scholarly journals Web Browsing with Edge Computing

2021 ◽  
Vol 10 (2) ◽  
pp. 97-103
Author(s):  
Abhishek Rastogi ◽  
◽  
Shashank Vats ◽  
Shivam Pundir ◽  
Ramender Singh ◽  
...  
Keyword(s):  
Mobile Device ◽  
Screen Time ◽  
Computing System ◽  
Web Caching ◽  
Edge Computing ◽  
Web Browser ◽  
Mobile Web ◽  
Internet Browser ◽  
Index Terms ◽  
Server Push

Webpages have become increasingly complex in recent years, with longer loading times to match. This paper uses tailored edge computing to address this issue. As is customary, A grip server interacts with cloud web servers in edge computing. In A footing server, on the other hand, is a personalised edge computing system. referred to as a foothold The Server in the Middle (ESM) collaborates with other servers. users' cell phonesThis research focuses on two strategies based on personalised edge computing: edge aided caching and edge aided reprioritizing. Edge-assisted caching decreases the time it takes for a page to load. Because an ESM saves the cached data on mobile devices, So far, we've got components. Edge helps in the reprioritization of forces on the internet. browser to show visual components earlier and lowers the amount of white space Time spent in front of a screen.In addition, the ESM uses HTTP/2 rather than HTTP/1.1. This decreases the number of interactions between a mobile device and, as a result, the ESM, allowing advanced functionalities to be used. such as priority and server push Edge-assisted caching has been implemented. built in a high-end PC for Google's web browser Chrome for Android is a mobile web browser. Edge aided in an experiment, according to the results. The time it took for a popular website to load was cut in half because to caching. 59 percent in a network that is extremely congested. Another experiment found that edge-assisted reprioritization cut the white screen time of a webpage with a lot of photo photos by 21%. edge computing, reprioritization, mobile device, index terms browsing the web, caching

Examining the accuracy of estimated smartphone use: How well-being and usage level predict discrepancies between estimated and actual use

2019 ◽  
Author(s):  
Craig Sewall ◽  
Daniel Rosen ◽  
Todd M. Bear
Keyword(s):  
Public Health ◽  
Social Media ◽  
Life Satisfaction ◽  
Mobile Device ◽  
Screen Time ◽  
Well Being ◽  
Actual Use ◽  
Smartphone Use ◽  
Usage Level

The increasing ubiquity of mobile device and social media (SM) use has generated a substantial amount of research examining how these phenomena may impact public health. Prior studies have found that mobile device and SM use are associated with various aspects of well-being. However, a large portion of these studies relied upon self-reported estimates to measure amount of use, which can be inaccurate. Utilizing Apple’s “Screen Time” application to obtain actual iPhone and SM use data, the current study examined the accuracy of self-reported estimates, how inaccuracies bias relationships between use and well-being (depression, loneliness, and life satisfaction), and the degree to which inaccuracies were predicted by levels of well-being. Among a sample of 393 iPhone users, we found that: a.) participants misestimated their weekly overall iPhone and SM use by 22.1 and 16.6 hours, respectively; b.) the correlations between estimated use and well-being variables were consistently stronger than the correlations between actual use and well-being variables; and c.) the amount of inaccuracy in estimated use is associated with levels of participant well-being as well as amount of use. These findings suggest that estimates of device/SM use may be biased by factors that are fundamental to the relationships being investigated. **This manuscript is currently under review**

Evaluasi Aksesibilitas Learning Object Berdasarkan Web Content Accessibility Guidelines 2.0

2017 ◽  
Vol 9 (2) ◽  
pp. 67-71
Author(s):  
Herru Darmadi ◽  
Yan Fi ◽  
Hady Pranoto
Keyword(s):  
Learning Objects ◽  
Authoring Tool ◽  
Learning Object ◽  
Web Content ◽  
Web Browser ◽  
E Learning ◽  
Index Terms ◽  
Accessibility Guidelines ◽  
Content Accessibility

Learning Object (LO) is a representation of interactive content that are used to enrich e-learning activities. The goals of this case study were to evaluate accessibility and compatibility factors from learning objects that were produced by using BINUS E-learning Authoring Tool. Data were compiled by using experiment to 30 learning objects by using stratified random sampling from seven faculties in undergraduate program. Data were analyzed using accessibility and compatibility tests based on Web Content Accessibility Guidelines 2.0 Level A. Results of the analysis for accessibility and compatibility tests of Learning Objects was 90% better than average. The result shows that learning objects is fully compatible with major web browser. This paper also presents five accessibility problems found during the test and provide recommendation to overcome the related problems. It can be concluded that the learning objects that were produced using BINUS E-learning Authoring Tool have a high compatibility, with minor accessibility problems. Learning objects with a good accessibility and compatibility will be beneficial to all learner with or without disabilities during their learning process. Index Terms—accessibility, compatibility, HTML, learning object, WCAG2.0, web

Blockchain-based mobile edge computing system

2021 ◽  
Vol 561 ◽  
pp. 70-80
Author(s):  
Guangshun Li ◽  
Xinrong Ren ◽  
Junhua Wu ◽  
Wanting Ji ◽  
Haili Yu ◽  
...  
Keyword(s):  
Computing System ◽  
Edge Computing ◽  
Mobile Edge Computing

An Edge Computing System for Estimating Road Surface Condition on Winter Expressway

2021 ◽  
Author(s):  
Masahiro Yagi ◽  
Tomoyuki Takase ◽  
Sho Takahashi ◽  
Toru Hagiwara ◽  
Tomonori Ohiro ◽  
...  
Keyword(s):  
Surface Condition ◽  
Computing System ◽  
Road Surface ◽  
Edge Computing

Computation Bits Maximization in UAV-Enabled Mobile Edge Computing System

2021 ◽  
pp. 1-1
Author(s):  
Liang Lyu ◽  
Fanzi Zeng ◽  
Zhu Xiao ◽  
Chengyuan Zhang ◽  
Hongbo Jiang ◽  
...  
Keyword(s):  
Computing System ◽  
Edge Computing ◽  
Mobile Edge Computing

Agriculture Management Based on LoRa Edge Computing System

2020 ◽  
pp. 113-125
Author(s):  
Fatkhullokhodzha Sharofidinov ◽  
Mohammed Saleh Ali Muthanna ◽  
Van Dai Pham ◽  
Abdukodir Khakimov ◽  
Ammar Muthanna ◽  
...  
Keyword(s):  
Computing System ◽  
Edge Computing ◽  
Agriculture Management
Sign in / Sign up

Export Citation Format

Share Document