Citizen-centric data services for smarter cities

Smart Cities use Information and Communication Technologies (ICT) to manage more efficiently the resources and services offered by a city and to make them more approachable to all its stakeholders (citizens, companies and public administration). In contrast to the view of big corporations promoting holistic “smart city in a box” solutions, this work proposes that smarter cities can be achieved by combining already available infrastructure, i.e., Open Government Data and sensor networks deployed in cities, with the citizens’ active contributions towards city knowledge by means of their smartphones and the apps executed in them. In addition, this work introduces the main characteristics of the IES Cities platform, whose goal is to ease the generation of citizen-centric apps that exploit urban data in different domains. The proposed vision is achieved by providing a common access mechanism to the heterogeneous data sources offered by the city, which reduces the complexity of accessing the city’s data whilst bringing citizens closely to a prosumer (double consumer and producer) role and allowing to integrate legacy data into the cities’ data ecosystem.     

Delivering public value through open government data initiatives in a Smart City context

By using ICT in an innovative way, governments can improve the delivery of services and interaction with stakeholders. Open data is a way to help public organizations became more open and improve interaction with stakeholders. This paper aims to identify what are the public values enhancements acquired on smart city environment that discloses open data. We propose a conceptual model to analyze the smart city initiative. We contextualized the model taking a smart city domain by analyzing three related-initiatives that comprises open data in a smart city case carried at Rio de Janeiro Operations Center (COR) in Brazil by seven deep-interviewees directly involved - from inside and outside – in this case. The findings reveal evidences that open data initiatives contribute to enhance the delivery of public value in smart city contexts.     

Serious gaming as a means of facilitating truly smart cities: a narrative review

ABSTRACT

The term ‘smart cities’ is contested: its interpretation is becoming ever broader, often to accommodate commercial interests. Since cities are made up of individuals, all of whom are guided by their own world views and attitudes, the residual question is not ‘what should we do?’ but ‘how should we do it and how should we encourage and enable everyone to join in?’ By exploring the ways that gamification can be used to understand the effects of ‘smart initiatives’ on cities and their operation, it was concluded that gaming has considerable potential to affect individual and societal practices by profoundly influencing the gamers themselves, while technology and the game design itself play a central role to how gamification is implemented and used. This paper proposes one way of both creating cities to which citizens aspire and delivering a beneficial change in attitudes and behaviours to make such cities work. We propose that way-finding games should be developed as the most appropriate tools for participation. Designing such serious games with sustainability, resilience and liveability agendas in mind, encouraging widespread citizen participation as gamers, and taking cognisance of the outcomes would lead to both smarter citizens and smarter cities.     

MIKADO: a smart city KPIs assessment modeling framework

Smart decision making plays a central role for smart city governance. It exploits data analytics approaches applied to collected data, for supporting smart cities stakeholders in understanding and effectively managing a smart city. Smart governance is performed through the management of key performance indicators (KPIs), reflecting the degree of smartness and sustainability of smart cities. Even though KPIs are gaining relevance, e.g., at European level, the existing tools for their calculation are still limited. They mainly consist in dashboards and online spreadsheets that are rigid, thus making the KPIs evolution and customization a tedious and error-prone process. In this paper, we exploit model-driven engineering (MDE) techniques, through metamodel-based domain-specific languages (DSLs), to build a framework called MIKADO for the automatic assessment of KPIs over smart cities. In particular, the approach provides support for both: (i) domain experts, by the definition of a textual DSL for an intuitive KPIs modeling process and (ii) smart cities stakeholders, by the definition of graphical editors for smart cities modeling. Moreover, dynamic dashboards are generated to support an intuitive visualization and interpretation of the KPIs assessed by our KPIs evaluation engine. We provide evaluation results by showing a demonstration case as well as studying the scalability of the KPIs evaluation engine and the general usability of the approach with encouraging results. Moreover, the approach is open and extensible to further manage comparison among smart cities, simulations, and KPIs interrelations.

