Pub Date : 2021-07-27DOI: 10.12142/ZTECOM.202102003
Zhao Kongyang, Gao Bin, Zhou Zhi
Collaborative cross-edge analytics is a new computing paradigm in which Inter⁃ net of Things (IoT) data analytics is performed across multiple geographically dispersed edge clouds. Existing work on collaborative cross-edge analytics mostly focuses on reduc⁃ ing either analytics response time or wide-area network (WAN) traffic volume. In this work, we empirically demonstrate that reducing either analytics response time or network traffic volume does not necessarily minimize the WAN traffic cost, due to the price hetero⁃ geneity of WAN links. To explicitly leverage the price heterogeneity for WAN cost minimi⁃ zation, we propose to schedule analytic tasks based on both price and bandwidth heteroge⁃ neities. Unfortunately, the problem of WAN cost minimization underperformance con⁃ straint is shown non-deterministic polynomial (NP)-hard and thus computationally intrac⁃ table for large inputs. To address this challenge, we propose priceand performanceaware geo-distributed analytics (PPGA) , an efficient task scheduling heuristic that im⁃ proves the cost-efficiency of IoT data analytic jobs across edge datacenters. We imple⁃ ment PPGA based on Apache Spark and conduct extensive experiments on Amazon EC2 to verify the efficacy of PPGA.
{"title":"Cost-Effective Task Scheduling for Collaborative Cross-Edge Analytics","authors":"Zhao Kongyang, Gao Bin, Zhou Zhi","doi":"10.12142/ZTECOM.202102003","DOIUrl":"https://doi.org/10.12142/ZTECOM.202102003","url":null,"abstract":"Collaborative cross-edge analytics is a new computing paradigm in which Inter⁃ net of Things (IoT) data analytics is performed across multiple geographically dispersed edge clouds. Existing work on collaborative cross-edge analytics mostly focuses on reduc⁃ ing either analytics response time or wide-area network (WAN) traffic volume. In this work, we empirically demonstrate that reducing either analytics response time or network traffic volume does not necessarily minimize the WAN traffic cost, due to the price hetero⁃ geneity of WAN links. To explicitly leverage the price heterogeneity for WAN cost minimi⁃ zation, we propose to schedule analytic tasks based on both price and bandwidth heteroge⁃ neities. Unfortunately, the problem of WAN cost minimization underperformance con⁃ straint is shown non-deterministic polynomial (NP)-hard and thus computationally intrac⁃ table for large inputs. To address this challenge, we propose priceand performanceaware geo-distributed analytics (PPGA) , an efficient task scheduling heuristic that im⁃ proves the cost-efficiency of IoT data analytic jobs across edge datacenters. We imple⁃ ment PPGA based on Apache Spark and conduct extensive experiments on Amazon EC2 to verify the efficacy of PPGA.","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"19 1","pages":"11-19"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42828505","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}
Pub Date : 2021-07-27DOI: 10.12142/ZTECOM.202102007
Tan Jie, Sha Xiubin, Dai Bo, Lu Ting
{"title":"Analysis of Industrial Internet of Things and Digital Twins","authors":"Tan Jie, Sha Xiubin, Dai Bo, Lu Ting","doi":"10.12142/ZTECOM.202102007","DOIUrl":"https://doi.org/10.12142/ZTECOM.202102007","url":null,"abstract":"","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"19 1","pages":"53-60"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44978305","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}
Pub Date : 2021-07-27DOI: 10.12142/ZTECOM.202102005
Lou Kaihao, Y. Yongjian, Yang Funing, Z. Xingliang
Out-door billboard advertising plays an important role in attracting potential cus⁃ tomers. However, whether a customer can be attracted is influenced by many factors, such as the probability that he/she sees the billboard, the degree of his/her interest, and the detour dis⁃ tance for buying the product. Taking the above factors into account, we propose advertising strategies for selecting an effective set of billboards under the advertising budget to maximize commercial profit. By using the data collected by Mobile Crowdsensing (MCS), we extract po⁃ tential customers’implicit information, such as their trajectories and preferences. We then study the billboard selection problem under two situations, where the advertiser may have only one or multiple products. When only one kind of product needs advertising, the billboard se⁃ lection problem is formulated as the probabilistic set coverage problem. We propose two heu⁃ ristic advertising strategies to greedily select advertising billboards, which achieves the expect⁃ ed maximum commercial profit with the lowest cost. When the advertiser has multiple prod⁃ ucts, we formulate the problem as searching for an optimal solution and adopt the simulated annealing algorithm to search for global optimum instead of local optimum. Extensive experi⁃ ments based on three real-world data sets verify that our proposed advertising strategies can achieve the superior commercial profit compared with the state-of-the-art strategies.
