Accuracy-Based Task Offloading and Resource Allocation for Edge Intelligence in IoT
Machine learning (ML) tasks in Internet of Things (IoT) are sensitive to task inference accuracy. In this letter, an ML task offloading scheme is proposed to minimize the total delay of task processing in an edge-intelligence-enabled IoT scenario, while guaranteeing the accuracy requirements of task...
Gespeichert in:
| Veröffentlicht in: | IEEE wireless communications letters 2022-02, Vol.11 (2), p.371-375 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 375 |
|---|---|
| container_issue | 2 |
| container_start_page | 371 |
| container_title | IEEE wireless communications letters |
| container_volume | 11 |
| creator | Fan, Wenhao Chen, Zeyu Su, Yi Wu, Fan Tang, Bihua Liu, Yuan'an |
| description | Machine learning (ML) tasks in Internet of Things (IoT) are sensitive to task inference accuracy. In this letter, an ML task offloading scheme is proposed to minimize the total delay of task processing in an edge-intelligence-enabled IoT scenario, while guaranteeing the accuracy requirements of tasks, and taking into account the multiple attributes of tasks, task inference accuracy, and impact of error inference on task processing delay. The problem of wireless channel allocation, and computing resource allocation is modeled along with the task offloading. Considering the high complexity of the optimization problem, we design an algorithm which decomposes the problem into a computing resource allocation sub-problem and a task offloading and channel allocation sub-problem, and then solves them separately. In extensive simulations, the superiority of our scheme is demonstrated in comparisons with 4 other schemes. |
| doi_str_mv | 10.1109/LWC.2021.3128911 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9619485</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9619485</ieee_id><sourcerecordid>2626973200</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-2e25c6ea916c7c9d24e56f7235f1d79ef38ad4a234802bca0d9053ff9287a9be3</originalsourceid><addsrcrecordid>eNo9kE1LAzEQQIMoWLR3wUvA89Zk0s1ujrVULRQKWvEY0mRStq6bmmwP_femtHQuM4c3X4-QB85GnDP1vPiejoABHwkOteL8igyASyhAjMvrSy2qWzJMactySMaB1wPyObF2H409FC8moaMrk37o0vs2GNd0G2o6Rz8whX20SCdtG6zpm9BRHyKduQ3Seddj2zYb7DLQdHQeVvfkxps24fCc78jX62w1fS8Wy7f5dLIoLCjeF4BQWolGcWkrqxyMsZS-AlF67iqFXtTGjU3-oWawtoY5xUrhvYK6MmqN4o48nebuYvjbY-r1Nt_Z5ZUaJEhVCWAsU-xE2RhSiuj1Lja_Jh40Z_poT2d7-mhPn-3llsdTS4OIF1xJrsZ1Kf4BmLBpqQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2626973200</pqid></control><display><type>article</type><title>Accuracy-Based Task Offloading and Resource Allocation for Edge Intelligence in IoT</title><source>IEEE Electronic Library (IEL)</source><creator>Fan, Wenhao ; Chen, Zeyu ; Su, Yi ; Wu, Fan ; Tang, Bihua ; Liu, Yuan'an</creator><creatorcontrib>Fan, Wenhao ; Chen, Zeyu ; Su, Yi ; Wu, Fan ; Tang, Bihua ; Liu, Yuan'an</creatorcontrib><description>Machine learning (ML) tasks in Internet of Things (IoT) are sensitive to task inference accuracy. In this letter, an ML task offloading scheme is proposed to minimize the total delay of task processing in an edge-intelligence-enabled IoT scenario, while guaranteeing the accuracy requirements of tasks, and taking into account the multiple attributes of tasks, task inference accuracy, and impact of error inference on task processing delay. The problem of wireless channel allocation, and computing resource allocation is modeled along with the task offloading. Considering the high complexity of the optimization problem, we design an algorithm which decomposes the problem into a computing resource allocation sub-problem and a task offloading and channel allocation sub-problem, and then solves them separately. In extensive simulations, the superiority of our scheme is demonstrated in comparisons with 4 other schemes.