regarding both infrastructures and vehicles development.

Wherefore is Soft Computing Useful? Smart City Research Based Reflexions

Im Sinne der Wirtschaftsinformatik, welche sich zunehmend auch mit der Stadtforschung beschaftigt, wird in diesem Artikel zuerst auf die Systemtheorie sowie die gestaltungsorientierte Forschung als Wegbereiterin von Smart Cities eingegangen. Dabei wird das transdisziplinare Forschungsfeld der Smart City, also die Anwendung technischer, wirtschaftlicher und gesellschaftlicher Innovationen auf die Stadt, aufgespannt. Danach wird der Fokus auf Soft Computing gelegt, welches gestaltungsorientierte Prozesse der transdisziplinaren Stadtforschung unterstutzt und holistische Innovationen in der Smart City ermoglicht. Anhand von ontologischem Design werden, begleitet von einem Veranschaulichungsbeispiel, das Potenzial dieser Art der Forschung aufgezeigt und deren Methoden vorgestellt, bevor ein Fazit gezogen und ein Ausblick gewagt wird.

Phase preserving profile generation from measurement data by clustering and performance analysis: a tool for network planning and operation

The need for improved operational efficiency planning accuracy leads to a growing number of sensors and other monitoring sources in our power system. New methods for properly dealing with this increasing amount of data are required. This paper presents how clustering can help to drastically reduce the processing time of energy data time series. The developed approach categorizes similar load behavior by means of agglomerative hierarchical clustering based on their correlation coefficient. It includes the determination of the best number of clusters to model different load patterns with respect to the total error given as a key performance indicator. The results are a reduced set of representative three phase load profiles based on the data input and clustering configurations. The accuracy of these representative profiles is validated by resembling the original data set. Dependent on available computational resources a network operator can use this to intelligently compress measurement data while keeping the required accuracy. The method is demonstrated on data from the testbed of Aspern Smart City Research in Seestadt Aspern, Austria.

Entrepreneurial Ecosystem Momentum and Maturity the Important Role of Entrepreneur Development Organizations and Their Activities

Entrepreneurial ecosystems are becoming recognized as a way to stimulate economic growth, innovation, and social change. Their implementation is gaining momentum across the United States and other parts of the world as their benefits are recognized. Nations, cities, regions, universities, and others are collaborating to put in place entrepreneurial ecosystems as a critical component of their innovation strategies seeking to improve economies, societies, and institutions. Innovation Districts, Smart City Infrastructures, research parks, coworking spaces, and regional economic clusters are examples of economic development investments that are being made by many, yet it is well recognized that much of the value comes from the personal collisions and relationships that are possible because of the physical proximity, information exchange, and density they create. Using the metropolitan areas of St. Louis and Kansas City, this paper provides insights into how regions can accelerate momentum and increase their return on these investments. The paper focuses on Entrepreneur Development organizations, which provide the first step in any ecosystem's startup pipeline and feed and strengthen Venture and Economic Development to achieve economic outcomes.

Multicriteria Spatial Decision Support Systems for Future Urban Energy Retrofitting Scenarios

Nowadays, there is an increasing concern about sustainable urban energy development taking into account national priorities of each city. Many cities have started to define future strategies and plans to reduce energy consumption and greenhouse gas emissions. Urban energy scenarios involve the consideration of a wide range of conflicting criteria, both socio-economic and environmental ones. Moreover, decision-makers (DMs) require proper tools that can support their choices in a context of multiple stakeholders and a long-term perspective. In this context, Multicriteria Spatial Decision Support Systems (MC-SDSS) are often used in order to define and analyze urban scenarios since they support the comparison of different solutions, based on a combination of multiple factors. The main problem, in relation to urban energy retrofitting scenarios, is the lack of appropriate knowledge and evaluation criteria. The latter are crucial for delivering and assessing urban energy scenarios through a MC-SDSS tool. The main goal of this paper is to analyze and test two different methods for the definition and ranking of the evaluation criteria. More specifically, the paper presents an on-going research study related to the development of a MC-SDSS tool able to identify and evaluate alternative energy urban scenarios in a long-term period perspective. This study refers to two Smart City and Communities research projects, namely: DIMMER (District Information Modeling and Management for Energy Reduction) and EEB (Zero Energy Buildings in Smart Urban Districts).

