{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:08:56Z","timestamp":1764778136106,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","funder":[{"name":"Digital Futures","award":["KTH-RPROJ-0278099"],"award-info":[{"award-number":["KTH-RPROJ-0278099"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,3]]},"DOI":"10.1145\/3769102.3774634","type":"proceedings-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:00:41Z","timestamp":1764777641000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Cost-Aware Hierarchical Cascade for Anomaly Detection at the Edge in Connected Vehicles"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0609-3490","authenticated-orcid":false,"given":"Cheng-Hsun","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7220-5353","authenticated-orcid":false,"given":"Adarsh Prasad","family":"Behera","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4530-3378","authenticated-orcid":false,"given":"Sophia","family":"Zhang Pettersson","sequence":"additional","affiliation":[{"name":"Cloud and Embedded Platform, Traton AB, S\u00f6dert\u00e4lje, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6682-6559","authenticated-orcid":false,"given":"James","family":"Gross","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Smart city transportation: Deep learning ensemble approach for traffic accident detection","author":"Adewopo Victor A","year":"2024","unstructured":"Victor A Adewopo and Nelly Elsayed. 2024. Smart city transportation: Deep learning ensemble approach for traffic accident detection. IEEE Access (2024)."},{"key":"e_1_3_2_1_2_1","volume-title":"Houbing Herbert Song, and Yazeed Alkhrijah","author":"Ahmad Usman","year":"2024","unstructured":"Usman Ahmad, Mu Han, Alireza Jolfaei, Sohail Jabbar, Muhammad Ibrar, Aiman Erbad, Houbing Herbert Song, and Yazeed Alkhrijah. 2024. A comprehensive survey and tutorial on smart vehicles: Emerging technologies, security issues, and solutions using machine learning. IEEE Transactions on Intelligent Transportation Systems (2024)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3597062.3597278"},{"key":"e_1_3_2_1_4_1","volume-title":"Variational autoencoder based anomaly detection using reconstruction probability. Special lecture on IE 2, 1","author":"An Jinwon","year":"2015","unstructured":"Jinwon An and Sungzoon Cho. 2015. Variational autoencoder based anomaly detection using reconstruction probability. Special lecture on IE 2, 1 (2015), 1\u201318."},{"key":"e_1_3_2_1_5_1","volume-title":"Exploring the boundaries of on-device inference: When tiny falls short, go hierarchical","author":"Behera Adarsh Prasad","year":"2025","unstructured":"Adarsh Prasad Behera, Paulius Daubaris, I\u00f1aki Bravo, Jos\u00e9 Gallego, Roberto Morabito, Joerg Widmer, and Jaya Prakash Champati. 2025. Exploring the boundaries of on-device inference: When tiny falls short, go hierarchical. IEEE Internet of Things Journal (2025)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3615732"},{"key":"e_1_3_2_1_7_1","volume-title":"Classification-based anomaly detection for general data. arXiv preprint arXiv:2005.02359","author":"Bergman Liron","year":"2020","unstructured":"Liron Bergman and Yedid Hoshen. 2020. Classification-based anomaly detection for general data. arXiv preprint arXiv:2005.02359 (2020)."},{"key":"e_1_3_2_1_8_1","volume-title":"Cascaded Ensembling for Resource-Efficient Multivariate Time Series Anomaly Detection. Master's thesis","author":"Mapitigama Boththanthrige Dhanushki Pavithya","unstructured":"Dhanushki Pavithya Mapitigama Boththanthrige. 2024. Cascaded Ensembling for Resource-Efficient Multivariate Time Series Anomaly Detection. Master's thesis. Uppsala University. Master's Thesis."},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the 17th ACM Workshop on Hot Topics in Networks. 50\u201356","author":"Chinchali Sandeep P","year":"2018","unstructured":"Sandeep P Chinchali, Eyal Cidon, Evgenya Pergament, Tianshu Chu, and Sachin Katti. 2018. Neural networks meet physical networks: Distributed inference between edge devices and the cloud. In Proceedings of the 17th ACM Workshop on Hot Topics in Networks. 50\u201356."