{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T03:17:32Z","timestamp":1777432652326,"version":"3.51.4"},"reference-count":70,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T00:00:00Z","timestamp":1644451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The past decades witnessed an unprecedented urbanization and the proliferation of modern information and communication technologies (ICT), which makes the concept of Smart City feasible. Among various intelligent components, smart urban transportation monitoring is an essential part of smoothly operational smart cities. Although there is fast development of Smart Cities and the growth of Internet of Things (IoT), real-time anomalous behavior detection in Intelligent Transportation Systems (ITS) is still challenging. Because of multiple advanced features including flexibility, safety, and ease of manipulation, quadcopter drones have been widely adopted in many areas, from service improvement to urban surveillance, and data collection for scientific research. In this paper, a Smart Urban traffic Monitoring (SurMon) scheme is proposed employing drones following an edge computing paradigm. A dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision-making at the edge. Specifically, we propose to identify anomalous vehicle behaviors in real time by creatively applying the multidimensional Singular Spectrum Analysis (mSSA) technique in space to detect the different vehicle behaviors on roads. Multiple features of vehicle behaviors are fed into channels of the mSSA procedure. Instead of trying to create and define a database of normal activity patterns of vehicles on the road, the anomaly detection is reformatted as an outlier identifying problem. Then, a cascaded Capsules Network is designed to predict whether the behavior is a violation. An extensive experimental study has been conducted and the results have validated the feasibility and effectiveness of the SurMon scheme.<\/jats:p>","DOI":"10.3390\/fi14020054","type":"journal-article","created":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T02:37:46Z","timestamp":1644547066000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service"],"prefix":"10.3390","volume":"14","author":[{"given":"Ning","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, NY 13905, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1880-0586","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, NY 13905, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,10]]},"reference":[{"key":"ref_1","unstructured":"UN (2016, September 09). World Urbanization Prospects 2014. Available online: http:\/\/www.un.org\/en\/development\/desa\/news\/population\/world-urbanization-prospects-2014.html."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MIC.2017.25","article-title":"A new era for cities with fog computing","volume":"21","author":"Yannuzzi","year":"2017","journal-title":"IEEE Internet Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"120743","DOI":"10.1016\/j.techfore.2021.120743","article-title":"Risk management in sustainable smart cities governance: A TOE framework","volume":"167","author":"Ullah","year":"2021","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MNET.2019.8675165","article-title":"The internet of things for smart cities: Technologies and applications","volume":"33","author":"Qian","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MIS.2019.2942836","article-title":"Research on road traffic situation awareness system based on image big data","volume":"35","author":"Zhu","year":"2019","journal-title":"IEEE Intell. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MELE.2018.2816840","article-title":"Smart cities for a sustainable urbanization: Illuminating the need for establishing smart urban infrastructures","volume":"6","author":"Shahidehpour","year":"2018","journal-title":"IEEE Electrif. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Visvizi, A., and Lytras, M.D. (2020). Sustainable Smart Cities and Smart Villages Research: Rethinking Security, Safety, Well-Being, and Happiness. Sustainability, 12.","DOI":"10.3390\/su12010215"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chackravarthy, S., Schmitt, S., and Yang, L. (2018, January 18\u201320). Intelligent crime anomaly detection in smart cities using deep learning. Proceedings of the 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC), Philadelphia, PA, USA.","DOI":"10.1109\/CIC.2018.00060"},{"key":"ref_9","unstructured":"Unions, U. (2021, October 25). World Health Organization: Road Traffic Deaths. Available online: https:\/\/sdgs.un.org\/goals\/goal11."