     

Multimedia-oriented action recognition in Smart City-based IoT using multilayer perceptron

The Internet of Things (IoT) devices and technologies for smart city applications produces a vast amount of multimedia data (e.g., audio, video, image, text and sensorial data), such big data are difficult to handle with traditional techniques and algorithms. The emerging machine learning techniques have the potential to facilitate the development of a new class of applications that can deal with such multimedia big data. Recently, Activity Recognition systems suggest using of multimedia data to detect daily actions, since it provides more accurate patterns; prevent the arising complain on privacy issues (in case of using audio-base data) and able to work on a big data. In this paper, we propose a Deep Learning (DL) methodology for classifying audio data that is based on multilayer perceptron neural networks. The contributions of our work are to propose an efficient design of the network topology including hidden layers, neurons, and the fitness function. In addition, the proposed methodology contributed in producing high performance classifier in terms of accuracy and f-measure. The experiments have been conducted on four large audio-datasets that have been collected to represent different modalities in a smart city. The results indicated that the proposed methodology achieved high performance as compared to the state-of-the-art machine learning techniques.

     

Big data in the public sector: Uncertainties and readiness

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.     

Privacy Preserving Geo-Linkage in the Big Urban Data Era

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.     

Recommender systems for smart cities

Among other conceptualizations, smart cities have been defined as functional urban areas articulated by the use of Information and Communication Technologies (ICT) and modern infrastructures to face city problems in efficient and sustainable ways. Within ICT, recommender systems are strong tools that filter relevant information, upgrading the relations between stakeholders in the polity and civil society, and assisting in decision making tasks through technological platforms. There are scientific articles covering recommendation approaches in smart city applications, and there are recommendation solutions implemented in real world smart city initiatives. However, to the best of our knowledge, there is not a comprehensive review of the state of the art on recommender systems for smart cities. For this reason, in this paper we present a taxonomy of smart city features, dimensions, actions and goals, and, according to these variables, we survey the existing literature on recommender systems. As a result of our survey, we do not only identify and analyze main research trends, but also show current opportunities and challenges where personalized recommendations could be exploited as solutions for citizens, firms and public administrations.     

Towards a data‐driven IoT software architecture for smart city utilities

The Internet of things (IoT) is emerging as the next big wave of digital presence for billions of devices on the Internet. Smart cities are a practical manifestation of IoT, with the goal of efficient, reliable, and safe delivery of city utilities like water, power, and transport to residents, through their intelligent management. A data‐driven IoT software platform is essential for realizing manageable and sustainable smart utilities and for novel applications to be developed upon them. Here, we propose such service‐oriented software architecture to address 2 key operational activities in a smart utility: the IoT fabric for resource management and the data and application platform for decision‐making. Our design uses Open Web standards and evolving network protocols, cloud and edge resources, and streaming big data platforms. We motivate our design requirements using the smart water management domain; some of these requirements are unique to developing nations. We also validate the architecture within a campus‐scale IoT testbed at the Indian Institute of Science, Bangalore and present our experiences. Our architecture is scalable to a township or city while also generalizable to other smart utility domains. Our experiences serve as a template for other similar efforts, particularly in emerging markets and highlight the gaps and opportunities for a data‐driven IoT software architecture for smart cities.     

The role of a location-based city exploration game in digital placemaking

ABSTRACT

Many digital technologies, such as social media, community systems, and public displays, have been studied to explore how people engage with each other in their community. Yet little is known about how one form of technology, location-based games (LBGs), can support urban residents in community awareness, city exploration, and placemaking as they navigate spaces and places in their cities. To explore this topic, we investigated the challenges urban residents faced in finding information about their community along a transit network. We then designed, developed, and evaluated an LBG called City Explorer that supports city exploration using gamification and the viewing and sharing of community information. We found that residents valued the fun, competition, and rewards afforded through play in public spaces, creating opportunities for placemaking through location services and knowledge sharing. Players also wanted additional knowledge about their transit commutes, including data about the frequency and routines of their transit rides. Collectively, such ridership data offers potential for smart city initiatives and illustrates that careful design considerations are required to balance people’s needs for play, personal data, privacy, and community information acquisition.     

Smart mechanisms and their influence on geometry learning of elementary school students in authentic contexts

With facilitation of advanced technologies, design and application of smart become promising research issues in education. Although it is potential for students to learn geometric in authentic contexts, there were still lack of studies addressing smart learning issue in authentic context for geometry. This study aim to propose an app, called SmartUG, to support students smartly to consolidate geometry understanding and learning through enriching experience of exploring and applying related geometry surrounding. There were four smart mechanisms proposed in SmartUG (direction guidance, learning progress, object recognition and answer feedback) to guide students' measuring and applying geometry smartly and meaningfully in authentic contexts. A total of 83 fifth-grade students participated in this experiment and were divided into three groups, an experimental group that learned with smart mechanisms, a control group that learned without smart mechanisms and a traditional control group that learning with traditional teaching approach. Basically, experimental group outperformed control group and traditional teaching group in term of geometry ability and estimation ability, which means students benefited from proposed SmartUG. Moreover, students showed positive attitude and high intention to use toward SmartUG. Students should be provided more chances to learn geometry smartly in authentic contexts with SmartUG. It is potential to future studies to implement more smart mechanisms to support students learning in authentic contexts. Moreover, the learning system can get smarter and smarter when the learning system gets more and more input data from students' use.     

