{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:50:11Z","timestamp":1780764611258,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":76,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T00:00:00Z","timestamp":1696204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2112778"],"award-info":[{"award-number":["2112778"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Cisco Research"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,2]]},"DOI":"10.1145\/3570361.3592531","type":"proceedings-article","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T22:39:36Z","timestamp":1696113576000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["AccuMO: Accuracy-Centric Multitask Offloading in Edge-Assisted Mobile Augmented Reality"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8887-1698","authenticated-orcid":false,"given":"Z. Jonny","family":"Kong","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9597-9968","authenticated-orcid":false,"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3091-8894","authenticated-orcid":false,"given":"Jiayi","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1136-9909","authenticated-orcid":false,"given":"Y. Charlie","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,2]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2019. Cloud-native computing platform. https:\/\/puzl.ee\/resources\/gpu.  2019. Cloud-native computing platform. https:\/\/puzl.ee\/resources\/gpu."},{"key":"e_1_3_2_1_2_1","unstructured":"MathWorks 2022. MATLAB Coder. MathWorks Natick USA.  MathWorks 2022. MATLAB Coder. MathWorks Natick USA."},{"key":"e_1_3_2_1_3_1","unstructured":"Tencent 2022. ncnn: a high-performance neural network inference computing framework optimized for mobile platforms. Tencent Shenzhen China.  Tencent 2022. ncnn: a high-performance neural network inference computing framework optimized for mobile platforms. Tencent Shenzhen China."},{"key":"e_1_3_2_1_4_1","unstructured":"MathWorks 2022. Sensor Fusion and Tracking Toolbox. MathWorks Natick USA.  MathWorks 2022. Sensor Fusion and Tracking Toolbox. MathWorks Natick USA."},{"key":"e_1_3_2_1_5_1","volume-title":"Retrieved","author":"Ahmadyan Adel","year":"2020","unstructured":"Adel Ahmadyan and Tingbo Hou . 2020 . Real-Time 3D Object Detection on Mobile Devices with MediaPipe . Retrieved July 13, 2022 from https:\/\/ai.googleblog.com\/2020\/03\/real-time-3d-object-detection-on-mobile.html Adel Ahmadyan and Tingbo Hou. 2020. Real-Time 3D Object Detection on Mobile Devices with MediaPipe. Retrieved July 13, 2022 from https:\/\/ai.googleblog.com\/2020\/03\/real-time-3d-object-detection-on-mobile.html"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3487552.3487863"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3356250.3360044"},{"key":"e_1_3_2_1_8_1","volume-title":"Retrieved","year":"2022","unstructured":"arcoredepth 2022 . Depth adds realism . Retrieved Jan 25, 2023 from https:\/\/developers.google.com\/ar\/develop\/depth arcoredepth 2022. Depth adds realism. Retrieved Jan 25, 2023 from https:\/\/developers.google.com\/ar\/develop\/depth"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467078"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4009--4018","author":"Bhat Shariq Farooq","year":"2021","unstructured":"Shariq Farooq Bhat , Ibraheem Alhashim , and Peter Wonka . 2021 . Adabins: Depth estimation using adaptive bins . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4009--4018 . Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka. 2021. Adabins: Depth estimation using adaptive bins. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4009--4018."},{"key":"e_1_3_2_1_11_1","unstructured":"D. Birkes and Y. Dodge. 2011. Alternative Methods of Regression. Wiley. https:\/\/books.google.com\/books?id=CIedErj0HKcC  D. Birkes and Y. Dodge. 2011. Alternative Methods of Regression. Wiley. https:\/\/books.google.com\/books?id=CIedErj0HKcC"},{"key":"e_1_3_2_1_12_1","unstructured":"BMI263 2022. Inertial Measurement Unit BMI263.  BMI263 2022. Inertial Measurement Unit BMI263."},{"key":"e_1_3_2_1_13_1","volume-title":"YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv preprint arXiv:2004.10934","author":"Bochkovskiy Alexey","year":"2020","unstructured":"Alexey Bochkovskiy , Chien-Yao Wang , and Hong-Yuan Mark Liao . 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv preprint arXiv:2004.10934 ( 2020 ). Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv preprint arXiv:2004.10934 (2020)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759611"},{"key":"e_1_3_2_1_15_1","unstructured":"E.F. Camacho and C.B. Alba. 2013. Model Predictive Control. Springer London. https:\/\/books.google.com\/books?id=tXZDAAAAQBAJ  E.F. Camacho and C.B. Alba. 2013. Model Predictive Control. Springer London. https:\/\/books.google.com\/books?id=tXZDAAAAQBAJ"},{"key":"e_1_3_2_1_16_1","volume-title":"Retrieved","author":"Chandrasekaran Shankar","year":"2020","unstructured":"Shankar Chandrasekaran . 2020 . Ride the Fast Lane to AI Productivity with Multi-Instance GPUs . Retrieved April 23, 2022 from https:\/\/blogs.nvidia.com\/blog\/2020\/05\/14\/multi-instance-gpus\/ Shankar Chandrasekaran. 2020. Ride the Fast Lane to AI Productivity with Multi-Instance GPUs. Retrieved April 23, 2022 from https:\/\/blogs.nvidia.com\/blog\/2020\/05\/14\/multi-instance-gpus\/"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274783.3274834"},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. 155--168","author":"Yu-Han Chen Tiffany","year":"2015","unstructured":"Tiffany Yu-Han Chen , Lenin Ravindranath , Shuo Deng , Paramvir Bahl , and Hari Balakrishnan . 