The Experts below are selected from a list of 45639 Experts worldwide ranked by ideXlab platform
Athanasios V. Vasilakos - One of the best experts on this subject based on the ideXlab platform.
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Fog Computing for Sustainable Smart Cities: A Survey
ACM Computing Surveys, 2017Co-Authors: Charith Perera, Yongrui Qin, Julio Cezar Estrella, Stephan Reiff-marganiec, Athanasios V. VasilakosAbstract:The Internet of Things (IoT) aims to connect billions of Smart objects to the Internet, which can bring a promising future to Smart Cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a Smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for Smart Cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable Smart Cities.
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exploring data validity in transportation systems for Smart Cities
IEEE Communications Magazine, 2017Co-Authors: Yongxin Liu, Xiaoxiong Weng, Jiafu Wan, Xuejun Yue, Houbing Song, Athanasios V. VasilakosAbstract:Efficient urban transportation systems are widely accepted as essential infrastructure for Smart Cities, and they can highly increase a city�s vitality and convenience for residents. The three core pillars of Smart Cities can be considered to be data mining technology, IoT, and mobile wireless networks. Enormous data from IoT is stimulating our Cities to become Smarter than ever before. In transportation systems, data-driven management can dramatically enhance the operating efficiency by providing a clear and insightful image of passengers� transportation behavior. In this article, we focus on the data validity problem in a cellular network based transportation data collection system from two aspects: internal time discrepancy and data loss. First, the essence of time discrepancy was analyzed for both automated fare collection (AFC) and automated vehicular location (AVL) systems, and it was found that time discrepancies can be identified and rectified by analyzing passenger origin inference success rate using different time shift values and evolutionary algorithms. Second, the algorithmic framework to handle location data loss and time discrepancy was provided. Third, the spatial distribution characteristics of location data loss events were analyzed, and we discovered that they have a strong and positive relationship with both high passenger volume and shadowing effects in urbanized areas, which can cause severe biases on passenger traffic analysis. Our research has proposed some data-driven methodologies to increase data validity and provided some insights into the influence of IoT level data loss on public transportation systems for Smart Cities.
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Fog Computing for Sustainable Smart Cities: A Survey
arXiv: Networking and Internet Architecture, 2017Co-Authors: Charith Perera, Yongrui Qin, Julio Cezar Estrella, Stephan Reiff-marganiec, Athanasios V. VasilakosAbstract:The Internet of Things (IoT) aims to connect billions of Smart objects to the Internet, which can bring a promising future to Smart Cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a Smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for Smart Cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable Smart Cities.
Yan Zhang - One of the best experts on this subject based on the ideXlab platform.
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Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities
IEEE Network, 2019Co-Authors: Yi Liu, Shengli Xie, Li Jiang, Chao Yang, Yan ZhangAbstract:In recent years, green energy management systems (Smart grid, Smart buildings, and so on) have received huge research and industrial attention with the explosive development of Smart Cities. By introducing Internet of Things (IoT) technology, Smart Cities are able to achieve exquisite energy management by ubiquitous monitoring and reliable communications. However, long-term energy efficiency has become an important issue when using an IoT-based network structure. In this article, we focus on designing an IoT-based energy management system based on edge computing infrastructure with deep reinforcement learning. First, an overview of IoT-based energy management in Smart Cities is described. Then the framework and software model of an IoT-based system with edge computing are proposed. After that, we present an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework. Finally, we illustrate the effectiveness of the proposed scheme.
Sotirios Paroutis - One of the best experts on this subject based on the ideXlab platform.
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understanding Smart Cities innovation ecosystems technological advancements and societal challenges
Technological Forecasting and Social Change, 2019Co-Authors: Francesco Paolo Appio, Marcos Lima, Sotirios ParoutisAbstract:Smart Cities initiatives are spreading all around the globe at a phenomenal pace. Their bold ambition is to increase the competitiveness of local communities through innovation while increasing the quality of life for its citizens through better public services and a cleaner environment. Prior research has shown contrasting views and a multitude of dimensions and approaches to look at this phenomenon. In spite of the fact that this can stimulate the debate, it lacks a systematic assessment and an integrative view. The papers in the special issue on “Understanding Smart Cities: Innovation Ecosystems, Technological Advancements, and Societal Challenges” take stock of past work and provide new insights through the lenses of a hybrid framework. Moving from these premises, we offer an overview of the topic by featuring possible linkages and thematic clusters. Then, we sketch a novel research agenda for scholars, practitioners, and policy makers who wish to engage in – and build – a critical, constructive, and conducive discourse on Smart Cities.
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understanding Smart Cities innovation ecosystems technological advancements and societal challenges
Research Papers in Economics, 2019Co-Authors: Francesco Paolo Appio, Marcos Lima, Sotirios ParoutisAbstract:Smart Cities initiatives are spreading all around the globe at a phenomenal pace. Their bold ambition is to increase the competitiveness of local communities through innovation while increasing the quality of life for its citizens through better public services and a cleaner environment. Prior research has shown contrasting views and a multitude of dimensions and approaches to look at this phenomenon. In spite of the fact that this can stimulate the debate, it lacks a systematic assessment and an integrative view. The papers in the special issue on “Understanding Smart Cities: Innovation Ecosystems, Technological Advancements, and Societal Challenges” take stock of past work and provide new insights through the lenses of a hybrid framework. Moving from these premises, we offer an overview of the topic by featuring possible linkages and thematic clusters. Then, we sketch a novel research agenda for scholars, practitioners, and policy makers who wish to engage in – and build – a critical, constructive, and conducive discourse on Smart Cities. (This abstract was borrowed from another version of this item.)
Yi Liu - One of the best experts on this subject based on the ideXlab platform.
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Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities
IEEE Network, 2019Co-Authors: Yi Liu, Shengli Xie, Li Jiang, Chao Yang, Yan ZhangAbstract:In recent years, green energy management systems (Smart grid, Smart buildings, and so on) have received huge research and industrial attention with the explosive development of Smart Cities. By introducing Internet of Things (IoT) technology, Smart Cities are able to achieve exquisite energy management by ubiquitous monitoring and reliable communications. However, long-term energy efficiency has become an important issue when using an IoT-based network structure. In this article, we focus on designing an IoT-based energy management system based on edge computing infrastructure with deep reinforcement learning. First, an overview of IoT-based energy management in Smart Cities is described. Then the framework and software model of an IoT-based system with edge computing are proposed. After that, we present an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework. Finally, we illustrate the effectiveness of the proposed scheme.
Charith Perera - One of the best experts on this subject based on the ideXlab platform.
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Fog Computing for Sustainable Smart Cities: A Survey
ACM Computing Surveys, 2017Co-Authors: Charith Perera, Yongrui Qin, Julio Cezar Estrella, Stephan Reiff-marganiec, Athanasios V. VasilakosAbstract:The Internet of Things (IoT) aims to connect billions of Smart objects to the Internet, which can bring a promising future to Smart Cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a Smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for Smart Cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable Smart Cities.
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Fog Computing for Sustainable Smart Cities: A Survey
arXiv: Networking and Internet Architecture, 2017Co-Authors: Charith Perera, Yongrui Qin, Julio Cezar Estrella, Stephan Reiff-marganiec, Athanasios V. VasilakosAbstract:The Internet of Things (IoT) aims to connect billions of Smart objects to the Internet, which can bring a promising future to Smart Cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a Smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for Smart Cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable Smart Cities.