Km4City ontology building vs data harvesting and cleaning for smart-city services

Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection.     

智能交通动态调度优化模型与评价方法

为了降低大城市市民出行成本,缓解公交企业运力压力,提出一种智能交通出行OD(Origin Destination,出行地和目的地)的公交调度优化算法,以公交出行OD客流预测和计划排班发车时间间隔为出发点,运用公交出行OD客流推导理论,构建智能交通出行OD的公交调度优化模型。通过获取个人OD数据,利用单条线路公交OD方法,实现全市公交OD矩阵推算。根据全市公交出行OD推算结果,求解公交调度模型,解决智能交通调度多目标规划和公交线网优化问题。通过仿真模拟试验,分析智能公交排班计划评价指标,计算车辆营运效率占比:自动排班仿真数据为79%,实际运营数据为73%;统计车辆高峰时段与全天营运车次占比:自动排班仿真数据为36.75%,实际运营数据为37.37%,满足智能公交计划排班评价指标的要求,实例证明模型和算法具有实用性和可靠性。     

CitySpec with shield: A secure intelligent assistant for requirement formalization

An increasing number of monitoring systems have been developed in smart cities to ensure that a city’s real-time operations satisfy safety and performance requirements. However, many existing city requirements are written in English with missing, inaccurate, or ambiguous information. There is a high demand for assisting city policymakers in converting human-specified requirements to machine-understandable formal specifications for monitoring systems. To tackle this limitation, we build CitySpec (Chen et al., 2022), the first intelligent assistant system for requirement specification in smart cities. To create CitySpec, we first collect over 1,500 real-world city requirements across different domains (e.g., transportation and energy) from over 100 cities and extract city-specific knowledge to generate a dataset of city vocabulary with 3,061 words. We also build a translation model and enhance it through requirement synthesis and develop a novel online learning framework with shielded validation. The evaluation results on real-world city requirements show that CitySpec increases the sentence-level accuracy of requirement specification from 59.02% to 86.64%, and has strong adaptability to a new city and a new domain (e.g., the F1 score for requirements in Seattle increases from 77.6% to 93.75% with online learning). After the enhancement from the shield function, CitySpec is now immune to most known textual adversarial inputs (e.g., the attack success rate of DeepWordBug (Gao et al., 2018) after the shield function is reduced to 0% from 82.73%). We test the CitySpec with 18 participants from different domains. CitySpec shows its strong usability and adaptability to different domains, and also its robustness to malicious inputs.     

Ignorance is bliss? An empirical analysis of the determinants of PSS usefulness in practice

Planning support systems (PSS) enabled by smart city technologies (big data and information and communication technologies (ICTs)) are becoming more widespread in their availability, but have not yet been fully recognized as being useful in planning practice. Thus, a better understanding of the determinants of PSS usefulness in practice helps to improve the functional support of PSS for smart cities. This study is based on a recent international questionnaire (268 respondents) designed to evaluate the perceptions of scholars and practitioners in the smart city planning field. Based on the empirical evidence, this paper recommends that it is imperative for PSS developers and users to be more responsive to the fit for task-technology and user-technology (i.e., utility and usability, respectively) since they positively contribute to PSS usefulness in practice and to be more sensitive to the potential negative effects of contextual factors on PSS usefulness in smart cities. The empirical analyses further suggest that rather than merely striving for integrating smart city technologies into advancing PSS, the way that innovative PSS are integrated into the planning framework (i.e., how well PSS can satisfy the needs of planning tasks and users by considering context-specificities) is of great significance in promoting PSS's actual usefulness.     

