{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:47:10Z","timestamp":1764175630618,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T00:00:00Z","timestamp":1699747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SRCco Inc","award":["2023-JU-3134"],"award-info":[{"award-number":["2023-JU-3134"]}]},{"DOI":"10.13039\/100005144","name":"Qualcomm","doi-asserted-by":"publisher","award":["Qualcomm Innovation Fellowship"],"award-info":[{"award-number":["Qualcomm Innovation Fellowship"]}],"id":[{"id":"10.13039\/100005144","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CAREER Award 2239440","Proto-OKN Award 2333790"],"award-info":[{"award-number":["CAREER Award 2239440","Proto-OKN Award 2333790"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH (National Institutes of Health)","doi-asserted-by":"publisher","award":["Bridge2AI Center Program Award 1U54HG012510-01"],"award-info":[{"award-number":["Bridge2AI Center Program Award 1U54HG012510-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,12]]},"DOI":"10.1145\/3625687.3625811","type":"proceedings-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T12:07:18Z","timestamp":1714133238000},"page":"83-96","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Physics-Informed Data Denoising for Real-Life Sensing Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8908-1307","authenticated-orcid":false,"given":"Xiyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"University of California, San Diego, La Jolla, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5614-5922","authenticated-orcid":false,"given":"Xiaohan","family":"Fu","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5493-3322","authenticated-orcid":false,"given":"Diyan","family":"Teng","sequence":"additional","affiliation":[{"name":"Qualcomm, Santa Clara, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9698-5884","authenticated-orcid":false,"given":"Chengyu","family":"Dong","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0441-3299","authenticated-orcid":false,"given":"Keerthivasan","family":"Vijayakumar","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3562-5794","authenticated-orcid":false,"given":"Jiayun","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8705-7485","authenticated-orcid":false,"given":"Ranak Roy","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7162-017X","authenticated-orcid":false,"given":"Junsheng","family":"Han","sequence":"additional","affiliation":[{"name":"Qualcomm, Santa Clara, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-6043","authenticated-orcid":false,"given":"Dezhi","family":"Hong","sequence":"additional","affiliation":[{"name":"Amazon, Seattle, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6329-2635","authenticated-orcid":false,"given":"Rashmi","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"Qualcomm, Santa Clara, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7249-4404","authenticated-orcid":false,"given":"Jingbo","family":"Shang","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6489-7633","authenticated-orcid":false,"given":"Rajesh K.","family":"Gupta","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, United States of America"}]}],"member":"320","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"ams OSRAM AG. 2016. CCS811 Datasheet. https:\/\/cdn.sparkfun.com\/assets\/learn_tutorials\/1\/4\/3\/CCS811_Datasheet-DS000459.pdf."},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments. 1--10","author":"Arief-Ang Irvan B","year":"2017","unstructured":"Irvan B Arief-Ang, Flora D Salim, and Margaret Hamilton. 2017. Da-hoc: semi-supervised domain adaptation for room occupancy prediction using co2 sensor data. In Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments. 1--10."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the 1st ACM conference on embedded systems for energy-efficient buildings. 168--171","author":"Beltran Alex","year":"2014","unstructured":"Alex Beltran and Alberto E Cerpa. 2014. Optimal HVAC building control with occupancy prediction. In Proceedings of the 1st ACM conference on embedded systems for energy-efficient buildings. 168--171."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3563357.3564050"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12102"},{"key":"e_1_3_2_1_6_1","volume-title":"Wei Wang, Andrew Markham, and Niki Trigoni.","author":"Chen Changhao","year":"2018","unstructured":"Changhao Chen, Peijun Zhao, Chris Xiaoxuan Lu, Wei Wang, Andrew Markham, and Niki Trigoni. 2018. Oxiod: The dataset for deep inertial odometry. arXiv preprint arXiv:1809.07491 (2018)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430393"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3267305.3274179"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5194\/amt-12-1441-2019"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5194\/amt-12-1441-2019"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/JSEN.2017.2767383","article-title":"Insights into IoT data and an innovative DWT-based technique to denoise sensor signals","volume":"18","author":"Lopes De Faria Maria Lu\u00edsa","year":"2017","unstructured":"Maria Lu\u00edsa Lopes De Faria, Carlos Eduardo Cugnasca, and Jos\u00e9 Roberto Almeida Amazonas. 2017. Insights into IoT data and an innovative DWT-based technique to denoise sensor signals. IEEE Sensors Journal 18, 1 (2017), 237--247.","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460418.3480415"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 12th international conference on Information processing in sensor networks. 7--18","author":"Faulkner Matthew","year":"2013","unstructured":"Matthew Faulkner, Annie H Liu, and Andreas Krause. 2013. A fresh perspective: Learning to sparsify for detection in massive noisy sensor networks. In Proceedings of the 12th international conference on Information processing in sensor networks. 