Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution
<div><p>The COVID-19 pandemic has affected the world socially and economically changing behaviors towards medical facilities, public gatherings, workplaces, and education. Educational institutes have been shutdown sporadically across the globe forcing teachers and students to adopt dista...
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2022
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| author | Kashif Bilal (16896357) |
| author2 | Junaid Shuja (18434070) Aiman Erbad (14150589) Waleed Alasmary (11741768) Eisa Alanazi (11741771) Abdullah Alourani (17721108) |
| author2_role | author author author author author |
| author_facet | Kashif Bilal (16896357) Junaid Shuja (18434070) Aiman Erbad (14150589) Waleed Alasmary (11741768) Eisa Alanazi (11741771) Abdullah Alourani (17721108) |
| author_role | author |
| dc.creator.none.fl_str_mv | Kashif Bilal (16896357) Junaid Shuja (18434070) Aiman Erbad (14150589) Waleed Alasmary (11741768) Eisa Alanazi (11741771) Abdullah Alourani (17721108) |
| dc.date.none.fl_str_mv | 2022-01-31T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s22031092 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Addressing_Challenges_of_Distance_Learning_in_the_Pandemic_with_Edge_Intelligence_Enabled_Multicast_and_Caching_Solution/25679856 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Computer vision and multimedia computation Information systems edge intelligence video multicast distance learning eMBMS edge caching |
| dc.title.none.fl_str_mv | Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>The COVID-19 pandemic has affected the world socially and economically changing behaviors towards medical facilities, public gatherings, workplaces, and education. Educational institutes have been shutdown sporadically across the globe forcing teachers and students to adopt distance learning techniques. Due to the closure of educational institutes, work and learn from home methods have burdened the network resources and considerably decreased a viewer’s Quality of Experience (QoE). The situation calls for innovative techniques to handle the surging load of video traffic on cellular networks. In the scenario of distance learning, there is ample opportunity to realize multi-cast delivery instead of a conventional unicast. However, the existing 5G architecture does not support service-less multi-cast. In this article, we advance the case of Virtual Network Function (VNF) based service-less architecture for video multicast. Multicasting a video session for distance learning significantly lowers the burden on core and Radio Access Networks (RAN) as demonstrated by evaluation over a real-world dataset. We debate the role of Edge Intelligence (EI) for enabling multicast and edge caching for distance learning to complement the performance of the proposed VNF architecture. EI offers the determination of users that are part of a multicast session based on location, session, and cell information. Moreover, user preferences and network’s contextual information can differentiate between live and cached access patterns optimizing edge caching decisions. While exploring the opportunities of EI-enabled distance learning, we demonstrate a significant reduction in network operator resource utilization and an increase in user QoE for VNF based multicast transmission.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22031092" target="_blank">https://dx.doi.org/10.3390/s22031092</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6f6c8e58d96f71ea6529eb1697141c41 |
| identifier_str_mv | 10.3390/s22031092 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25679856 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching SolutionKashif Bilal (16896357)Junaid Shuja (18434070)Aiman Erbad (14150589)Waleed Alasmary (11741768)Eisa Alanazi (11741771)Abdullah Alourani (17721108)Information and computing sciencesComputer vision and multimedia computationInformation systemsedge intelligencevideo multicastdistance learningeMBMSedge caching<div><p>The COVID-19 pandemic has affected the world socially and economically changing behaviors towards medical facilities, public gatherings, workplaces, and education. Educational institutes have been shutdown sporadically across the globe forcing teachers and students to adopt distance learning techniques. Due to the closure of educational institutes, work and learn from home methods have burdened the network resources and considerably decreased a viewer’s Quality of Experience (QoE). The situation calls for innovative techniques to handle the surging load of video traffic on cellular networks. In the scenario of distance learning, there is ample opportunity to realize multi-cast delivery instead of a conventional unicast. However, the existing 5G architecture does not support service-less multi-cast. In this article, we advance the case of Virtual Network Function (VNF) based service-less architecture for video multicast. Multicasting a video session for distance learning significantly lowers the burden on core and Radio Access Networks (RAN) as demonstrated by evaluation over a real-world dataset. We debate the role of Edge Intelligence (EI) for enabling multicast and edge caching for distance learning to complement the performance of the proposed VNF architecture. EI offers the determination of users that are part of a multicast session based on location, session, and cell information. Moreover, user preferences and network’s contextual information can differentiate between live and cached access patterns optimizing edge caching decisions. While exploring the opportunities of EI-enabled distance learning, we demonstrate a significant reduction in network operator resource utilization and an increase in user QoE for VNF based multicast transmission.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22031092" target="_blank">https://dx.doi.org/10.3390/s22031092</a></p>2022-01-31T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s22031092https://figshare.com/articles/journal_contribution/Addressing_Challenges_of_Distance_Learning_in_the_Pandemic_with_Edge_Intelligence_Enabled_Multicast_and_Caching_Solution/25679856CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256798562022-01-31T03:00:00Z |
| spellingShingle | Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution Kashif Bilal (16896357) Information and computing sciences Computer vision and multimedia computation Information systems edge intelligence video multicast distance learning eMBMS edge caching |
| status_str | publishedVersion |
| title | Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution |
| title_full | Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution |
| title_fullStr | Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution |
| title_full_unstemmed | Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution |
| title_short | Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution |
| title_sort | Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution |
| topic | Information and computing sciences Computer vision and multimedia computation Information systems edge intelligence video multicast distance learning eMBMS edge caching |