</description><identifier>ISSN: 2162-2337</identifier><identifier>EISSN: 2162-2345</identifier><identifier>DOI: 10.1109/LWC.2021.3128911</identifier><identifier>CODEN: IWCLAF</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Accuracy ; Algorithms ; cloud computing ; Computation offloading ; Computational modeling ; Delays ; Design optimization ; edge computing ; Edge intelligence ; Inference ; Intelligence ; Internet of Things ; IoT ; Machine learning ; Resource allocation ; Resource management ; Task analysis ; task offloading ; Wireless communication ; Wireless sensor networks</subject><ispartof>IEEE wireless communications letters, 2022-02, Vol.11 (2), p.371-375</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-2e25c6ea916c7c9d24e56f7235f1d79ef38ad4a234802bca0d9053ff9287a9be3</citedby><cites>FETCH-LOGICAL-c291t-2e25c6ea916c7c9d24e56f7235f1d79ef38ad4a234802bca0d9053ff9287a9be3</cites><orcidid>0000-0002-1286-7141 ; 0000-0002-5834-8240 ; 0000-0001-5288-8708 ; 0000-0002-8076-1904 ; 0000-0002-0431-9903</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9619485$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9619485$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Fan, Wenhao</creatorcontrib><creatorcontrib>Chen, Zeyu</creatorcontrib><creatorcontrib>Su, Yi</creatorcontrib><creatorcontrib>Wu, Fan</creatorcontrib><creatorcontrib>Tang, Bihua</creatorcontrib><creatorcontrib>Liu, Yuan'an</creatorcontrib><title>Accuracy-Based Task Offloading and Resource Allocation for Edge Intelligence in IoT</title><title>IEEE wireless communications letters</title><addtitle>LWC</addtitle><description>Machine learning (ML) tasks in Internet of Things (IoT) are sensitive to task inference accuracy. In this letter, an ML task offloading scheme is proposed to minimize the total delay of task processing in an edge-intelligence-enabled IoT scenario, while guaranteeing the accuracy requirements of tasks, and taking into account the multiple attributes of tasks, task inference accuracy, and impact of error inference on task processing delay. The problem of wireless channel allocation, and computing resource allocation is modeled along with the task offloading. Considering the high complexity of the optimization problem, we design an algorithm which decomposes the problem into a computing resource allocation sub-problem and a task offloading and channel allocation sub-problem, and then solves them separately. In extensive simulations, the superiority of our scheme is demonstrated in comparisons with 4 other schemes.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>cloud computing</subject><subject>Computation offloading</subject><subject>Computational modeling</subject><subject>Delays</subject><subject>Design optimization</subject><subject>edge computing</subject><subject>Edge intelligence</subject><subject>Inference</subject><subject>Intelligence</subject><subject>Internet of Things</subject><subject>IoT</subject><subject>Machine learning</subject><subject>Resource allocation</subject><subject>Resource management</subject><subject>Task analysis</subject><subject>task offloading</subject><subject>Wireless communication</subject><subject>Wireless sensor networks</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQQIMoWLR3wUvA89Zk0s1ujrVULRQKWvEY0mRStq6bmmwP_femtHQuM4c3X4-QB85GnDP1vPiejoABHwkOteL8igyASyhAjMvrSy2qWzJMactySMaB1wPyObF2H409FC8moaMrk37o0vs2GNd0G2o6Rz8whX20SCdtG6zpm9BRHyKduQ3Seddj2zYb7DLQdHQeVvfkxps24fCc78jX62w1fS8Wy7f5dLIoLCjeF4BQWolGcWkrqxyMsZS-AlF67iqFXtTGjU3-oWawtoY5xUrhvYK6MmqN4o48nebuYvjbY-r1Nt_Z5ZUaJEhVCWAsU-xE2RhSiuj1Lja_Jh40Z_poT2d7-mhPn-3llsdTS4OIF1xJrsZ1Kf4BmLBpqQ</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Fan, Wenhao</creator><creator>Chen, Zeyu</creator><creator>Su, Yi</creator><creator>Wu, Fan</creator><creator>Tang, Bihua</creator><creator>Liu, Yuan'an</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-1286-7141</orcidid><orcidid>https://orcid.org/0000-0002-5834-8240</orcidid><orcidid>https://orcid.org/0000-0001-5288-8708</orcidid><orcidid>https://orcid.org/0000-0002-8076-1904</orcidid><orcidid>https://orcid.org/0000-0002-0431-9903</orcidid></search><sort><creationdate>20220201</creationdate><title>Accuracy-Based Task Offloading and Resource Allocation for Edge Intelligence in IoT</title><author>Fan, Wenhao ; Chen, Zeyu ; Su, Yi ; Wu, Fan ; Tang, Bihua ; Liu, Yuan'an</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-2e25c6ea916c7c9d24e56f7235f1d79ef38ad4a234802bca0d9053ff9287a9be3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>cloud