Living Lab Bamberg: an infrastructure to explore smart city research challenges in the wild

The growing number of smart technologies for ubiquitous sensing and interaction of computer systems and the physical environment offer many opportunities for more efficient usage of energy and other resources and to improve the quality of life among communities. There are numerous problems that must be solved first. Research areas such as security, privacy, data quality, and data modeling must be addressed. In order to move forward to a better world, we have established the living lab Bamberg where we will address these research problems and provide open data and interfaces to other researchers to enable collaboration and extensive testing of smart city research and applications. As we continue to use the living lab Bamberg to improve state of the art research in smart cities, we believe we will see smart city technology adopted more commonly and drastically improve the lives of people living in those cities.

Loss of coordination in complex directed networks: An incremental approach based on matrix measures

SummaryThis paper is concerned with the problem of understanding the dynamical mechanisms responsible for the loss of a desired behavior in complex directed networks. Specifically, we give 2 sufficient conditions for the desynchronization of a directed complex network, which are based on the use of matrix measures. The effectiveness of our approach is shown on 2 applications that are directly motivated by our research in smart cities and systems/synthetic biology.

The First Two Decades of Smart-City Research: A Bibliometric Analysis

ABSTRACTThis paper reports on the first two decades of research on smart cities by conducting a bibliometric analysis of the literature published between 1992 and 2012. The analysis shows that smart-city research is fragmented and lacks cohesion, and its growth follows two main development paths. The first one is based on the peer-reviewed publications produced by European universities, which support a holistic perspective on smart cities. The second path, instead, stands on the gray literature produced by the American business community and relates to a techno-centric understanding of the subject. Divided along such paths, the future development of this new and promising field of research risks being undermined. For while the bibliometric analysis indicates that smart cities are emerging as a fast-growing topic of scientific enquiry, much of the knowledge that is generated about them is singularly technological in nature. In that sense, lacking the social intelligence, cultural artifacts, and environmental attributes, which are needed for the ICT-related urban innovation that such research champions.

A Mixed Methodology for Defining a New Spatial Decision Analysis towards Low Carbon Cities

Cities play a leading role in economic development, security, sustainability, and climate change. The fact that the built environment energy consumption contributes to a huge amount of CO2 emissions has a significant impact on the public agenda. Consequently, the emergence of the low carbon city concept has enhanced the necessity for a both quantitative and qualitative evaluation of related energy strategies and policies. Support decision maker's tools can play an essential role for sustainable and effective land use governance, in particular, geo-referencing territorial data. This paper proposes a mixed approach method, integrating GIS Urban Energy Mapping, Stakeholders Analysis, and Multi-Criteria Decision Analysis whit the aim at defining urban energy saving scenarios in two sides which includes (a) the development of an energy consumption model of the building stock that is able to explore a number of possible futures scenarios based on GIS and give a representative picture of the actual energy consumption state and performance of urban systems (b) and the pre-selection of evaluation criteria based on environmental, economic, and social sustainability pillars. The expected result is the development of spatial decision support tool able to support urban planners, policy makers and built environment stakeholders in their efforts to plan, design and manage low carbon cities in their future strategic decisions by visualizing scenarios analysis. This paper is part of an ongoing Smart City research, a national cluster project, called Zero Energy Buildings in Smart Urban Districts (EEB).

Integrating Open Innovation Platforms in Public Sector Decision Making: Empirical Results from Smart City Research

Integrating Open Innovation Platforms in Public Sector Decision Making: Empirical Results from Smart City Research

Integrating Open Innovation Platforms in Public Sector Decision Making: Empirical Results from Smart City Research

Integrating Open Innovation Platforms in Public Sector Decision Making: Empirical Results from Smart City Research

Towards the next generation of intelligent building: An assessment study of current automation and future IoT based systems with a proposal for transitional design

The intelligent building, a major smart city research and development domain, has grown beyond the scope of automation, currently focusing on the occupant centered approaches and the ancillary services offered to the power grid. The Internet of Things (IoT) introduced in the recent years are enabling those prospects. However, due to numerous legacy automation systems already in the construction market, the IoT based intelligent building approach is frequently challenged. This paper assesses the opportunities along with the criticism for a fully IoT enabled and controllable intelligent building against the well established, legacy automation systems in a fair and transparent approach. In the second part, it proposes an interoperable intelligent building design for the creation of advanced building managements schemes, by integrating the assets of current automation tools and the emerging innovations. This unification and the progressive phasing out of legacy technologies, would catalyze the market of intelligent buildings in the context of smart cities by not limiting their potential solely on new constructions and by mitigating the investment if legacy systems are already installed.