},{"key":"e_1_3_2_1_10_1","volume-title":"2023 IEEE International Conference on Edge Computing and Communications (EDGE). IEEE, 150\u2013158","author":"Das Ronit","year":"2023","unstructured":"Ronit Das and Tie Luo. 2023. LightESD: Fully-automated and lightweight anomaly detection framework for edge computing. In 2023 IEEE International Conference on Edge Computing and Communications (EDGE). IEEE, 150\u2013158."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/s25061763"},{"key":"e_1_3_2_1_12_1","volume-title":"Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors. arXiv preprint arXiv:2409.17841","author":"Gallon Riccardo","year":"2024","unstructured":"Riccardo Gallon, Fabian Schiemenz, Alisa Krstova, Alessandra Menicucci, and Eberhard Gill. 2024. Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors. arXiv preprint arXiv:2409.17841 (2024)."},{"key":"e_1_3_2_1_13_1","volume-title":"Edge-Assisted Flexible Platooning of Connected and Automated Vehicles Under Traffic Anomaly","author":"Gao Fengkun","year":"2025","unstructured":"Fengkun Gao, Bo Yang, Cailian Chen, and Xinping Guan. 2025. Edge-Assisted Flexible Platooning of Connected and Automated Vehicles Under Traffic Anomaly. IEEE Transactions on Vehicular Technology (2025)."},{"key":"e_1_3_2_1_14_1","volume-title":"Turbo Expo: Power for Land, Sea, and Air","author":"Goyal Vipul","unstructured":"Vipul Goyal, Mengyu Xu, and Jayanta Kapat. 2019. Use of vector autoregressive model for anomaly detection in utility gas turbines. In Turbo Expo: Power for Land, Sea, and Air, Vol. 58608. American Society of Mechanical Engineers, V003T08A004."},{"key":"e_1_3_2_1_15_1","volume-title":"Time-series anomaly detection in automated vehicles using d-cnn-lstm autoencoder","author":"Khanmohammadi Fatemeh","year":"2024","unstructured":"Fatemeh Khanmohammadi and Reza Azmi. 2024. Time-series anomaly detection in automated vehicles using d-cnn-lstm autoencoder. IEEE Transactions on Intelligent Transportation Systems (2024)."},{"key":"e_1_3_2_1_16_1","volume-title":"2018 international conference on information and computer technologies (icict). IEEE, 67\u201371","author":"Kim Dohyung","year":"2018","unstructured":"Dohyung Kim, Hyochang Yang, Minki Chung, Sungzoon Cho, Huijung Kim, Minhee Kim, Kyungwon Kim, and Eunseok Kim. 2018. Squeezed convolutional variational autoencoder for unsupervised anomaly detection in edge device industrial internet of things. In 2018 international conference on information and computer technologies (icict). IEEE, 67\u201371."},{"key":"e_1_3_2_1_17_1","volume-title":"Choong Seon Hong, and Nguyen H Tran","author":"Le Long Tan","year":"2025","unstructured":"Long Tan Le, Tung-Anh Nguyen, Han Shu, Suranga Seneviratne, Choong Seon Hong, and Nguyen H Tran. 2025. Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection. arXiv preprint arXiv:2503.11255 (2025)."},{"key":"e_1_3_2_1_18_1","volume-title":"2024 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, 476\u2013482","author":"Letsiou Afroditi","year":"2024","unstructured":"Afroditi Letsiou, Vishnu Narayanan Moothedath, Adarsh Prasad Behera, Jaya Prakash Champati, and James Gross. 2024. Hierarchical Inference at the Edge: A Batch Processing Approach. In 2024 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, 476\u2013482."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"e_1_3_2_1_20_1","volume-title":"ECAI","author":"Li Jiahao","year":"2024","unstructured":"Jiahao Li, Yiqiang Chen, Yunbing Xing, Yang Gu, and Xiangyuan Lan. 2024. Cascade Memory for Unsupervised Anomaly Detection. In ECAI 2024. IOS Press, 2854\u20132861."},{"key":"e_1_3_2_1_21_1","volume-title":"Kai Ming Ting, and Zhi-Hua Zhou","author":"Liu Fei Tony","year":"2008","unstructured":"Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE, 413\u2013422."},{"key":"e_1_3_2_1_22_1","first-page":"94","article-title":"Long short term memory networks for anomaly detection in time series","volume":"89","author":"Malhotra Pankaj","year":"2015","unstructured":"Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Puneet Agarwal, et al. 2015. Long short term memory networks for anomaly detection in time series. In Proceedings, Vol. 89. 94.","journal-title":"Proceedings"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3286611"},{"key":"e_1_3_2_1_24_1","volume-title":"Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods. Expert Systems with Applications","author":"Mejri Nesryne","year":"2024","unstructured":"Nesryne Mejri, Laura Lopez-Fuentes, Kankana Roy, Pavel Chernakov, Enjie Ghorbel, and Djamila Aouada. 