7--18."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3408308.3431113"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0054312"},{"key":"e_1_3_2_1_16_1","volume-title":"Combining generative and discriminative models for hybrid inference. Advances in Neural Information Processing Systems 32","author":"Satorras Victor Garcia","year":"2019","unstructured":"Victor Garcia Satorras, Zeynep Akata, and Max Welling. 2019. Combining generative and discriminative models for hybrid inference. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_17_1","volume-title":"Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications. arXiv preprint arXiv:2211.08064","author":"Hao Zhongkai","year":"2022","unstructured":"Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, and Jun Zhu. 2022. Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications. arXiv preprint arXiv:2211.08064 (2022)."},{"key":"e_1_3_2_1_18_1","volume-title":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. 532--537","author":"He Lixing","year":"2020","unstructured":"Lixing He, Carlos Ruiz, Mostafa Mirshekari, and Shijia Pan. 2020. Scsv2: physics-informed self-configuration sensing through vision and vibration context modeling. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. 532--537."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196860"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 14781--14790","author":"Huang Tao","year":"2021","unstructured":"Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. 2021. Neighbor2neighbor: Self-supervised denoising from single noisy images. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 14781--14790."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3356250.3360032"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"GE Karniadakis IG Kevrekidis L Lu P Perdikaris S Wang and L Yang. 2021. Physics-informed machine learning: Nature Reviews Physics. (2021).","DOI":"10.1038\/s42254-021-00314-5"},{"key":"e_1_3_2_1_23_1","volume-title":"Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems. arXiv preprint arXiv:2210.16215","author":"Kelshaw Daniel","year":"2022","unstructured":"Daniel Kelshaw and Luca Magri. 2022. Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems. arXiv preprint arXiv:2210.16215 (2022)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3448619"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3486611.3488727"},{"key":"e_1_3_2_1_26_1","volume-title":"Noise2Noise: Learning image restoration without clean data. arXiv preprint arXiv:1803.04189","author":"Lehtinen Jaakko","year":"2018","unstructured":"Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. 2018. Noise2Noise: Learning image restoration without clean data. arXiv preprint arXiv:1803.04189 (2018)."},{"key":"e_1_3_2_1_27_1","volume-title":"Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems. 277--290","author":"Li Shuheng","year":"2022","unstructured":"Shuheng Li, Jingbo Shang, Rajesh K Gupta, and Dezhi Hong. 2022. SQEE: A Machine Perception Approach to Sensing Quality Evaluation at the Edge by Uncertainty Quantification. In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems. 277--290."},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems. 120--133","author":"Liu Miaomiao","year":"2022","unstructured":"Miaomiao Liu, Sikai Yang, Wyssanie Chomsin, and Wan Du. 2022. Real-time tracking of smartwatch orientation and location by multitask learning. In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems. 120--133."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485945"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3007421"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3307334.3326109"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430776"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3412382.3458255"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01208"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. 11--20","author":"Nagarathinam Srinarayana","year":"2022","unstructured":"Srinarayana Nagarathinam, Yashovardhan S Chati, Malini Pooni Venkat, and Arunchandar Vasan. 2022. PACMAN: physics-aware control MANager for HVAC. In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. 11--20."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2821650.2821655"},{"key":"e_1_3_2_1_37_1","unstructured":"Wisconsin Department of Health Service. 2023. Carbon Dioxide. https:\/\/www.dhs.wisconsin.gov\/chemical\/carbondioxide.htm."},{"key":"e_1_3_2_1_38_1","volume-title":"Empirical mode decomposition technique with conditional mutual information for denoising operational sensor data","author":"Omitaomu Olufemi A","year":"2011","unstructured":"Olufemi A Omitaomu, Vladimir A Protopopescu, and Auroop R Ganguly. 2011. Empirical mode decomposition technique with conditional mutual information for denoising operational sensor data. IEEE sensors journal 11, 10 (2011), 2565--2575."},{"key":"e_1_3_2_1_39_1","first-page":"9","article-title":"Photoacoustic-based gas sensing: A review","volume":"20","author":"Palzer Stefan","year":"2020","unstructured":"Stefan Palzer. 2020. Photoacoustic-based gas sensing: A review. Sensors (Basel) 20, 9 (May 2020), 2745.","journal-title":"Sensors (Basel)"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131672.3131690"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v39i1.2776"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1034\/j.1600-0668.2003.00189.x"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241582"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906407"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i7.16763"},{"key":"e_1_3_2_1_47_1","volume-title":"Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems. 1077--1082","author":"Sun Yifei","year":"2022","unstructured":"Yifei Sun, Yuxuan Liu, Ziteng Wang, Xiaolei Qu, Dezhi Zheng, and Xinlei Chen. 2022. C-RIDGE: Indoor CO2 Data Collection System for Large Venues Based on prior Knowledge. In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems. 1077--1082."