computing</topic><topic>Computation offloading</topic><topic>Computational modeling</topic><topic>Delays</topic><topic>Design optimization</topic><topic>edge computing</topic><topic>Edge intelligence</topic><topic>Inference</topic><topic>Intelligence</topic><topic>Internet of Things</topic><topic>IoT</topic><topic>Machine learning</topic><topic>Resource allocation</topic><topic>Resource management</topic><topic>Task analysis</topic><topic>task offloading</topic><topic>Wireless communication</topic><topic>Wireless sensor networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fan, Wenhao</creatorcontrib><creatorcontrib>Chen, Zeyu</creatorcontrib><creatorcontrib>Su, Yi</creatorcontrib><creatorcontrib>Wu, Fan</creatorcontrib><creatorcontrib>Tang, Bihua</creatorcontrib><creatorcontrib>Liu, Yuan'an</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE wireless communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fan, Wenhao</au><au>Chen, Zeyu</au><au>Su, Yi</au><au>Wu, Fan</au><au>Tang, Bihua</au><au>Liu, Yuan'an</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accuracy-Based Task Offloading and Resource Allocation for Edge Intelligence in IoT</atitle><jtitle>IEEE wireless communications letters</jtitle><stitle>LWC</stitle><date>2022-02-01</date><risdate>2022</risdate><volume>11</volume><issue>2</issue><spage>371</spage><epage>375</epage><pages>371-375</pages><issn>2162-2337</issn><eissn>2162-2345</eissn><coden>IWCLAF</coden><abstract>Machine learning (ML) tasks in Internet of Things (IoT) are sensitive to task inference accuracy. In this letter, an ML task offloading scheme is proposed to minimize the total delay of task processing in an edge-intelligence-enabled IoT scenario, while guaranteeing the accuracy requirements of tasks, and taking into account the multiple attributes of tasks, task inference accuracy, and impact of error inference on task processing delay. The problem of wireless channel allocation, and computing resource allocation is modeled along with the task offloading. Considering the high complexity of the optimization problem, we design an algorithm which decomposes the problem into a computing resource allocation sub-problem and a task offloading and channel allocation sub-problem, and then solves them separately. In extensive simulations, the superiority of our scheme is demonstrated in comparisons with 4 other schemes.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LWC.2021.3128911</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-1286-7141</orcidid><orcidid>https://orcid.org/0000-0002-5834-8240</orcidid><orcidid>https://orcid.org/0000-0001-5288-8708</orcidid><orcidid>https://orcid.org/0000-0002-8076-1904</orcidid><orcidid>https://orcid.org/0000-0002-0431-9903</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2162-2337 |
| ispartof | IEEE wireless communications letters, 2022-02, Vol.11 (2), p.371-375 |
| issn | 2162-2337 2162-2345 |
| language | eng |
| recordid | cdi_ieee_primary_9619485 |
| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Algorithms cloud computing Computation offloading Computational modeling Delays Design optimization edge computing Edge intelligence Inference Intelligence Internet of Things IoT Machine learning Resource allocation Resource management Task analysis task offloading Wireless communication Wireless sensor networks |
| title | Accuracy-Based Task Offloading and Resource Allocation for Edge Intelligence in IoT |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T17%3A46%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Accuracy-Based%20Task%20Offloading%20and%20Resource%20Allocation%20for%20Edge%20Intelligence%20in%20IoT&rft.jtitle=IEEE%20wireless%20communications%20letters&rft.au=Fan,%20Wenhao&rft.date=2022-02-01&rft.volume=11&rft.issue=2&rft.spage=371&rft.epage=375&rft.pages=371-375&rft.issn=2162-2337&rft.eissn=2162-2345&rft.coden=IWCLAF&rft_id=info:doi/10.1109/LWC.2021.3128911&rft_dat=%3Cproquest_RIE%3E2626973200%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2626973200&rft_id=info:pmid/&rft_ieee_id=9619485&rfr_iscdi=true |