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"85714","DOI":"10.1109\/ACCESS.2020.2991734","article-title":"An overview on edge computing research","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.future.2019.12.039","article-title":"Offloading decision methods for multiple users with structured tasks in edge computing for smart cities","volume":"105","author":"Kuang","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, N., and Chen, Y. (2018). Smart city surveillance at the network edge in the era of iot: Opportunities and challenges. Smart Cities, Springer.","DOI":"10.1007\/978-3-319-76669-0_7"},{"key":"ref_13","first-page":"2191","article-title":"Edge-cloud computing for Internet of Things data analytics: Embedding intelligence in the edge with deep learning","volume":"17","author":"Ghosh","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge computing: Vision and challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xu, R., Nikouei, S.Y., Chen, Y., Polunchenko, A., Song, S., Deng, C., and Faughnan, T.R. (2018, January 20\u201324). Real-time human objects tracking for smart surveillance at the edge. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422970"},{"key":"ref_16","unstructured":"Yun, K., Huyen, A., and Lu, T. (2018). Deep neural networks for pattern recognition. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/COMST.2020.2970550","article-title":"Convergence of edge computing and deep learning: A comprehensive survey","volume":"22","author":"Wang","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2348","DOI":"10.1109\/TCAD.2018.2858384","article-title":"Deepthings: Distributed adaptive deep learning inference on resource-constrained iot edge clusters","volume":"37","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Teerapittayanon, S., McDanel, B., and Kung, H.T. (2017, January 5\u20138). Distributed deep neural networks over the cloud, the edge and end devices. Proceedings of the 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, USA.","DOI":"10.1109\/ICDCS.2017.226"},{"key":"ref_20","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017). Dynamic routing between capsules. arXiv."},{"key":"ref_21","unstructured":"Punjabi, A., Schmid, J., and Katsaggelos, A.K. (2020). Examining the benefits of capsule neural networks. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, N., Yang, Z., Chen, Y., and Polunchenko, A. (2017, January 1\u20134). Online anomalous vehicle detection at the edge using multidimensional SSA. Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Atlanta, GA, USA.","DOI":"10.1109\/INFCOMW.2017.8116487"},{"key":"ref_23","unstructured":"Department of Transportation ITS Joint Program Office (2021, December 22). New Data Sets from the Next Generation Simulation (NGSIM) Program are Now Available in the Research Data Exchange (RDE), Available online: http:\/\/www.its.dot.gov\/press\/2016\/datasets_ngsim.htm."},{"key":"ref_24","first-page":"e3","article-title":"Exploration of singular spectrum analysis for online anomaly detection in crns","volume":"4","author":"Dong","year":"2017","journal-title":"EAI Endorsed Trans. Secur. Saf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1049\/iet-its.2014.0238","article-title":"Trajectory-based anomalous behaviour detection for intelligent traffic surveillance","volume":"9","author":"Cai","year":"2015","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wu, C.E., Yang, W.Y., Ting, H.C., and Wang, J.S. (2017, January 14\u201317). Traffic pattern modeling, trajectory classification and vehicle tracking within urban intersections. Proceedings of the 2017 International Smart Cities Conference (ISC2), Wuxi, China.","DOI":"10.1109\/ISC2.2017.8090791"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3417989","article-title":"Anomaly detection in road traffic using visual surveillance: A survey","volume":"53","author":"Santhosh","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ucar, S., Patnayak, C., Oza, P., Hoh, B., and Oguchi, K. (2019, January 4\u20139). Management of Anomalous Driving Behavior. Proceedings of the 2019 IEEE Vehicular Networking Conference (VNC), Los Angeles, CA, USA.","DOI":"10.1109\/VNC48660.2019.9062814"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/LRA.2020.3032079","article-title":"Interaction-aware motion prediction for autonomous driving: A multiple model kalman filtering scheme","volume":"6","author":"Lefkopoulos","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TITS.2020.3012034","article-title":"Deep learning-based vehicle behavior prediction for autonomous driving applications: A review","volume":"23","author":"Mozaffari","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6943","DOI":"10.1109\/TVT.2020.2993247","article-title":"Abnormal driving detection with normalized driving behavior data: A deep learning approach","volume":"69","author":"Hu","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6172","DOI":"10.1109\/TVT.2021.3078482","article-title":"Deep Learning Enhanced Driving Behavior Evaluation Based on Vehicle-Edge-Cloud Architecture","volume":"70","author":"Xun","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, M., Liu, Q., Yin, G., and Zhang, Y. (2020). Deep anomaly detection in expressway based on edge computing and deep learning. J. Ambient. Intell. Humaniz. Comput., 1\u201313.","DOI":"10.1007\/s12652-020-02574-y"},{"key":"ref_34","unstructured":"Jiang, L., Xie, W., Zhang, D., and Gu, T. (2021). Smart diagnosis: Deep learning boosted driver inattention detection and abnormal driving prediction. IEEE Internet Things J., 1\u201314."