利用预测小波分析优化动态网络中的移动采样

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Peppino Fazio , Miralem Mehic , Floriano De Rango , Mauro Tropea , Miroslav Voznak
{"title":"利用预测小波分析优化动态网络中的移动采样","authors":"Peppino Fazio ,&nbsp;Miralem Mehic ,&nbsp;Floriano De Rango ,&nbsp;Mauro Tropea ,&nbsp;Miroslav Voznak","doi":"10.1016/j.pmcj.2024.101887","DOIUrl":null,"url":null,"abstract":"<div><p>In the last decade, the investigation of mobility features has gained enormous significance in many scenarios as a result of the significant diffusion and deployment of mobile devices covered by high-speed technologies (e.g., 5G). Many contributions in the literature have attempted to discover mobility properties, but most studies are based on the time features of the mobility process. No study has yet considered the effects of setting a proper sampling frequency (generally set to 1 s), in order to avoid information loss. Following our previous works, we propose a novel predictive spectral approach for mobility sampling based on the concept of a predictive wavelet. With this method, the choice of sampling frequency is governed by the current spectral components of the mobility process and derived from an analysis of future, predicted components. To assess whether our proposal may yield a helpful method, we conducted several simulation campaigns to test sampling accuracy and obtained results that confirmed our expectations.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000130/pdfft?md5=4ee4cc1275b8ee0647dbfa8fed17e7b2&pid=1-s2.0-S1574119224000130-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Optimization of mobility sampling in dynamic networks using predictive wavelet analysis\",\"authors\":\"Peppino Fazio ,&nbsp;Miralem Mehic ,&nbsp;Floriano De Rango ,&nbsp;Mauro Tropea ,&nbsp;Miroslav Voznak\",\"doi\":\"10.1016/j.pmcj.2024.101887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In the last decade, the investigation of mobility features has gained enormous significance in many scenarios as a result of the significant diffusion and deployment of mobile devices covered by high-speed technologies (e.g., 5G). Many contributions in the literature have attempted to discover mobility properties, but most studies are based on the time features of the mobility process. No study has yet considered the effects of setting a proper sampling frequency (generally set to 1 s), in order to avoid information loss. Following our previous works, we propose a novel predictive spectral approach for mobility sampling based on the concept of a predictive wavelet. With this method, the choice of sampling frequency is governed by the current spectral components of the mobility process and derived from an analysis of future, predicted components. To assess whether our proposal may yield a helpful method, we conducted several simulation campaigns to test sampling accuracy and obtained results that confirmed our expectations.</p></div>\",\"PeriodicalId\":49005,\"journal\":{\"name\":\"Pervasive and Mobile Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1574119224000130/pdfft?md5=4ee4cc1275b8ee0647dbfa8fed17e7b2&pid=1-s2.0-S1574119224000130-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pervasive and Mobile Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574119224000130\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pervasive and Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574119224000130","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

近十年来,由于高速技术(如 5G)所覆盖的移动设备的大量普及和部署,对移动特性的研究在许多场景中都获得了巨大的意义。许多文献都试图发现移动特性,但大多数研究都是基于移动过程的时间特性。目前还没有研究考虑过设置适当的采样频率(一般设置为 1 秒)对避免信息丢失的影响。根据我们之前的研究成果,我们提出了一种基于预测小波概念的新型预测频谱流动性采样方法。利用这种方法,采样频率的选择受移动过程当前频谱成分的制约,并通过对未来预测成分的分析得出。为了评估我们的建议是否能产生一种有用的方法,我们进行了几次模拟活动来测试采样精度,结果证实了我们的预期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of mobility sampling in dynamic networks using predictive wavelet analysis

In the last decade, the investigation of mobility features has gained enormous significance in many scenarios as a result of the significant diffusion and deployment of mobile devices covered by high-speed technologies (e.g., 5G). Many contributions in the literature have attempted to discover mobility properties, but most studies are based on the time features of the mobility process. No study has yet considered the effects of setting a proper sampling frequency (generally set to 1 s), in order to avoid information loss. Following our previous works, we propose a novel predictive spectral approach for mobility sampling based on the concept of a predictive wavelet. With this method, the choice of sampling frequency is governed by the current spectral components of the mobility process and derived from an analysis of future, predicted components. To assess whether our proposal may yield a helpful method, we conducted several simulation campaigns to test sampling accuracy and obtained results that confirmed our expectations.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信