Facing the development of future 5 G, the emerging technologies such as Internet of things, big data, cloud computing, and artificial intelligence is enhancing an explosive growth in data traffic. Radical changes in c...Facing the development of future 5 G, the emerging technologies such as Internet of things, big data, cloud computing, and artificial intelligence is enhancing an explosive growth in data traffic. Radical changes in communication theory and implement technologies, the wireless communications and wireless networks have entered a new era. Among them, wireless big data(WBD) has tremendous value, and artificial intelligence(AI) gives unthinkable possibilities. However, in the big data development and artificial intelligence application groups, the lack of a sound theoretical foundation and mathematical methods is regarded as a real challenge that needs to be solved. From the basic problem of wireless communication, the interrelationship of demand, environment and ability, this paper intends to investigate the concept and data model of WBD, the wireless data mining, the wireless knowledge and wireless knowledge learning(WKL), and typical practices examples, to facilitate and open up more opportunities of WBD research and developments. Such research is beneficial for creating new theoretical foundation and emerging technologies of future wireless communications.展开更多
In HetNets(Heterogeneous Networks),each network is allocated with xed spectrum resource and provides service to its assigned users using speci c RAT(Radio Access Technology).Due to the high dynamics of load distributi...In HetNets(Heterogeneous Networks),each network is allocated with xed spectrum resource and provides service to its assigned users using speci c RAT(Radio Access Technology).Due to the high dynamics of load distribution among di erent networks,simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices,the consequent upsurge of data trac,and dynamic user QoE(Quality-of-Experience).The deployment of smart networks,which are supported by SRA(Smart Resource Allocation)among di erent networks and CUA(Cognitive User Access)among di erent users,is deemed a promising solution to these challenges.In this paper,we propose a frame-work to transform HetNets to smart networks by leveraging WBD(Wireless Big Data),CR(Cognitive Radio)and NFV(Network Function Virtualization)techniques.CR and NFV support resource slicing in spectrum,physical layers,and network layers,while WBD is used to design intelligent mechanisms for resource mapping and trac prediction through powerful AI(Arti cial Intelligence)methods.We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks.In particular,the potential of WBD is revealed through high level view on SRA,which intelligently maps radio and network resources to each network for meeting the dynamic trac demand,as well as CUA,which allows mobile users to access the best available network with manageable cost,yet achieving target QoS(Quality-of-Service)or QoE.展开更多
Wireless big data describes a wide range of massive data that is generated,collected and stored in wireless networks by wireless devices and users.While these data share some common properties with traditional big dat...Wireless big data describes a wide range of massive data that is generated,collected and stored in wireless networks by wireless devices and users.While these data share some common properties with traditional big data,they have their own unique characteristics and provide numerous advantages for academic research and practical applications.This article reviews the recent advances and trends in the field of wireless big data.Due to space constraints,this survey is not intended to cover all aspects in this field,but to focus on the data aided transmission,data driven network optimization and novel applications.It is expected that the survey will help the readers to understand this exciting and emerging research field better.Moreover,open issues and promising future directions are also identified.展开更多
The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. Thi...The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. This paper gives an overview the e-health-care architecture. We discuss the four layers of the architecture-data collection, data transport, data storage, and data analysis-as well as the challenges of data security, data privacy, real-time delivery, and open standard interface. We discuss the necessity of establishing an impeccably secure access mechanism and of enacting strong laws to protect patient privacy.展开更多
The past decade has witnessed explosive growth in wireless big data,as well as in various big data analytics technologies.The intelligence mined from these massive datasets can be utilized to optimize wireless system ...The past decade has witnessed explosive growth in wireless big data,as well as in various big data analytics technologies.The intelligence mined from these massive datasets can be utilized to optimize wireless system design.Due to the open data policy of the mainstream OSN(Online Social Network)service providers and the pervasiveness of online social services,this paper studies how social big data can be embraced in wireless communication system design.We start with our rst hand experience on crawling social big data and the principal of social-aware system design.Then we present ve studies on utilizing social intelligence for system optimization,including community-aware social video distribution over cloud content delivery networks,public cloud assisted mobile social video sharing,data driven bitrate adjustment and spectrum allocation for mobile social video sharing,location-aware video streaming,and social video distribution over information-centric networking.展开更多
The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applicatio...The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applications.Therefore the research and development on 6 G have been put on the agenda.Regarding demands and characteristics of future 6 G,artificial intelligence(A),big data(B)and cloud computing(C)will play indispensable roles in achieving the highest efficiency and the largest benefits.Interestingly,the initials of these three aspects remind us the significance of vitamin ABC to human body.In this article we specifically expound on the three elements of ABC and relationships in between.We analyze the basic characteristics of wireless big data(WBD)and the corresponding technical action in A and C,which are the high dimensional feature and spatial separation,the predictive ability,and the characteristics of knowledge.Based on the abilities of WBD,a new learning approach for wireless AI called knowledge+data-driven deep learning(KD-DL)method,and a layered computing architecture of mobile network integrating cloud/edge/terminal computing,is proposed,and their achievable efficiency is discussed.These progress will be conducive to the development of future 6 G.展开更多
文摘Facing the development of future 5 G, the emerging technologies such as Internet of things, big data, cloud computing, and artificial intelligence is enhancing an explosive growth in data traffic. Radical changes in communication theory and implement technologies, the wireless communications and wireless networks have entered a new era. Among them, wireless big data(WBD) has tremendous value, and artificial intelligence(AI) gives unthinkable possibilities. However, in the big data development and artificial intelligence application groups, the lack of a sound theoretical foundation and mathematical methods is regarded as a real challenge that needs to be solved. From the basic problem of wireless communication, the interrelationship of demand, environment and ability, this paper intends to investigate the concept and data model of WBD, the wireless data mining, the wireless knowledge and wireless knowledge learning(WKL), and typical practices examples, to facilitate and open up more opportunities of WBD research and developments. Such research is beneficial for creating new theoretical foundation and emerging technologies of future wireless communications.