An edge-cloud-aided incremental tensor-based fuzzy c-means approach with big data fusion for exploring smart data

Recently, smart data has attracted great attention in the smart city community since it can provide valuable information to support intelligent services such as planning, monitoring, and decision making. However, it imposes a big challenge to explore smart data from big data gathered from smart city with various advanced fusion and analysis approaches. This paper proposes an incremental tensor-based fuzzy c-means approach (IT-FCM) for obtaining smart data from continuously generated big data. Specifically, a weighted version of the tensor-based fuzzy c-means approach (T-FCM) is firstly proposed to cluster the dataset that combines the previous cluster centroids and the new generated data. Aiming to improve the clustering efficiency, the old data objects are represented by the centroids to avoid repeat clustering. Furthermore, this paper presents an edge-cloud-aided clustering scheme to fuse big data from different sources and perspectives and further to implement co-clustering on the fused datasets for exploring smart data. Finally, the proposed IT-FCM approach is evaluated by comparing with T-FCM regarding clustering accuracy and efficiency on two different datasets in the experiments. The results state that IT-FCM outperforms T-FCM in clustering streaming big data in terms of accuracy and efficiency for obtaining smart data.     

Smart cities in the new service economy: building platforms for smart services

Recent changes in service environments have changed the preconditions of their production and consumption. These changes include unbundling services from production processes, growth of the information-rich economy and society, the search for creativity in service production and consumption and continuing growth of digital technologies. These contextual changes affect city governments because they provide a range of infrastructure and welfare services to citizens. Concepts such as ‘smart city’, ‘intelligent city’ and ‘knowledge city’ build new horizons for cities in undertaking their challenging service functions in an increasingly cost-conscious, competitive and environmentally oriented setting. What is essential in practically all of them is that they paint a picture of cities with smooth information processes, facilitation of creativity and innovativeness, and smart and sustainable solutions promoted through service platforms. This article discusses this topic, starting from the nature of services and the new service economy as the context of smart local public services. On this basis, we build an overall framework for understanding the basic forms and dimensions of smart public services. The focus is on conceptual systematisation of the key dimensions of smart services and the conceptual modelling of smart service platforms through which digital technology is increasingly embedded in social creativity. We provide examples of real-life smart service applications within the European context.     

Impacts of low citizen awareness and usage in smart city services: the case of London’s smart parking system

Smart city applications and services are increasingly considered as strategic means to cope with emerging global challenges such as climate change, pollution, the ageing population, and energy shortage. In particular, smart parking is a type of smart services used to improve traffic congestion and pollution within cities. Nevertheless, although smart city services are driven by advanced information technologies, their success is highly dependent on user engagement, which is historically problematic. This paper presents and discusses the results of a case study on the smart parking service in London. A questionnaire (involved a total of 212 local drivers) was adopted as the main data collection method. This was complemented by the collection and analysis of 470 online user comments left for the service. The results showed that London’s smart parking service may potentially help each driver to save an average of £68 (62.2 l) on petrol annually and reduce CO₂ emissions by 238.14 kg per car per year. At city level, a smart parking system could help London save £183.6 million worth of petrol per year and reduce its annual CO₂ emissions by 642,978 tons. However, public awareness, actual usage, and user satisfaction of this smart service are currently very low. These present substantial barriers to realise the potential economic and environmental benefits of the service. This paper concluded that further to the very technological efforts, local authorities and service providers need to make a stronger endeavour to enhance public engagement and user satisfaction towards smart services, in order to realise the promises of such solutions.     

Multi Criteria Decision Making System for Parking System

System supported smart parking can reduce traffic by making it stress free to locate empty parking spaces, hence lowering the risk of unfocussed driving. In this study, we propose a smart parking system using deep learning and an application-based approach. This system has two modules, one module detects and recognizes a license plate (LP), and the other selects a parking space; both modules use deep learning techniques. We used two modules that work independently to detect and recognize an LP by using an image of the vehicle. To detect parking space, only deep learning techniques were used. The two modules were compared with other state-of-the-art solutions. We utilized the You Only Look Once (YOLO) architecture to detect and recognize an LP because its performance in the context of Saudi Arabian LP numbers was superior to that of other solutions. Compared with existing state-of-the-art solutions, the performance of the proposed solution was more effective. The solution can be further improved for use in the city and large organizations that have priority parking spaces. A dataset of LP-annotated images of vehicles was used. The results of this study have considerable implications for smart parking, particularly in universities; in addition, they can be utilized for smart cities.     

物联网在萍乡智慧城市建设中的应用研究

物联网技术的应用为智慧城市信息化建设提供了基础数据来源,从而有利于城市的信息优化、控制和决策。文章提出了萍乡发展物联网的思路,最后对萍乡建设智慧城市应用物联网提出了有益的建议。