10,979 research outputs found

    Empowering citizens' cognition and decision making in smart sustainable cities

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    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Advances in Internet technologies have made it possible to gather, store, and process large quantities of data, often in real time. When considering smart and sustainable cities, this big data generates useful information and insights to citizens, service providers, and policy makers. Transforming this data into knowledge allows for empowering citizens' cognition as well as supporting decision-making routines. However, several operational and computing issues need to be taken into account: 1) efficient data description and visualization, 2) forecasting citizens behavior, and 3) supporting decision making with intelligent algorithms. This paper identifies several challenges associated with the use of data analytics in smart sustainable cities and proposes the use of hybrid simulation-optimization and machine learning algorithms as an effective approach to empower citizens' cognition and decision making in such ecosystemsPeer ReviewedPostprint (author's final draft

    Transport and traffic analytics in smart cities

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    Vast generation of high resolution spatial and temporal data, particularly in urban settings, started revolution in mobility and human behavior related research. However, after initial wave of first data oriented insights their integration into ongoing, and traditionally used, planning and decision making processes seems to be hindered by still opened challenges. These challenges suggest need for stronger integration between data analytics and dedicated domain knowledge. Special session on Transport and Traffic Analytics in Smart Cities tackles these challenges from transport planners’ point of view. Collection of papers aims at identifying the existing gaps and bridging between related disciplines with aspiration to foster faster integration of data driven insights into smart cities’ dedicated planning

    Big data analytics:Computational intelligence techniques and application areas

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    Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

    Integrating big data into a sustainable mobility policy 2.0 planning support system

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    It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations

    A Framework for Integrating Transportation Into Smart Cities

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    In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities

    Smart Cities: Towards a New Citizenship Regime? A Discourse Analysis of the British Smart City Standard

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    Growing practice interest in smart cities has led to calls for a less technology-oriented and more citizen-centric approach. In response, this articles investigates the citizenship mode promulgated by the smart city standard of the British Standards Institution. The analysis uses the concept of citizenship regime and a mixture of quantitative and qualitative methods to discern key discursive frames defining the smart city and the particular citizenship dimensions brought into play. The results confirm an explicit citizenship rationale guiding the smart city (standard), although this displays some substantive shortcomings and contradictions. The article concludes with recommendations for both further theory and practice development

    Towards Sustainable Urban Futures: Exploring Environmental Initiatives in Smart Cities

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    Environmentally sustainable smart cities have emerged as a promising approach to address the challenges of urbanization while promoting sustainable development and enhancing residents' quality of life. This research article presents the key findings of a comprehensive study that explores the various aspects and initiatives found in environmentally sustainable smart cities.Renewable energy plays a pivotal role in these cities, with a strong emphasis on harnessing solar, wind, and geothermal power. Investments in clean energy infrastructure, such as solar panels, wind farms, and geothermal plants, significantly reduce reliance on fossil fuels and contribute to lower carbon emissions.Energy efficiency is another critical aspect of sustainable smart cities. These cities prioritize the use of smart grids for optimized energy distribution, smart meters for real-time energy monitoring and control, and energy-efficient buildings equipped with insulation, lighting, and HVAC systems that minimize energy consumption.Smart transportation is a key initiative in environmentally sustainable smart cities, focusing on reducing traffic congestion and air pollution. Electric vehicles (EVs) are promoted, accompanied by the development of charging infrastructure. Intelligent transportation systems aid in effective traffic management, while active transportation modes such as cycling, walking, and public transportation are encouraged.Efficient waste management systems are implemented to minimize landfill waste and promote recycling and composting. Smart waste bins equipped with sensors optimize waste collection routes, reduce littering, and provide real-time data on fill levels, aiding in effective waste management.Water management strategies are prioritized to conserve this precious resource. Smart water meters monitor consumption patterns, rainwater harvesting systems are implemented, water-efficient practices are promoted in buildings, and advanced leak detection technologies minimize water loss.Green spaces and biodiversity conservation are fundamental in environmentally sustainable smart cities. By integrating parks, gardens, rooftop greenery, and urban forests, these cities enhance residents' well-being, improve air quality, and provide habitats for wildlife, thus promoting biodiversity.Data analytics and the Internet of Things (IoT) play a crucial role in monitoring and optimizing various city systems. Real-time data collection and analysis enable effective management of energy usage, traffic flow, waste management, and other infrastructure, facilitating informed decision-making and resource allocation.Citizen engagement is fostered in environmentally sustainable smart cities. Platforms for citizen participation enable residents to provide feedback, report issues, and actively contribute to decision-making processes related to urban planning, energy conservation, waste management, and other sustainability initiatives.The implementation of these strategies in environmentally sustainable smart cities aims to reduce carbon footprints, enhance resource efficiency, improve air and water quality, and create healthier and more livable urban environments. By embracing technology, innovation, and citizen engagement, these cities pave the way for a sustainable and resilient future

    Recent advances in industrial wireless sensor networks towards efficient management in IoT

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    With the accelerated development of Internet-of- Things (IoT), wireless sensor networks (WSN) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for wireless sensor networks design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross layer design of lightweight and cloud-based RESTful web service
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