2015 . Glimpse: Continuous, real-time object recognition on mobile devices . In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. 155--168 . Tiffany Yu-Han Chen, Lenin Ravindranath, Shuo Deng, Paramvir Bahl, and Hari Balakrishnan. 2015. Glimpse: Continuous, real-time object recognition on mobile devices. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. 155--168."},{"key":"e_1_3_2_1_19_1","volume-title":"Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing. In 2022 USENIX Annual Technical Conference (USENIX ATC 22)","author":"Choi Seungbeom","year":"2022","unstructured":"Seungbeom Choi , Sunho Lee , Yeonjae Kim , Jongse Park , Youngjin Kwon , and Jaehyuk Huh . 2022 . Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing. In 2022 USENIX Annual Technical Conference (USENIX ATC 22) . USENIX Association, Carlsbad, CA, 199--216. https:\/\/www.usenix.org\/conference\/atc22\/presentation\/choi-seungbeom Seungbeom Choi, Sunho Lee, Yeonjae Kim, Jongse Park, Youngjin Kwon, and Jaehyuk Huh. 2022. Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing. In 2022 USENIX Annual Technical Conference (USENIX ATC 22). USENIX Association, Carlsbad, CA, 199--216. https:\/\/www.usenix.org\/conference\/atc22\/presentation\/choi-seungbeom"},{"key":"e_1_3_2_1_20_1","unstructured":"Jifeng Dai Yi Li Kaiming He and Jian Sun. 2016. R-FCN: Object detection via region-based fully convolutional networks. In Advances in neural information processing systems. 379--387.  Jifeng Dai Yi Li Kaiming He and Jian Sun. 2016. R-FCN: Object detection via region-based fully convolutional networks. In Advances in neural information processing systems. 379--387."},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the 1st Annual Conference on Robot Learning. 1--16","author":"Dosovitskiy Alexey","year":"2017","unstructured":"Alexey Dosovitskiy , German Ros , Felipe Codevilla , Antonio Lopez , and Vladlen Koltun . 2017 . CARLA: An Open Urban Driving Simulator . In Proceedings of the 1st Annual Conference on Robot Learning. 1--16 . Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. 2017. CARLA: An Open Urban Driving Simulator. In Proceedings of the 1st Annual Conference on Robot Learning. 1--16."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.94"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387514.3405887"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2947893"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586328"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"e_1_3_2_1_27_1","volume-title":"Can 5G mmWave Support Multi-user AR?","author":"Ghoshal Moinak","unstructured":"Moinak Ghoshal , Pranab Dash , Zhaoning Kong , Qiang Xu , Y. Charlie Hu , Dimitrios Koutsonikolas , and Yuanjie Li. 2022. Can 5G mmWave Support Multi-user AR? . In Passive and Active Measurement, Oliver Hohlfeld, Giovane Moura, and Cristel Pelsser (Eds.). Springer International Publishing , Cham , 180--196. Moinak Ghoshal, Pranab Dash, Zhaoning Kong, Qiang Xu, Y. Charlie Hu, Dimitrios Koutsonikolas, and Yuanjie Li. 2022. Can 5G mmWave Support Multi-user AR?. In Passive and Active Measurement, Oliver Hohlfeld, Giovane Moura, and Cristel Pelsser (Eds.). Springer International Publishing, Cham, 180--196."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_3_2_1_29_1","unstructured":"Google Brain Team. 2022. TensorFlow Lite. Mountain View USA.  Google Brain Team. 2022. TensorFlow Lite. Mountain View USA."},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the 14th USENIX Conference on Operating Systems Design and Implementation (OSDI'20)","author":"Gujarati Arpan","year":"2020","unstructured":"Arpan Gujarati , Reza Karimi , Safya Alzayat , Wei Hao , Antoine Kaufmann , Ymir Vigfusson , and Jonathan Mace . 2020 . Serving DNNs like Clockwork: Performance Predictability from the Bottom Up . In Proceedings of the 14th USENIX Conference on Operating Systems Design and Implementation (OSDI'20) . USENIX Association, USA, Article 25, 20 pages. Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, and Jonathan Mace. 2020. Serving DNNs like Clockwork: Performance Predictability from the Bottom Up. In Proceedings of the 14th USENIX Conference on Operating Systems Design and Implementation (OSDI'20). USENIX Association, USA, Article 25, 20 pages."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241557"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173162.3173185"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906396"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318216.3363303"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737614"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.179"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483273"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3037697.3037698"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9340890"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419194"},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation","author":"Lee Yunseong","year":"2018","unstructured":"Yunseong Lee , Alberto Scolari , Byung-Gon Chun , Marco Domenico Santambrogio , Markus Weimer , and Matteo Interlandi . 2018 . Pretzel: Opening the Black Box of Machine Learning Prediction Serving Systems . In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation ( Carlsbad, CA, USA) (OSDI'18). USENIX Association, USA, 611--626. Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, and Matteo Interlandi. 2018. Pretzel: Opening the Black Box of Machine Learning Prediction Serving Systems. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (Carlsbad, CA, USA) (OSDI'18). USENIX Association, USA, 611--626."