Data oriented view of a smart city: A big data approach

Transition Technologies for Electrification and Optimisation of Bus Transport Systems

Abstract: The topical issue of climate change has increasingly become important as scenarios indicate an increase of 2.5–7.8°C in the global mean temperature by the end of this century, if no greenhouse gas ...

OSeMOSYS-PuLP : A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Abstract: Recent open-data movements give access to large datasets derived from real-world observations. This data can be utilized to enhance energy systems modeling in terms of heterogeneity, confidence, and transparency. Furthermore, it allows to shift away from the common practice of considering average values towards probability distributions. In turn, heterogeneity and randomness of the real-world can be captured that are usually found in large samples of real-world data. This paper presents a methodological framework for an empirical deterministic–stochastic modeling approach to utilize large real-world datasets in long-term energy systems modeling. A new software system—OSeMOSYS-PuLP—was developed and is available now.It adds the feature of Monte Carlo simulations to the existing open-source energy modeling system (the OSeMOSYS modeling framework). An application example is given, in which the initial application example of OSeMOSYS is used and modified to include real-world operation data from a public bus transport system.

Smart Cities. Governance, Big Data und Anwendungsbeispiele

Abstract: Die Digitalisierung zeigt Auswirkungen auch auf die moderne Konzeption von Stadten. Unter der Bezeichnung Smart City entwickeln die Verantwortlichen zusammen mit den Stadtplanern und Experten Plattformen, die nicht nur die internen Verwaltungsablaufe, sondern auch die Beziehung zu und das Miteinander zwischen den Stakeholdern einer Stadt verbessern sollen. Dabei entstehen integrative Konzepte, die die City-Governance, Organisation und Technologie bundeln und auf eine einheitliche Zielerreichung hin ausrichten. Im Anschluss an die generische Entwicklung einer Smart City behandelt der Beitrag die spanische Stadt Barcelona als Beispiel fur eine entsprechende Umsetzung. Am Ende werden die Chancen und Risiken dieses Konzeptes zusammenfassend gewurdigt.

Mining and monitoring human activity patterns in smart environment-based healthcare systems

Abstract: Rapid evolution of sensing technology and increasing power computation has resulted in the emergence of smart environments with smart health services. Smart environments can generate hundreds of thousands of transactions per day and storage over the long term is a major issue. Therefore smart environment big data is utilized, which stores large volumes of datasets, both structured and unstructured. Healthcare services are recent and challenging aspects in analytics and sensor technology. People migrating from rural areas to urban areas affect healthcare services to a large extent. Due to migration and developing technology, cities around the world are investing in digital transformation, which aims to provide people with a healthier environment. New healthcare applications are based on activity recognition of people and can include wearable or ambient sensors to gather information related to health and human activity. Consumption of energy is analyzed along with the activity pattern of humans by determining the level of appliance usage that relates to human activity. The pattern determined is used to monitor elderly people living alone. The activity monitored can be learned just from appliances and their time usage. The proposed model detects appliance usage in particular with reliable accuracy. Mining is done purely based on absolute prediction along with clustering models.

IoTaaS based Approach to Design of a WSN for Secure Smart City Monitoring

Abstract: The paper proposes a new approach to designing a software for Digital City systems. These systems are built on the basis of wireless sensor networks and implement Internet of Things as a Service (IoTaaS) concept. The approach is based on a proposed knowledge model of distributed systems for design and testing of devices in the Digital City. This approach comprises a specific service-oriented architecture and software prototypes of design and testing tools. The suggested approach increases the automation level of the development process of such systems. It will contribute to reducing time of the development process and operating costs of the developers. The peculiarities of the approach include the use of rules for the interconnection of particular hardware units, on the one hand, and ways of software devices modeling, on the other. Besides, the originality is in application of specific Digital City services by using virtualization tools.

Understanding Smart Cities: An Integrative Framework

Abstract: Making a city "smart" is emerging as a strategy to mitigate the problems generated by the urban population growth and rapid urbanization. Yet little academic research has sparingly discussed the phenomenon. To close the gap in the literature about smart cities and in response to the increasing use of the concept, this paper proposes a framework to understand the concept of smart cities. Based on the exploration of a wide and extensive array of literature from various disciplinary areas we identify eight critical factors of smart city initiatives: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment. These factors form the basis of an integrative framework that can be used to examine how local governments are envisioning smart city initiatives. The framework suggests directions and agendas for smart city research and outlines practical implications for government professionals.

Conceptualizing smart city with dimensions of technology, people, and institutions

Abstract: This conceptual paper discusses how we can consider a particular city as a smart one, drawing on recent practices to make cities smart. A set of the common multidimensional components underlying the smart city concept and the core factors for a successful smart city initiative is identified by exploring current working definitions of smart city and a diversity of various conceptual relatives similar to smart city. The paper offers strategic principles aligning to the three main dimensions (technology, people, and institutions) of smart city: integration of infrastructures and technology-mediated services, social learning for strengthening human infrastructure, and governance for institutional improvement and citizen engagement.

Urban Computing: Concepts, Methodologies, and Applications

Abstract: Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

Foundations for smarter cities

Abstract: This paper describes the information technology (IT) foundation and principles for Smarter Cities™. Smarter Cities are urban areas that exploit operational data, such as that arising from traffic congestion, power consumption statistics, and public safety events, to optimize the operation of city services. The foundational concepts are instrumented, interconnected, and intelligent. Instrumented refers to sources of near-real-time real-world data from both physical and virtual sensors. Interconnected means the integration of those data into an enterprise computing platform and the communication of such information among the various city services. Intelligent refers to the inclusion of complex analytics, modeling, optimization, and visualization in the operational business processes to make better operational decisions. This approach enables the adaptation of city services to the behavior of the inhabitants, which permits the optimal use of the available physical infrastructure and resources, for example, in sensing and controlling consumption of energy and water, managing waste processing and transportation systems, and applying optimization to achieve new efficiencies among these resources. Additional roles exist in intelligent interaction between the city and its inhabitants and further contribute to operational efficiency while maintaining or enhancing quality of life.

Location-based and preference-aware recommendation using sparse geo-social networking data

Abstract: The popularity of location-based social networks provide us with a new platform to understand users' preferences based on their location histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are automatically learned from her location history and 2) Social opinions, which are mined from the location histories of the local experts. This recommender system can facilitate people's travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the user-locations matrix is very sparse, leading to a big challenge to traditional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to a new city. To this end, we propose a novel location recommender system, which consists of two main parts: offline modeling and online recommendation. The offline modeling part models each individual's personal preferences with a weighted category hierarchy (WCH) and infers the expertise of each user in a city with respect to different category of locations according to their location histories using an iterative learning model. The online recommendation part selects candidate local experts in a geospatial range that matches the user's preferences using a preference-aware candidate selection algorithm and then infers a score of the candidate locations based on the opinions of the selected local experts. Finally, the top-k ranked locations are returned as the recommendations for the user. We evaluated our system with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than baselines, while having a good efficiency of providing location recommendations.