{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T08:14:20Z","timestamp":1783757660624,"version":"3.55.0"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100027003","name":"Massachusetts Export Center","doi-asserted-by":"publisher","award":["2024AH050598"],"award-info":[{"award-number":["2024AH050598"]}],"id":[{"id":"10.13039\/100027003","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Array"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.array.2026.100940","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T15:10:09Z","timestamp":1779981009000},"page":"100940","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A 5G-enabled edge\u2013cloud collaborative framework for anomaly detection in industrial compressor PLC data"],"prefix":"10.1016","volume":"30","author":[{"given":"Na","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.array.2026.100940_bib1","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.3390\/s25041006","article-title":"A machine learning implementation to predictive maintenance and monitoring of industrial compressors","volume":"25","author":"Aminzadeh","year":"2025","journal-title":"Sensors"},{"issue":"16","key":"10.1016\/j.array.2026.100940_bib2","doi-asserted-by":"crossref","first-page":"8081","DOI":"10.3390\/app12168081","article-title":"On predictive maintenance in industry 4.0: overview, models, and challenges","volume":"12","author":"Achouch","year":"2022","journal-title":"Appl Sci"},{"issue":"3","key":"10.1016\/j.array.2026.100940_bib3","doi-asserted-by":"crossref","first-page":"372","DOI":"10.63278\/1387","article-title":"Predictive maintenance and monitoring of industrial compressors using machine learning: a proactive approach","volume":"31","author":"Dwivedi","year":"2025","journal-title":"Metallurgical and Materials Engineering"},{"key":"10.1016\/j.array.2026.100940_bib4","series-title":"2023 4th international conference on communications, information, electronic and energy systems (CIEES)","first-page":"1","article-title":"Structural analysis of a PLC controller based industrial application","author":"Mihaylova","year":"2023"},{"issue":"2","key":"10.1016\/j.array.2026.100940_bib5","doi-asserted-by":"crossref","first-page":"44","DOI":"10.3390\/bdcc9020044","article-title":"A survey on the applications of cloud computing in the industrial internet of things","volume":"9","author":"Dritsas","year":"2025","journal-title":"Big data and cognitive computing"},{"issue":"4","key":"10.1016\/j.array.2026.100940_bib6","doi-asserted-by":"crossref","first-page":"805","DOI":"10.35833\/MPCE.2021.000161","article-title":"Edge-cloud computing systems for smart grid: state-of-the-art, architecture, and applications","volume":"10","author":"Li","year":"2022","journal-title":"J Mod Power Syst Clean Energy"},{"issue":"5","key":"10.1016\/j.array.2026.100940_bib7","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1007\/s00607-020-00896-5","article-title":"Edge computing: current trends, research challenges and future directions","volume":"103","author":"Carvalho","year":"2021","journal-title":"Computing"},{"issue":"2","key":"10.1016\/j.array.2026.100940_bib8","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/COMST.2021.3061981","article-title":"A survey on mobile augmented reality with 5G mobile edge computing: architectures, applications, and technical aspects","volume":"23","author":"Siriwardhana","year":"2021","journal-title":"IEEE Commun Surv Tutor"},{"issue":"2","key":"10.1016\/j.array.2026.100940_bib9","first-page":"19","article-title":"Edge computing and the future of cloud computing: a survey of industry perspectives and predictions","volume":"2","author":"George","year":"2023","journal-title":"Partners Universal International Research Journal"},{"issue":"4","key":"10.1016\/j.array.2026.100940_bib10","doi-asserted-by":"crossref","first-page":"2892","DOI":"10.1109\/COMST.2023.3316615","article-title":"Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: challenges, recent advances, and future directions","volume":"25","author":"Duan","year":"2023","journal-title":"IEEE Commun Surv Tutor"},{"key":"10.1016\/j.array.2026.100940_bib11","doi-asserted-by":"crossref","first-page":"3768","DOI":"10.1109\/ACCESS.2023.3349132","article-title":"A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: methods, applications, and directions","volume":"12","author":"Yan","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.array.2026.100940_bib12","article-title":"KACNet: enhancing CNN feature representation with Kolmogorov-arnold networks for medical image segmentation and classification","author":"Li","year":"2025","journal-title":"Inf Sci"},{"key":"10.1016\/j.array.2026.100940_bib13","first-page":"1","article-title":"Finger vein recognition algorithm based on lightweight deep convolutional neural network","volume":"71","author":"Shen","year":"2021","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"10.1016\/j.array.2026.100940_bib14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3691338","article-title":"Deep