Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effec...Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’content request patterns,the sophisticated caching placement policy,and the personalized recommendation tactics.In this article,we investigate how the potentials of Artificial Intelligence(AI)and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era.Towards this end,we first elaborate on the hierarchical RCC network architecture.Then,the devised AI and optimization empowered paradigm is introduced,whereas AI and optimization techniques are leveraged to predict the users’content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision,respectively.Through extensive case studies,we validate the effectiveness of AI-based predictors in estimating users’content preference and the superiority of optimized RCC policies over the conventional benchmarks.At last,we shed light on the opportunities and challenges in the future.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
基金This work was supported in part by the MOE ARF Tier 2 under Grant MOE2015-T2-2-104the Singapore University of Technology and Design-Zhejiang University(SUTD-ZJU)Research Collaboration under Grant SUTD-ZJU/RES/01/2016and the SUTD-ZJU Research Collaboration under Grant SUTD-ZJU/RES/05/2016.
文摘Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’content request patterns,the sophisticated caching placement policy,and the personalized recommendation tactics.In this article,we investigate how the potentials of Artificial Intelligence(AI)and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era.Towards this end,we first elaborate on the hierarchical RCC network architecture.Then,the devised AI and optimization empowered paradigm is introduced,whereas AI and optimization techniques are leveraged to predict the users’content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision,respectively.Through extensive case studies,we validate the effectiveness of AI-based predictors in estimating users’content preference and the superiority of optimized RCC policies over the conventional benchmarks.At last,we shed light on the opportunities and challenges in the future.
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.
