{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:04:59Z","timestamp":1750309499198,"version":"3.41.0"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T00:00:00Z","timestamp":1712966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research Plan","award":["2021YFB2900100"],"award-info":[{"award-number":["2021YFB2900100"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["62072272, 62202263, 62372265, 62202262, 62302254, and 62272462"],"award-info":[{"award-number":["62072272, 62202263, 62372265, 62202262, 62302254, and 62272462"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>This article introduces LinkStream, a liquidity analysis system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements and network costs, and enables real-time analysis of large-scale video stream on limited edge devices. Second, it designed a set of liquidity information to describe the liquidity status in the oilfield. Finally, it uses object tracking technology to design a counting algorithm for the unique tubing object in the oilfield. We have deployed LinkStream in an oilfield in Iraq. LinkStream can perform real-time inference on over 200 video streams with acceptable resource overhead.<\/jats:p>","DOI":"10.1145\/3649222","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T12:28:35Z","timestamp":1709209715000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Liquidity Analysis System for Large-scale Video Streams in the Oilfield"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5791-1890","authenticated-orcid":false,"given":"Qiang","family":"Ma","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6215-2796","authenticated-orcid":false,"given":"Hao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3097-251X","authenticated-orcid":false,"given":"Zhe","family":"Hu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7195-5603","authenticated-orcid":false,"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4048-2684","authenticated-orcid":false,"given":"Zheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,4,13]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS56603.2022.00101"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/SECON52354.2021.9491582"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3092725"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.733"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3150401"},{"key":"e_1_3_1_8_2","unstructured":"Mengyue Geng Yaowei Wang Tao Xiang and Yonghong Tian. 2016. Deep transfer learning for person re-identification. CoRR abs\/1611.05244 (2016). http:\/\/arxiv.org\/abs\/1611.05244"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906396"},{"key":"e_1_3_1_10_2","unstructured":"Lingxiao He Xingyu Liao Wu Liu Xinchen Liu Peng Cheng and Tao Mei. 2020. FastReID: A pytorch toolbox for general instance re-identification. CoRR abs\/2006.02631. https:\/\/arxiv.org\/abs\/2006.02631"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2980070"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"e_1_3_1_14_2","unstructured":"Geoffrey E. Hinton Oriol Vinyals and Jeffrey Dean. 2015. Distilling the knowledge in a neural network. CoRR abs\/1503.02531. http:\/\/arxiv.org\/abs\/1503.02531"},{"key":"e_1_3_1_15_2","unstructured":"Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. CoRR abs\/1704.04861 (2017). http:\/\/arxiv.org\/abs\/1704.04861"},{"key":"e_1_3_1_16_2","first-page":"269","volume-title":"Proceedings of the 13th  \\(\\lbrace\\) USENIX \\(\\rbrace\\)  Symposium on Operating Systems Design and Implementation","author":"Hsieh Kevin","year":"2018","unstructured":"Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik, Shivaram Venkataraman, Paramvir Bahl, Matthai Philipose, Phillip B. Gibbons, and Onur Mutlu. 2018. Focus: Querying large video datasets with low latency and low cost. In Proceedings of the 13th \\(\\lbrace\\) USENIX \\(\\rbrace\\) Symposium on Operating Systems Design and Implementation. 269\u2013286."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/2999572.2999587"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/SEC50012.2020.00016"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230574"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.332"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00312"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2017.2707399"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2019.00148"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3300061.3300116"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/2987550.2987564"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8485905"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_48"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_9"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00632"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00142"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241563"},{"key":"e_1_3_1_34_2","first-page":"377","volume-title":"Proceedings of the 14th  \\(\\lbrace\\) USENIX \\(\\rbrace\\)  Symposium on Networked Systems Design and Implementation","author":"Zhang Haoyu","year":"2017","unstructured":"Haoyu Zhang, Ganesh Ananthanarayanan, Peter Bodik, Matthai Philipose, Paramvir Bahl, and Michael J. Freedman. 2017. Live video analytics at scale with approximation and delay-tolerance. In Proceedings of the 14th \\(\\lbrace\\) USENIX \\(\\rbrace\\) Symposium on Networked Systems Design and Implementation. 377\u2013392."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS54860.2022.00058"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00541"}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649222","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3649222","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:47Z","timestamp":1750295867000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,13]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5,31]]}},"alternative-id":["10.1145\/3649222"],"URL":"https:\/\/doi.org\/10.1145\/3649222","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"type":"print","value":"1550-4859"},{"type":"electronic","value":"1550-4867"}],"subject":[],"published":{"date-parts":[[2024,4,13]]},"assertion":[{"value":"2023-08-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-11","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}