2024. Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods. Expert Systems with Applications (2024), 124922."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010394"},{"key":"e_1_3_2_1_26_1","volume-title":"Jaya Prakash Champati, and James Gross","author":"Moothedath Vishnu Narayanan","year":"2024","unstructured":"Vishnu Narayanan Moothedath, Jaya Prakash Champati, and James Gross. 2024. Getting the Best Out of Both Worlds: Algorithms for Hierarchical Inference at the Edge. IEEE Transactions on Machine Learning in Communications and Networking (2024)."},{"key":"e_1_3_2_1_27_1","volume-title":"Deep learning for anomaly detection: A review. ACM computing surveys (CSUR) 54, 2","author":"Pang Guansong","year":"2021","unstructured":"Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. 2021. Deep learning for anomaly detection: A review. ACM computing surveys (CSUR) 54, 2 (2021), 1\u201338."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330871"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/app13031778"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Douglas A Reynolds et al. 2009. Gaussian mixture models. Encyclopedia of biometrics 741 659\u2013663 (2009) 3.","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"e_1_3_2_1_32_1","volume-title":"International conference on machine learning. PMLR, 4393\u20134402","author":"Ruff Lukas","year":"2018","unstructured":"Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel M\u00fcller, and Marius Kloft. 2018. Deep one-class classification. In International conference on machine learning. PMLR, 4393\u20134402."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681978"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/TITS.2019.2906038","article-title":"Real-time sensor anomaly detection and identification in automated vehicles","volume":"21","author":"Wyk Franco Van","year":"2019","unstructured":"Franco Van Wyk, Yiyang Wang, Anahita Khojandi, and Neda Masoud. 2019. Real-time sensor anomaly detection and identification in automated vehicles. IEEE Transactions on Intelligent Transportation Systems 21, 3 (2019), 1264\u20131276.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2001.990517"},{"key":"e_1_3_2_1_38_1","volume-title":"Idk cascades: Fast deep learning by learning not to overthink. arXiv preprint arXiv:1706.00885","author":"Wang Xin","year":"2017","unstructured":"Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, and Joseph E Gonzalez. 2017. Idk cascades: Fast deep learning by learning not to overthink. arXiv preprint arXiv:1706.00885 (2017)."},{"key":"e_1_3_2_1_39_1","volume-title":"Real-time sensor anomaly detection and recovery in connected automated vehicle sensors","author":"Wang Yiyang","year":"2020","unstructured":"Yiyang Wang, Neda Masoud, and Anahita Khojandi. 2020. Real-time sensor anomaly detection and recovery in connected automated vehicle sensors. IEEE transactions on intelligent transportation systems 22, 3 (2020), 1411\u20131421."},{"key":"e_1_3_2_1_40_1","volume-title":"Enhancing Computational Efficiency in Anomaly Detection with a Cascaded Machine Learning Model. Master's thesis","author":"Teng-Sung Yu.","unstructured":"Teng-Sung Yu. 2024. Enhancing Computational Efficiency in Anomaly Detection with a Cascaded Machine Learning Model. Master's thesis. Uppsala University. Master's Thesis."}],"event":{"name":"SEC '25: Tenth ACM\/IEEE Symposium on Edge Computing","location":"the Hilton Arlington National Landing Arlington VA USA","acronym":"SEC '25","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","IEEE Computer Society"]},"container-title":["Proceedings of the Tenth ACM\/IEEE Symposium on Edge Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3769102.3774634","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:04:11Z","timestamp":1764777851000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3769102.3774634"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,3]]},"references-count":40,"alternative-id":["10.1145\/3769102.3774634","10.1145\/3769102"],"URL":"https:\/\/doi.org\/10.1145\/3769102.3774634","relation":{},"subject":[],"published":{"date-parts":[[2025,12,3]]},"assertion":[{"value":"2025-12-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}