Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution

Bilal, Kashif; Shuja, Junaid; Erbad, Aiman; Alasmary, Waleed; Alanazi, Eisa; Alourani, Abdullah

doi:10.3390/s22031092

Cited by 15 publications

(4 citation statements)

References 32 publications

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“… 6 COVID-19 pandemic has also given rise to a range of technological innovations. Previous studies of emerging technologies in the pandemic centre on distance learning, 7 efficacy and accuracy of contact tracing, 8 COVID-19 risk estimation in education setting, 9 public perceptions and attitudes towards these digital tools, 10 , 11 or the discussion of liberty and autonomy under the expanding digital surveillance. 12 …”

Section: Biosecurity Health and Exclusionmentioning

confidence: 99%

Governing through big data: An ethnographic exploration of invisible lives in China's digital surveillance of the coronavirus disease 2019 pandemic

Zhang

Rafiq

2023

DIGITAL HEALTH

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Introduction/Background Since 2020, China has implemented unprecedented digital health surveillance over citizens and residents in response to the coronavirus disease 2019 pandemic. We explore the implementation of Health Code ( jiankang ma), a contract-tracing and risk assessment app for coronavirus disease 2019, in China. By engaging with the concept of ‘ocular ethics’, we ask why and how some populations become invisible in China's Health Code surveillance system. Methods This study used an ethnographic approach to critically examine the role of digital technology in the coronavirus disease 2019 pandemic governance. Three months of participant observation and 20 interviews were conducted to understand the design of Health Code and the situation of homeless population. Results We find that China's digital health surveillance during the coronavirus disease 2019 pandemic has failed to cover the homeless population, who either fail to access Health Code or find ways to avoid its mandatory health surveillance. We further summarize four problems resulting in their exclusion, including the loss of ID cards, access to smartphones and phone numbers, problematic design and elastic surveillance, and the neglect of homeless community's precarious living situation. Conclusion Situating our work in the literature on theories of surveillance and anthropology of pandemics, we argue that without recognizing the structural problems embedded in homelessness, a large number of poor and homeless migrants are rendered invisible in this data-driven health surveillance, which further pushes them into social exclusion.

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Section: Biosecurity Health and Exclusionmentioning

confidence: 99%

Governing through big data: An ethnographic exploration of invisible lives in China's digital surveillance of the coronavirus disease 2019 pandemic

Zhang

Rafiq

2023

DIGITAL HEALTH

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“…In a large-scale cache-enabled wireless network, Jiang et al [17] applied an iterative numerical algorithm to analyze and optimize caching and multicasting. Authors in [18][19][20][21] proposed different coding multicasting mechanisms to accommodate different scenarios. Nevertheless, for large-scale cooperative caching scenarios, how to improve resource efficiency by seeking a trade-off between edge caching and multicasting remains challenging.…”

Section: Introductionmentioning

confidence: 99%

COCAM: A Cooperative Video Edge Caching and Multicasting Approach Based on Multi-Agent Deep Reinforcement Learning

Shi

Fan

et al. 2023

Preprint

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The evolution of the Internet of Things technology has boosted the drastic increase in network traffic demand. Caching and multicasting are effective approaches to alleviate the backhaul burden of networks and reduce service latency. However, existing works do not jointly exploit the advantages of these two approaches. In this paper, we propose COCAM, a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning to minimize the transmission number in the multi-clouds scenario with limited storage capacity in each mobile edge server. Specifically, by integrating a cooperative transmission model with the caching model, we provide a concrete formulation of the joint problem. Then, We cast this decision-making problem as a multi-agent extension of the Markov decision process and propose a multi-agent actor-critic algorithm in which each agent learns a local caching policy and observes the information of neighboring agents as part of the environment. Finally, to validate the COCAM algorithm, we conduct extensive experiments on a real-world dataset. The results show that our proposed algorithm outperforms other baseline algorithms in terms of the number of video transmissions.

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“…Many AI-proposed techniques optimize content caching on edge servers [8][9][10]. The authors in [6] employ machine learning for caching the multi-media content and location prediction to enrich the customer experience [11].…”

Section: Introductionmentioning

confidence: 99%

A Time Pattern-Based Intelligent Cache Optimization Policy on Korea Advanced Research Network

Akbar¹,

Afaq²,

Song³

2023

Intelligent Automation &Amp; Soft Computing

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Data is growing quickly due to a significant increase in social media applications. Today, billions of people use an enormous amount of data to access the Internet. The backbone network experiences a substantial load as a result of an increase in users. Users in the same region or company frequently ask for similar material, especially on social media platforms. The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user. Applications that require relatively low latency can use Content Delivery Network (CDN) technology to meet their requirements. An edge and the data center constitute the CDN architecture. To fulfill requests from the edge and minimize the impact on the network, the requested content can be buffered closer to the user device. Which content should be kept on the edge is the primary concern. The cache policy has been optimized using various conventional and unconventional methods, but they have yet to include the timestamp beside a video request. The 24-h content request pattern was obtained from publicly available datasets. The popularity of a video is influenced by the time of day, as shown by a time-based video profile. We present a cache optimization method based on a time-based pattern of requests. The problem is described as a cache hit ratio maximization problem emphasizing a relevance score and machine learning model accuracy. A model predicts the video to be cached in the next time stamp, and the relevance score identifies the video to be removed from the cache. Afterwards, we gather the logs and generate the content requests using an extracted video request pattern. These logs are pre-processed to create a dataset divided into three-time slots per day. A Long short-term memory (LSTM) model is trained on this dataset to forecast the video at the next time interval. The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network (KOREN) infrastructure. Our findings demonstrate how adding time-based request patterns impacts the system by increasing the cache hit rate. To show the effectiveness of the proposed model, we compare the results with state-of-the-art techniques.

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Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution

Cited by 15 publications

References 32 publications

Governing through big data: An ethnographic exploration of invisible lives in China's digital surveillance of the coronavirus disease 2019 pandemic

Governing through big data: An ethnographic exploration of invisible lives in China's digital surveillance of the coronavirus disease 2019 pandemic

COCAM: A Cooperative Video Edge Caching and Multicasting Approach Based on Multi-Agent Deep Reinforcement Learning

A Time Pattern-Based Intelligent Cache Optimization Policy on Korea Advanced Research Network

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