Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)envir...Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)environments.A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems.In the real world,the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage.In addition to pure manual rebalancing,in several works,attempts were made to predict the demand for bikes.In this paper,we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion.We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering.We carefully analyzed and discovered the group of features that impact the demand of bikes,from historical bike-sharing records and 5G IoT environment data.We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand.We performed sufficient experiments on two real-world datasets.The results confirm that compared to some existing methods,our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.展开更多
The 5G IoT(Internet of Things,IoT)is easier to implement in location privacy-preserving research.The terminals in distributed network architecture blur their accurate locations into a spatial cloaking region but most ...The 5G IoT(Internet of Things,IoT)is easier to implement in location privacy-preserving research.The terminals in distributed network architecture blur their accurate locations into a spatial cloaking region but most existing spatial cloaking algorithms cannot work well because of man-in-the-middle attacks,high communication overhead,time consumption,and the lower success rate.This paper proposes an algorithm that can recommend terminal’s privacy requirements based on getting terminal distribution information in the neighborhood after cross-layer authentication and therefore help 5G IoT terminals find enough collaborative terminals safely and quickly.The approach shows it can avoid man-in-the-middle attacks and needs lower communication costs and less searching time than 520ms at the same time.It has a great anonymization success rate by 93%through extensive simulation experiments for a range of 5G IoT scenarios.展开更多
因特殊工况要求,5G基站的空调设备多常年开启以维持基站内的正常工作温度,从而保障主通讯设备的正常运行。据统计,移动通信基站中空调设备能耗约占总能耗的46%,5G基站空调的高能耗问题亟待解决。本文基于NB-IoT(Narrow Band Internet of...因特殊工况要求,5G基站的空调设备多常年开启以维持基站内的正常工作温度,从而保障主通讯设备的正常运行。据统计,移动通信基站中空调设备能耗约占总能耗的46%,5G基站空调的高能耗问题亟待解决。本文基于NB-IoT(Narrow Band Internet of Things,窄带物联网)技术设计了远程控制系统,实现了对5G基站空调的实时监控和运维;再通过设计智能温控系统,利用室内温度与室内外温差双参数决策、PID控制来实现5G基站空调的智能调节。通过Simulink仿真与实验测试验证了控制系统的有效性。展开更多
基金supported by the National Natural Science Foundation of China (No. 61902236)Fundamental Research Funds for the Central Universities (No. JB210311).
文摘Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)environments.A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems.In the real world,the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage.In addition to pure manual rebalancing,in several works,attempts were made to predict the demand for bikes.In this paper,we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion.We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering.We carefully analyzed and discovered the group of features that impact the demand of bikes,from historical bike-sharing records and 5G IoT environment data.We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand.We performed sufficient experiments on two real-world datasets.The results confirm that compared to some existing methods,our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.
基金the Project“Research on Basic Theory of Cyber Mimic Defense”supported by Innovation Group Project of the National Natural Science Foundation of China(No.61521003)partly supported by National Natural Science Foundation of China(No.61772548)key universities and academic disciplines contruction project and Key Scientific and Technological Projects in Henan Province(Grant No.192102210092).
文摘The 5G IoT(Internet of Things,IoT)is easier to implement in location privacy-preserving research.The terminals in distributed network architecture blur their accurate locations into a spatial cloaking region but most existing spatial cloaking algorithms cannot work well because of man-in-the-middle attacks,high communication overhead,time consumption,and the lower success rate.This paper proposes an algorithm that can recommend terminal’s privacy requirements based on getting terminal distribution information in the neighborhood after cross-layer authentication and therefore help 5G IoT terminals find enough collaborative terminals safely and quickly.The approach shows it can avoid man-in-the-middle attacks and needs lower communication costs and less searching time than 520ms at the same time.It has a great anonymization success rate by 93%through extensive simulation experiments for a range of 5G IoT scenarios.
文摘因特殊工况要求,5G基站的空调设备多常年开启以维持基站内的正常工作温度,从而保障主通讯设备的正常运行。据统计,移动通信基站中空调设备能耗约占总能耗的46%,5G基站空调的高能耗问题亟待解决。本文基于NB-IoT(Narrow Band Internet of Things,窄带物联网)技术设计了远程控制系统,实现了对5G基站空调的实时监控和运维;再通过设计智能温控系统,利用室内温度与室内外温差双参数决策、PID控制来实现5G基站空调的智能调节。通过Simulink仿真与实验测试验证了控制系统的有效性。
