IEEE Trans. Netw. Sci. Eng.最新文献

{"title":"Introduction to the Special Section on Learning-Based Modeling, Management, and Control for Computer and Communication Networks","authors":"Jian Tang, Rong L. Zheng, Ö. Akan, Weiyi Zhang","doi":"10.1109/tnse.2019.2961103","DOIUrl":"https://doi.org/10.1109/tnse.2019.2961103","url":null,"abstract":"COMPUTER and communication networks are becoming larger and more complicated, generating a huge amount of runtime statistics data (such as traffic load, resource usages, etc.) every second. Meanwhile, emerging machine learning models and techniques, such as active learning, Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL), have been shown to dramatically improve the state-of-the-art of many applications, including video/image processing, natural language processing, game playing, etc. This special issue aims to exploit how these emerging and powerful techniques can be leveraged to grasp the exciting opportunities provided by pervasive availability of voluminous data to model, manage and control computer and communication networks. We appreciate contributions to this special section and the valuable and extensive efforts of the reviewers. The topics of this special section range from modeling, analysis, demonstration of various networks with emerging machine learning techniques. A brief review follows: In “Mitigating bottlenecks in wide area data analytics via machine learning,” Wang et al. present a system framework that minimizes query response times by detecting and mitigating bottlenecks at runtime. In “Deep learning meets wireless network optimization: Identify critical links,” Liu et al. investigate how to exploit deep learning for significant performance gain in wireless network optimization by identifying the possibility that a smaller-sized problem can be solved while sharing equally optimal solutions with the original problem. For the first time, in “Channel selective activity recognition with WiFi: A deep learning approach exploring wideband information,” Wang et al. explore wideband WiFi information with advanced deep learning towards more accurate and robust activity recognition. The key innovation is to actively select available WiFi channels with good quality and seamlessly hop among adjacent channels to form an extended channel. In “Caching for mobile social networks with deep learning: Twitter analysis for 2016 U.S. election,” Tsai et al. discuss the problem of context-aware data caching in the heterogeneous small cell networks to reduce the service delay and how the device-to-device and device-to-infrastructure improve the system social welfare. In simulation, such scheme was shown to efficiently reduce the service latency during 2016 U.S. presidential election where mobile users were urgent to request the election information through wireless networks. In “Renewable energy-aware big data analytics in geo-distributed data centers with reinforcement learning,” Xu et al. investigate the cost minimization problem of big data analytics on geodistributed data centers connected to renewable energy sources with unpredictable capacity. Dai et al. propose in “Hierarchical and hybrid: Mobility-compatible database-assisted framework for dynamic spectrum access” a hierarchical framework to enable the hybrid spectrum access s","PeriodicalId":407574,"journal":{"name":"IEEE Trans. Netw. Sci. Eng.","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131925558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to the Special Section on Network Science for Internet of Things (IoT)","authors":"Honggang Wang, Shui Yu, S. Zeadally, D. Rawat, Yue Gao","doi":"10.1109/tnse.2019.2959676","DOIUrl":"https://doi.org/10.1109/tnse.2019.2959676","url":null,"abstract":"The papers in this special section examine network science for the Internet of Things (IoT). IoT applications have been growing significantly in recent years and the so-called IoT ecosystem enables seamless connectivity that is paving the way for many applications such as smart home, smart health, connected vehicle, smart grid and others. The network infrastructure, connectivity, and dynamics in the IoT ecosystem are becoming increasingly complex, scalable and heterogeneous, opening up many challenges for network sciences and system engineering including architectural, operational, service and security challenges. In addition, since IoT applications generate huge amounts of network traffic over networks, network issues such complexity, efficiency, dynamics, interferences and interaction and robustness need to be re-examined on a large scale. We aim to leverage them in order to better understand network performance bound, user demands and experience and capacity of the IoT network infrastructure which will enable the network to seamlessly connect to IoT devices and support the emerging applications of IoT users. In summary, research on network sciences and engineering for inter-disciplinary IoT applications is still in its infancy.","PeriodicalId":407574,"journal":{"name":"IEEE Trans. Netw. Sci. Eng.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130462313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to the Special Section on Big Data and Artificial Intelligence for Network Technologies","authors":"Jie Li, Jinsong Wu, Bin Hu, Chonggang Wang, M. Daneshmand, R. Malekian","doi":"10.1109/tnse.2020.2968206","DOIUrl":"https://doi.org/10.1109/tnse.2020.2968206","url":null,"abstract":"The papers in this special section examines the deployment of Big Data and artificial intelligence for network technologies. The eneration of huge amounts of data, called big data, is creating the need for efficient tools to manage those data. Artificial intelligence (AI) has become the powerful tool in dealing with big data with recent breakthroughs at multiple fronts in machine learning, including deep learning. Meanwhile, information networks are becoming larger and more complicated, generating a huge amount of runtime statistics data such as traffic load, resource usages. The emerging big data and AI technologies may include a bunch of new requirements, applications and scenarios such as e-health, Intelligent Transportation Systems (ITS), Industrial Internet of Things (IIoT), and smart cities in the term of computing networks. The big data and AI driven network technologies also provide an unprecedented patient to discover new features, to characterize user demands and system capabilities in network resource assignment, security and privacy, system architecture, modeling and applications, which needs more explorations. The focus of this special section is to address the big data and artificial intelligence for network technologies. We appreciate contributions to this special section and the valuable and extensive efforts of the reviewers. The topics of this special section range from big data and AI algorithms, models, architecture for networks and systems to network architecture,","PeriodicalId":407574,"journal":{"name":"IEEE Trans. Netw. Sci. Eng.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130373301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}