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3229556.3229562"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/PADSW.2018.8645013"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387514.3405874"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485938"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3300061.3300116"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586088"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317865"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3472727.3472807"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01094"},{"key":"e_1_3_2_1_52_1","volume-title":"Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer","author":"Ranftl Ren\u00e9","year":"2020","unstructured":"Ren\u00e9 Ranftl , Katrin Lasinger , David Hafner , Konrad Schindler , and Vladlen Koltun . 2020. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer . IEEE transactions on pattern analysis and machine intelligence ( 2020 ). Ren\u00e9 Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. 2020. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence (2020)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_3_2_1_54_1","volume-title":"YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767","author":"Redmon Joseph","year":"2018","unstructured":"Joseph Redmon and Ali Farhadi . 2018. YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767 ( 2018 ). Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3560539"},{"key":"e_1_3_2_1_56_1","volume-title":"Retrieved","author":"Simon Julien","year":"2018","unstructured":"Julien Simon . 2018 . Amazon Elastic Inference - GPU-Powered Deep Learning Inference Acceleration . Retrieved July 14, 2022 from https:\/\/aws.amazon.com\/blogs\/aws\/amazon-elastic-inference-gpu-powered-deep-learning-inference-acceleration\/ Julien Simon. 2018. Amazon Elastic Inference - GPU-Powered Deep Learning Inference Acceleration. Retrieved July 14, 2022 from https:\/\/aws.amazon.com\/blogs\/aws\/amazon-elastic-inference-gpu-powered-deep-learning-inference-acceleration\/"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1711.02508"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00122"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794182"},{"key":"e_1_3_2_1_60_1","unstructured":"Yuxin Wu Alexander Kirillov Francisco Massa Wan-Yen Lo and Ross Girshick. 2019. Detectron2. https:\/\/github.com\/facebookresearch\/detectron2.  Yuxin Wu Alexander Kirillov Francisco Massa Wan-Yen Lo and Ross Girshick. 2019. Detectron2. https:\/\/github.com\/facebookresearch\/detectron2."},{"key":"e_1_3_2_1_61_1","volume-title":"Wortman Vaughan (Eds.)","volume":"34","author":"Xie Enze","year":"2021","unstructured":"Enze Xie , Wenhai Wang , Zhiding Yu , Anima Anandkumar , Jose M. Alvarez , and Ping Luo . 2021 . SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J . Wortman Vaughan (Eds.) , Vol. 34 . Curran Associates, Inc., 1 2077--12090. https:\/\/proceedings.neurips.cc\/paper\/2021\/file\/64f1f27bf1b4ec22924fd0acb550c235-Paper.pdf Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, and Ping Luo. 2021. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 12077--12090. https:\/\/proceedings.neurips.cc\/paper\/2021\/file\/64f1f27bf1b4ec22924fd0acb550c235-Paper.pdf"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3300061.3345448"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3492321.3519577"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3431159"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_10"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430898"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419185"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419192"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787486"},{"key":"e_1_3_2_1_70_1","volume-title":"Retrieved","author":"Yong Ming Guang","year":"2019","unstructured":"Ming Guang Yong . 2019 . Object Detection and Tracking using MediaPipe . Retrieved April 22, 2022 from https:\/\/developers.googleblog.com\/2019\/12\/object-detection-and-tracking-using-mediapipe.html Ming Guang Yong. 2019. Object Detection and Tracking using MediaPipe. Retrieved April 22, 2022 from https:\/\/developers.googleblog.com\/2019\/12\/object-detection-and-tracking-using-mediapipe.html"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD51958.2021.9643501"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9197374"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230554"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430726"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3448628"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTAS.2018.00028"}],"event":{"name":"ACM MobiCom '23: 29th Annual International Conference on Mobile Computing and Networking","location":"Madrid Spain","acronym":"ACM MobiCom '23","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"]},"container-title":["Proceedings of the 29th Annual International Conference on Mobile Computing and Networking"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3570361.3592531","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3570361.3592531","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:27Z","timestamp":1750182567000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3570361.3592531"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,2]]},"references-count":76,"alternative-id":["10.1145\/3570361.3592531","10.1145\/3570361"],"URL":"https:\/\/doi.org\/10.1145\/3570361.3592531","relation":{},"subject":[],"published":{"date-parts":[[2023,10,2]]},"assertion":[{"value":"2023-10-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}