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/JPROC.2019.2921977","article-title":"Deep Learning With Edge Computing: A Review","volume":"107","author":"Chen","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MNET.2018.1700202","article-title":"Learning IoT in edge: Deep learning for the Internet of Things with edge computing","volume":"32","author":"Li","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Qi, X., and Liu, C. (2018, January 25\u201327). Enabling deep learning on iot edge: Approaches and evaluation. Proceedings of the 2018 IEEE\/ACM Symposium on Edge Computing (SEC), Seattle, WA, USA.","DOI":"10.1109\/SEC.2018.00047"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_39","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_40","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.neucom.2021.07.045","article-title":"Pruning and quantization for deep neural network acceleration: A survey","volume":"461","author":"Liang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, S., Lin, Y., Zhou, Z., Nan, K., Liu, H., and Du, J. (2018, January 10\u201315). On-demand deep model compression for mobile devices: A usage-driven model selection framework. Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, Munich, Germany.","DOI":"10.1145\/3210240.3210337"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yao, S., Zhao, Y., Zhang, A., Su, L., and Abdelzaher, T. (2017, January 6\u20138). Deepiot: Compressing deep neural network structures for sensing systems with a compressor-critic framework. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, Delft, The Netherlands.","DOI":"10.1145\/3131672.3131675"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Reuther, A., Michaleas, P., Jones, M., Gadepally, V., Samsi, S., and Kepner, J. (2021, January 21\u201323). AI Accelerator Survey and Trends. Proceedings of the 2021 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA.","DOI":"10.1109\/HPEC49654.2021.9622867"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"104441","DOI":"10.1016\/j.micpro.2022.104441","article-title":"Review of ASIC Accelerators for Deep Neural Network","volume":"89","author":"Machupalli","year":"2022","journal-title":"Microprocess. Microsyst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7823","DOI":"10.1109\/ACCESS.2018.2890150","article-title":"FPGA-based accelerators of deep learning networks for learning and classification: A review","volume":"7","author":"Shawahna","year":"2018","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/MNET.001.1900200","article-title":"Toward intelligent task offloading at the edge","volume":"34","author":"Guo","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/MWC.001.1900232","article-title":"Intelligent edge: Leveraging deep imitation learning for mobile edge computation offloading","volume":"27","author":"Yu","year":"2020","journal-title":"IEEE Wirel. Commun."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zamzam, M., Elshabrawy, T., and Ashour, M. (2019, January 8\u201312). Resource management using machine learning in mobile edge computing: A survey. Proceedings of the 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/ICICIS46948.2019.9014733"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Jiang, J., Ananthanarayanan, G., Bodik, P., Sen, S., and Stoica, I. (2018, January 20\u201325). Chameleon: Scalable adaptation of video analytics. Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, Budapest, Hungary.","DOI":"10.1145\/3230543.3230574"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/JPROC.2021.3055679","article-title":"Communication-efficient and distributed learning over wireless networks: Principles and applications","volume":"109","author":"Park","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1145\/3093337.3037698","article-title":"Neurosurgeon: Collaborative intelligence between the cloud and mobile edge","volume":"45","author":"Kang","year":"2017","journal-title":"ACM SIGARCH Comput. Archit. News"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ran, X., Chen, H., Zhu, X., Liu, Z., and Chen, J. (2018, January 15\u201319). Deepdecision: A mobile deep learning framework for edge video analytics. Proceedings of the IEEE INFOCOM 2018-IEEE Conference on Computer Communications, Honolulu, HI, USA.","DOI":"10.1109\/INFOCOM.2018.8485905"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5297","DOI":"10.1109\/JSTARS.2020.3021045","article-title":"Diverse capsules network combining multiconvolutional layers for remote sensing image scene classification","volume":"13","author":"Raza","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Afshar, P., Mohammadi, A., and Plataniotis, K.N. (2018, January 7\u201310). Brain tumor type classification via capsule networks. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451379"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4663","DOI":"10.1109\/JSTARS.2020.3015909","article-title":"Learning capsules for SAR target recognition","volume":"13","author":"Guo","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chen, R., Jalal, M.A., Mihaylova, L., and Moore, R.K. (2018, January 10\u201313). Learning capsules for vehicle logo recognition. Proceedings of the 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK.","DOI":"10.23919\/ICIF.2018.8455227"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"101889","DOI":"10.1016\/j.media.2020.101889","article-title":"Capsules for biomedical image segmentation","volume":"68","author":"LaLonde","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_59","unstructured":"LaLonde, R., and Bagci, U. (2018). Capsules for object segmentation. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Weld, H., Huang, X., Long, S., Poon, J., and Han, S.C. (2021). A survey of joint intent detection and slot-filling models in natural language understanding. arXiv.","DOI":"10.1145\/3547138"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Stali\u016bnait\u0117, I., and Iacobacci, I. (2020, January 4\u20138). Auxiliary Capsules for Natural Language Understanding. Proceedings of the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053899"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"116780","DOI":"10.1016\/j.conbuildmat.2019.116780","article-title":"Feasibility study on real-scale, self-healing concrete slab by developing a smart capsules network and assessed by a plethora of advanced monitoring techniques","volume":"228","author":"Tsangouri","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"ref_63","first-page":"1295","article-title":"Capsule networks\u2014A survey","volume":"34","author":"Patrick","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1109\/TMM.2021.3060280","article-title":"Associated Spatio-Temporal Capsule Network for Gait Recognition","volume":"24","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_65","unstructured":"Paik, I., Kwak, T., and Kim, I. (2019, January 17\u201319). Capsule networks need an improved routing algorithm. Proceedings of the Asian Conference on Machine Learning, PMLR, Nagoya, Japan."},{"key":"ref_66","first-page":"6896579","article-title":"Capsules TCN network for urban computing and intelligence in urban traffic prediction","volume":"2020","author":"Li","year":"2020","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4813","DOI":"10.1109\/TITS.2020.2984813","article-title":"Forecasting transportation network speed using deep capsule networks with nested LSTM models","volume":"22","author":"Ma","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kim, Y., Wang, P., Zhu, Y., and Mihaylova, L. (2018, January 9\u201311). A capsule network for traffic speed prediction in complex road networks. Proceedings of the 2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF), Bonn, Germany.","DOI":"10.1109\/SDF.2018.8547068"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Chen, N., Chen, Y., Blasch, E., Ling, H., You, Y., and Ye, X. (2017, January 3\u20135). Enabling smart urban surveillance at the edge. Proceedings of the 2017 IEEE International Conference on Smart Cloud (SmartCloud), New York, NY, USA.","DOI":"10.1109\/SmartCloud.2017.24"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/JSAIT.2021.3072962","article-title":"Sequential (Quickest) Change Detection: Classical Results and New Directions","volume":"2","author":"Xie","year":"2021","journal-title":"IEEE J. Sel. Areas Inf. Theory"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/2\/54\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:17:50Z","timestamp":1760134670000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/2\/54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,10]]},"references-count":70,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["fi14020054"],"URL":"https:\/\/doi.org\/10.3390\/fi14020054","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,10]]}}}