},{"key":"e_1_3_2_1_48_1","first-page":"14809","article-title":"Physics-integrated variational autoencoders for robust and interpretable generative modeling","volume":"34","author":"Takeishi Naoya","year":"2021","unstructured":"Naoya Takeishi and Alexandros Kalousis. 2021. Physics-integrated variational autoencoders for robust and interpretable generative modeling. Advances in Neural Information Processing Systems 34 (2021), 14809--14821.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_49_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 9446--9454","author":"Ulyanov Dmitry","year":"2018","unstructured":"Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2018. Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition. 9446--9454."},{"key":"e_1_3_2_1_50_1","unstructured":"CO2Meter.com. 2022. K30 Datasheet. https:\/\/cdn.shopify.com\/s\/files\/1\/0019\/5952\/files\/DS-SenseAir-K30-CO2-Sensor-Revised-061722_96d4f229-643c-45b0-95a6-24efa252490d.pdf?v=1672160950."},{"key":"e_1_3_2_1_51_1","volume-title":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 7098--7102","author":"Wang Kai","year":"2021","unstructured":"Kai Wang, Bengbeng He, and Wei-Ping Zhu. 2021. TSTNN: Two-stage transformer based neural network for speech enhancement in the time domain. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 7098--7102."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403198"},{"key":"e_1_3_2_1_53_1","volume-title":"Physics-guided deep learning for dynamical systems: A survey. arXiv preprint arXiv:2107.01272","author":"Wang Rui","year":"2021","unstructured":"Rui Wang and Rose Yu. 2021. Physics-guided deep learning for dynamical systems: A survey. arXiv preprint arXiv:2107.01272 (2021)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568504"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3408308.3431124"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1109\/TCST.2014.2384002","article-title":"Modeling and estimation of the humans' effect on the CO 2 dynamics inside a conference room","volume":"23","author":"Weekly Kevin","year":"2015","unstructured":"Kevin Weekly, Nikolaos Bekiaris-Liberis, Ming Jin, and Alexandre M Bayen. 2015. Modeling and estimation of the humans' effect on the CO 2 dynamics inside a conference room. IEEE Transactions on Control Systems Technology 23, 5 (2015), 1770--1781.","journal-title":"IEEE Transactions on Control Systems Technology"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3026622"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3563357.3564064"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_38"},{"key":"e_1_3_2_1_60_1","first-page":"17157","article-title":"Learning physics constrained dynamics using autoencoders","volume":"35","author":"Yang Tsung-Yen","year":"2022","unstructured":"Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, and Peter J Ramadge. 2022. Learning physics constrained dynamics using autoencoders. Advances in Neural Information Processing Systems 35 (2022), 17157--17172.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_61_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 13167--13178","author":"Yi Xinyu","year":"2022","unstructured":"Xinyu Yi, Yuxiao Zhou, Marc Habermann, Soshi Shimada, Vladislav Golyanik, Christian Theobalt, and Feng Xu. 2022. Physical inertial poser (pip): Physics-aware real-time human motion tracking from sparse inertial sensors. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 13167--13178."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3360322.3360861"},{"key":"e_1_3_2_1_63_1","volume-title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising","author":"Zhang Kai","year":"2017","unstructured":"Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. 2017. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing 26, 7 (2017), 3142--3155."},{"key":"e_1_3_2_1_64_1","volume-title":"Dezhi Hong, Rajesh K Gupta, and Jingbo Shang.","author":"Zhang Xiyuan","year":"2023","unstructured":"Xiyuan Zhang, Ranak Roy Chowdhury, Dezhi Hong, Rajesh K Gupta, and Jingbo Shang. 2023. Modeling Label Semantics Improves Activity Recognition. arXiv preprint arXiv:2301.03462 (2023)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131672.3131695"},{"key":"e_1_3_2_1_66_1","volume-title":"Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images. Advances in neural information processing systems 32","author":"Zhussip Magauiya","year":"2019","unstructured":"Magauiya Zhussip, Shakarim Soltanayev, and Se Young Chun. 2019. Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images. Advances in neural information processing systems 32 (2019)."}],"event":{"name":"SenSys '23: 21st ACM Conference on Embedded Networked Sensor Systems","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture","SIGBED ACM Special Interest Group on Embedded Systems","SIGMETRICS ACM Special Interest Group on Measurement and Evaluation","SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems"],"location":"Istanbul Turkiye","acronym":"SenSys '23"},"container-title":["Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3625687.3625811","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3625687.3625811","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:11Z","timestamp":1750182551000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3625687.3625811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,12]]},"references-count":66,"alternative-id":["10.1145\/3625687.3625811","10.1145\/3625687"],"URL":"https:\/\/doi.org\/10.1145\/3625687.3625811","relation":{},"subject":[],"published":{"date-parts":[[2023,11,12]]},"assertion":[{"value":"2024-04-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}