The Experts below are selected from a list of 87804 Experts worldwide ranked by ideXlab platform
Tianpeng Jiang - One of the best experts on this subject based on the ideXlab platform.
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d2d big data content deliveries over wireless device to device Sharing in large scale mobile networks
arXiv: Networking and Internet Architecture, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device (D2D) Sharing among users in proximity has been gaining more and more attention of global researchers and engineers. Users utilize wireless short-range D2D communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impacts among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by emerging Big Data techniques, we propose to design a big data platform, named D2D Big Data, in order to encourage the wireless D2D communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TBytes) from a popular D2D Sharing Application (APP), which contains 866 million D2D Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multidimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies and so on, we verify and evaluate the D2D Big Data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D Big Data and propose to unveil a promising upcoming future of wireless D2D communications.
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D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks
IEEE Wireless Communications, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device Sharing among users in proximity has been gaining more and more attention from global researchers and engineers. Users utilize wireless short-range device-to-device communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impact among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by the emerging big data techniques, we propose to design a big data platform, named D2D big data, in order to encourage wireless device-to-device communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TB) from a popular device-to-device Sharing Application that contains 866 million device-to-device Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multi-dimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies, and so on, we verify and evaluate the D2D big data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D big data and unveil the promising upcoming future of wireless device-to-device communications.
Xiaofei Wang - One of the best experts on this subject based on the ideXlab platform.
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d2d big data content deliveries over wireless device to device Sharing in large scale mobile networks
arXiv: Networking and Internet Architecture, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device (D2D) Sharing among users in proximity has been gaining more and more attention of global researchers and engineers. Users utilize wireless short-range D2D communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impacts among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by emerging Big Data techniques, we propose to design a big data platform, named D2D Big Data, in order to encourage the wireless D2D communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TBytes) from a popular D2D Sharing Application (APP), which contains 866 million D2D Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multidimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies and so on, we verify and evaluate the D2D Big Data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D Big Data and propose to unveil a promising upcoming future of wireless D2D communications.
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D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks
IEEE Wireless Communications, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device Sharing among users in proximity has been gaining more and more attention from global researchers and engineers. Users utilize wireless short-range device-to-device communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impact among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by the emerging big data techniques, we propose to design a big data platform, named D2D big data, in order to encourage wireless device-to-device communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TB) from a popular device-to-device Sharing Application that contains 866 million device-to-device Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multi-dimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies, and so on, we verify and evaluate the D2D big data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D big data and unveil the promising upcoming future of wireless device-to-device communications.
Yuhua Zhang - One of the best experts on this subject based on the ideXlab platform.
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d2d big data content deliveries over wireless device to device Sharing in large scale mobile networks
arXiv: Networking and Internet Architecture, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device (D2D) Sharing among users in proximity has been gaining more and more attention of global researchers and engineers. Users utilize wireless short-range D2D communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impacts among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by emerging Big Data techniques, we propose to design a big data platform, named D2D Big Data, in order to encourage the wireless D2D communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TBytes) from a popular D2D Sharing Application (APP), which contains 866 million D2D Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multidimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies and so on, we verify and evaluate the D2D Big Data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D Big Data and propose to unveil a promising upcoming future of wireless D2D communications.
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D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks
IEEE Wireless Communications, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device Sharing among users in proximity has been gaining more and more attention from global researchers and engineers. Users utilize wireless short-range device-to-device communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impact among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by the emerging big data techniques, we propose to design a big data platform, named D2D big data, in order to encourage wireless device-to-device communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TB) from a popular device-to-device Sharing Application that contains 866 million device-to-device Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multi-dimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies, and so on, we verify and evaluate the D2D big data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D big data and unveil the promising upcoming future of wireless device-to-device communications.
Nadra Guizani - One of the best experts on this subject based on the ideXlab platform.
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d2d big data content deliveries over wireless device to device Sharing in large scale mobile networks
arXiv: Networking and Internet Architecture, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device (D2D) Sharing among users in proximity has been gaining more and more attention of global researchers and engineers. Users utilize wireless short-range D2D communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impacts among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by emerging Big Data techniques, we propose to design a big data platform, named D2D Big Data, in order to encourage the wireless D2D communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TBytes) from a popular D2D Sharing Application (APP), which contains 866 million D2D Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multidimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies and so on, we verify and evaluate the D2D Big Data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D Big Data and propose to unveil a promising upcoming future of wireless D2D communications.
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D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks
IEEE Wireless Communications, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device Sharing among users in proximity has been gaining more and more attention from global researchers and engineers. Users utilize wireless short-range device-to-device communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impact among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by the emerging big data techniques, we propose to design a big data platform, named D2D big data, in order to encourage wireless device-to-device communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TB) from a popular device-to-device Sharing Application that contains 866 million device-to-device Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multi-dimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies, and so on, we verify and evaluate the D2D big data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D big data and unveil the promising upcoming future of wireless device-to-device communications.
Victor C M Leung - One of the best experts on this subject based on the ideXlab platform.
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d2d big data content deliveries over wireless device to device Sharing in large scale mobile networks
arXiv: Networking and Internet Architecture, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device (D2D) Sharing among users in proximity has been gaining more and more attention of global researchers and engineers. Users utilize wireless short-range D2D communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impacts among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by emerging Big Data techniques, we propose to design a big data platform, named D2D Big Data, in order to encourage the wireless D2D communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TBytes) from a popular D2D Sharing Application (APP), which contains 866 million D2D Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multidimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies and so on, we verify and evaluate the D2D Big Data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D Big Data and propose to unveil a promising upcoming future of wireless D2D communications.
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D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks
IEEE Wireless Communications, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto device-to-device Sharing among users in proximity has been gaining more and more attention from global researchers and engineers. Users utilize wireless short-range device-to-device communications for Sharing contents locally, due to not only the rapid Sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impact among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by the emerging big data techniques, we propose to design a big data platform, named D2D big data, in order to encourage wireless device-to-device communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TB) from a popular device-to-device Sharing Application that contains 866 million device-to-device Sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multi-dimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies, and so on, we verify and evaluate the D2D big data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D big data and unveil the promising upcoming future of wireless device-to-device communications.