{"title":"Maximum-Profit Advertising Strategy Using Crowdsensing Trajectory Data","authors":"Lou Kaihao, Y. Yongjian, Yang Funing, Z. Xingliang","doi":"10.12142/ZTECOM.202102005","DOIUrl":"https://doi.org/10.12142/ZTECOM.202102005","url":null,"abstract":"Out-door billboard advertising plays an important role in attracting potential cus⁃ tomers. However, whether a customer can be attracted is influenced by many factors, such as the probability that he/she sees the billboard, the degree of his/her interest, and the detour dis⁃ tance for buying the product. Taking the above factors into account, we propose advertising strategies for selecting an effective set of billboards under the advertising budget to maximize commercial profit. By using the data collected by Mobile Crowdsensing (MCS), we extract po⁃ tential customers’implicit information, such as their trajectories and preferences. We then study the billboard selection problem under two situations, where the advertiser may have only one or multiple products. When only one kind of product needs advertising, the billboard se⁃ lection problem is formulated as the probabilistic set coverage problem. We propose two heu⁃ ristic advertising strategies to greedily select advertising billboards, which achieves the expect⁃ ed maximum commercial profit with the lowest cost. When the advertiser has multiple prod⁃ ucts, we formulate the problem as searching for an optimal solution and adopt the simulated annealing algorithm to search for global optimum instead of local optimum. Extensive experi⁃ ments based on three real-world data sets verify that our proposed advertising strategies can achieve the superior commercial profit compared with the state-of-the-art strategies.","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"19 1","pages":"29-43"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48690649","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}
Pub Date : 2021-01-13DOI: 10.12142/ZTECOM.202004006
Sun Chenhua, Y. Bo, Liang Xudong, Tianzhang Xing, Pang Ce
{"title":"Adaptability Analysis of IP Routing Protocol in Broadband LEO Constellation Systems","authors":"Sun Chenhua, Y. Bo, Liang Xudong, Tianzhang Xing, Pang Ce","doi":"10.12142/ZTECOM.202004006","DOIUrl":"https://doi.org/10.12142/ZTECOM.202004006","url":null,"abstract":"","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"18 1","pages":"34-44"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46334275","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}
Pub Date : 2021-01-13DOI: 10.12142/ZTECOM.202004007
Li Li, Zhang Xuejiao, Z. Jianhua, Xu Changzhi, Jin Yi
{"title":"Advanced Space Laser Communication Technology on CubeSats","authors":"Li Li, Zhang Xuejiao, Z. Jianhua, Xu Changzhi, Jin Yi","doi":"10.12142/ZTECOM.202004007","DOIUrl":"https://doi.org/10.12142/ZTECOM.202004007","url":null,"abstract":"","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"18 1","pages":"45-54"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48507718","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}
Pub Date : 2021-01-13DOI: 10.12142/ZTECOM.202004002
Zhang Gengxin, Ding Xiaojin, QU Zhicheng
{"title":"Space‑Terrestrial Integrated Architecture for Internet of Things","authors":"Zhang Gengxin, Ding Xiaojin, QU Zhicheng","doi":"10.12142/ZTECOM.202004002","DOIUrl":"https://doi.org/10.12142/ZTECOM.202004002","url":null,"abstract":"","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"18 1","pages":"3-9"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47321752","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}
Pub Date : 2021-01-13DOI: 10.12142/ZTECOM.202004010
Fan Guotian, L. Bo, Han Qin, Jiao Rihua, Qu Gang
{"title":"Robust Lane Detection and Tracking Based on Machine Vision","authors":"Fan Guotian, L. Bo, Han Qin, Jiao Rihua, Qu Gang","doi":"10.12142/ZTECOM.202004010","DOIUrl":"https://doi.org/10.12142/ZTECOM.202004010","url":null,"abstract":"","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"18 1","pages":"69-77"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48075252","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}
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