Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework
<div><p>Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513518586298368 |
|---|---|
| author | Tayyabah Hasan (18427887) |
| author2 | Fahad Ahmad (18427890) Muhammad Rizwan (536386) Nasser Alshammari (8220885) Saad Awadh Alanazi (18427893) Iftikhar Hussain (8827335) Shahid Naseem (18427896) |
| author2_role | author author author author author author |
| author_facet | Tayyabah Hasan (18427887) Fahad Ahmad (18427890) Muhammad Rizwan (536386) Nasser Alshammari (8220885) Saad Awadh Alanazi (18427893) Iftikhar Hussain (8827335) Shahid Naseem (18427896) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tayyabah Hasan (18427887) Fahad Ahmad (18427890) Muhammad Rizwan (536386) Nasser Alshammari (8220885) Saad Awadh Alanazi (18427893) Iftikhar Hussain (8827335) Shahid Naseem (18427896) |
| dc.date.none.fl_str_mv | 2022-01-07T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1155/2022/6138434 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Edge_Caching_in_Fog-Based_Sensor_Networks_through_Deep_Learning-Associated_Quantum_Computing_Framework/25672503 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Mathematical sciences Applied mathematics Fog computing (FC) Sensor networks Mobile edge computing (MEC) Quality of Service (QoS) Internet of Things (IoT) Caching strategies |
| dc.title.none.fl_str_mv | Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. These videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm’s training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking “n” qubits that can be stored and execute 2n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. The experiments for content prioritization are conducted using Google Colab, and IBM’s Quantum Experience is considered to simulate the quantum phenomena.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Computational Intelligence and Neuroscience<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/10.1155/2022/6138434" target="_blank">https://dx.doi.org/10.1155/2022/6138434</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_871956b69142fe4bc691bf8317236894 |
| identifier_str_mv | 10.1155/2022/6138434 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25672503 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing FrameworkTayyabah Hasan (18427887)Fahad Ahmad (18427890)Muhammad Rizwan (536386)Nasser Alshammari (8220885)Saad Awadh Alanazi (18427893)Iftikhar Hussain (8827335)Shahid Naseem (18427896)Biomedical and clinical sciencesNeurosciencesMathematical sciencesApplied mathematicsFog computing (FC)Sensor networksMobile edge computing (MEC)Quality of Service (QoS)Internet of Things (IoT)Caching strategies<div><p>Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. These videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm’s training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking “n” qubits that can be stored and execute 2n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. The experiments for content prioritization are conducted using Google Colab, and IBM’s Quantum Experience is considered to simulate the quantum phenomena.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Computational Intelligence and Neuroscience<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/10.1155/2022/6138434" target="_blank">https://dx.doi.org/10.1155/2022/6138434</a></p>2022-01-07T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1155/2022/6138434https://figshare.com/articles/journal_contribution/Edge_Caching_in_Fog-Based_Sensor_Networks_through_Deep_Learning-Associated_Quantum_Computing_Framework/25672503CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256725032022-01-07T03:00:00Z |
| spellingShingle | Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework Tayyabah Hasan (18427887) Biomedical and clinical sciences Neurosciences Mathematical sciences Applied mathematics Fog computing (FC) Sensor networks Mobile edge computing (MEC) Quality of Service (QoS) Internet of Things (IoT) Caching strategies |
| status_str | publishedVersion |
| title | Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework |
| title_full | Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework |
| title_fullStr | Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework |
| title_full_unstemmed | Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework |
| title_short | Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework |
| title_sort | Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework |
| topic | Biomedical and clinical sciences Neurosciences Mathematical sciences Applied mathematics Fog computing (FC) Sensor networks Mobile edge computing (MEC) Quality of Service (QoS) Internet of Things (IoT) Caching strategies |