基金This work is supported by the National Natural Science Foundation of China(Nos.61571100,61631005).
文摘In HetNets(Heterogeneous Networks),each network is allocated with xed spectrum resource and provides service to its assigned users using speci c RAT(Radio Access Technology).Due to the high dynamics of load distribution among di erent networks,simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices,the consequent upsurge of data trac,and dynamic user QoE(Quality-of-Experience).The deployment of smart networks,which are supported by SRA(Smart Resource Allocation)among di erent networks and CUA(Cognitive User Access)among di erent users,is deemed a promising solution to these challenges.In this paper,we propose a frame-work to transform HetNets to smart networks by leveraging WBD(Wireless Big Data),CR(Cognitive Radio)and NFV(Network Function Virtualization)techniques.CR and NFV support resource slicing in spectrum,physical layers,and network layers,while WBD is used to design intelligent mechanisms for resource mapping and trac prediction through powerful AI(Arti cial Intelligence)methods.We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks.In particular,the potential of WBD is revealed through high level view on SRA,which intelligently maps radio and network resources to each network for meeting the dynamic trac demand,as well as CUA,which allows mobile users to access the best available network with manageable cost,yet achieving target QoS(Quality-of-Service)or QoE.
基金This research work is supported in part by the U.S.OASD(R&E)(Office of the Assistant Secretary of Defense for Research and Engineering)(No.FA8750-15-2-0119)by the U.S.Army Research Office(No.W911NF-16-1-0496)+1 种基金The U.S.Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements,either expressed or implied,of the Office of the Assistant Secretary of Defense for Research and Engineering(OASD(R&E)),the Army Research Office,or the U.S.Government.Sihai Zhang received support from Key Program of National Natural Science Foundation of China(No.61631018)the Fundamental Research Funds for the Central Universities and Huawei Technology Innovative Research on Wireless Big Data.
文摘Wireless big data describes a wide range of massive data that is generated,collected and stored in wireless networks by wireless devices and users.While these data share some common properties with traditional big data,they have their own unique characteristics and provide numerous advantages for academic research and practical applications.This article reviews the recent advances and trends in the field of wireless big data.Due to space constraints,this survey is not intended to cover all aspects in this field,but to focus on the data aided transmission,data driven network optimization and novel applications.It is expected that the survey will help the readers to understand this exciting and emerging research field better.Moreover,open issues and promising future directions are also identified.
基金the Natural Science Foundation of Guangdong Province, China (No.9151009001000021)the Ministry of Education of Guangdong Province Special Fund Funded Projects through the Cooperative of China (No.2009B090300341)+2 种基金the National Natural Science Foundation of China (No.61262013)the Open Fund of Guangdong Province Key Laboratory of Precision Equipment and Manufacturing Technology (No.PEMT1303)the Higher Vocational Education Teaching Reform Project of Guangdong Province (No.20130301011) for their support in this research
文摘The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. This paper gives an overview the e-health-care architecture. We discuss the four layers of the architecture-data collection, data transport, data storage, and data analysis-as well as the challenges of data security, data privacy, real-time delivery, and open standard interface. We discuss the necessity of establishing an impeccably secure access mechanism and of enacting strong laws to protect patient privacy.
基金This work is supported by the Ministry of Education Academic Research Fund Tier 1(No.RG 17/14)SMART Innovation Grant(No.ING148077-ICT),Cisco Systems Inc.(No.M4061334.020)BCA Green Buildings Innovation Cluster R&D Grant(No.NRF2015ENC-GBICRD001-012).
文摘The past decade has witnessed explosive growth in wireless big data,as well as in various big data analytics technologies.The intelligence mined from these massive datasets can be utilized to optimize wireless system design.Due to the open data policy of the mainstream OSN(Online Social Network)service providers and the pervasiveness of online social services,this paper studies how social big data can be embraced in wireless communication system design.We start with our rst hand experience on crawling social big data and the principal of social-aware system design.Then we present ve studies on utilizing social intelligence for system optimization,including community-aware social video distribution over cloud content delivery networks,public cloud assisted mobile social video sharing,data driven bitrate adjustment and spectrum allocation for mobile social video sharing,location-aware video streaming,and social video distribution over information-centric networking.
基金supported by Key Program of Natural Science Foundation of China(Grant No.61631018)Anhui Provincial Natural Science Foundation(Grant No.1908085MF177)Huawei Technology Innovative Research(YBN2018095087)。
文摘The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applications.Therefore the research and development on 6 G have been put on the agenda.Regarding demands and characteristics of future 6 G,artificial intelligence(A),big data(B)and cloud computing(C)will play indispensable roles in achieving the highest efficiency and the largest benefits.Interestingly,the initials of these three aspects remind us the significance of vitamin ABC to human body.In this article we specifically expound on the three elements of ABC and relationships in between.We analyze the basic characteristics of wireless big data(WBD)and the corresponding technical action in A and C,which are the high dimensional feature and spatial separation,the predictive ability,and the characteristics of knowledge.Based on the abilities of WBD,a new learning approach for wireless AI called knowledge+data-driven deep learning(KD-DL)method,and a layered computing architecture of mobile network integrating cloud/edge/terminal computing,is proposed,and their achievable efficiency is discussed.These progress will be conducive to the development of future 6 G.