learning for time series anomaly detection: a survey","volume":"57","author":"Zamanzadeh Darban","year":"2024","journal-title":"ACM Comput Surv"},{"issue":"19","key":"10.1016\/j.array.2026.100940_bib15","doi-asserted-by":"crossref","first-page":"21903","DOI":"10.1109\/JSEN.2021.3105226","article-title":"Fault detection with LSTM-Based variational autoencoder for maritime components","volume":"21","author":"Han","year":"2021","journal-title":"IEEE Sens J"},{"issue":"3","key":"10.1016\/j.array.2026.100940_bib16","doi-asserted-by":"crossref","first-page":"972","DOI":"10.3390\/s21030972","article-title":"A deep learning model for predictive maintenance in cyber-physical production systems using lstm autoencoders","volume":"21","author":"Bampoula","year":"2021","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.array.2026.100940_bib17","first-page":"33","article-title":"Revolutionizing PLC systems with AI: a new era of industrial automation","volume":"1","author":"Dhameliya","year":"2023","journal-title":"American Digits: Journal of Computing and Digital Technologies"},{"issue":"1","key":"10.1016\/j.array.2026.100940_bib18","first-page":"19","article-title":"Integration of IoT and edge computing in smart industrial environments","volume":"1","author":"Reyes","year":"2025","journal-title":"Technical Science Integrated Research"},{"key":"10.1016\/j.array.2026.100940_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110499","article-title":"Paving the way towards safer and more efficient maritime industry with 5G and beyond edge computing systems","volume":"250","author":"Charpentier","year":"2024","journal-title":"Comput Netw"},{"issue":"2","key":"10.1016\/j.array.2026.100940_bib20","first-page":"433","article-title":"Integrating multi-access edge computing (Mec) into open 5G core","volume":"5","author":"Xavier","year":"2024","journal-title":"Tele com (NY)"},{"issue":"5","key":"10.1016\/j.array.2026.100940_bib21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3529758","article-title":"On the edge of the deployment: a survey on multi-access edge computing","volume":"55","author":"Cruz","year":"2022","journal-title":"ACM Comput Surv"},{"issue":"1","key":"10.1016\/j.array.2026.100940_bib22","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/s25010190","article-title":"A survey of deep anomaly detection in multivariate time series: taxonomy, applications, and directions","volume":"25","author":"Wang","year":"2025","journal-title":"Sensors (Basel, Switzerland)"},{"issue":"3","key":"10.1016\/j.array.2026.100940_bib23","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/MWC.2019.1800234","article-title":"An overview of network slicing for 5G","volume":"26","author":"Zhang","year":"2019","journal-title":"IEEE Wireless Commun"},{"issue":"12","key":"10.1016\/j.array.2026.100940_bib24","doi-asserted-by":"crossref","first-page":"12591","DOI":"10.1109\/TKDE.2023.3270293","article-title":"Deep isolation forest for anomaly detection","volume":"35","author":"Xu","year":"2023","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.array.2026.100940_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112369","article-title":"Deep anomaly detection: a linear one-class SVM approach for high-dimensional and large-scale data","volume":"167","author":"Suresh","year":"2024","journal-title":"Appl Soft Comput"},{"issue":"1","key":"10.1016\/j.array.2026.100940_bib26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42400-022-00134-9","article-title":"Practical autoencoder based anomaly detection by using vector reconstruction error","volume":"6","author":"Torabi","year":"2023","journal-title":"Cybersecurity"},{"key":"10.1016\/j.array.2026.100940_bib27","series-title":"Proceedings of ICML workshop on Anomaly Detection","first-page":"1","article-title":"LSTM-based encoder-decoder for multi-sensor anomaly detection","author":"Malhotra","year":"2016"},{"issue":"3","key":"10.1016\/j.array.2026.100940_bib28","first-page":"231","article-title":"Reconstruction probability-based anomaly detection using variational auto-encoders","volume":"45","author":"Iqbal","year":"2023","journal-title":"Int J Comput Appl"},{"key":"10.1016\/j.array.2026.100940_bib29","doi-asserted-by":"crossref","first-page":"161003","DOI":"10.1109\/ACCESS.2021.3131949","article-title":"Applications of generative adversarial networks in anomaly detection: a systematic literature review","volume":"9","author":"Sabuhi","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.array.2026.100940_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111507","article-title":"Transformer-based multivariate time series anomaly detection using inter-variable attention mechanism","volume":"290","author":"Kang","year":"2024","journal-title":"Knowl Base Syst"},{"issue":"11","key":"10.1016\/j.array.2026.100940_bib31","doi-asserted-by":"crossref","first-page":"19143","DOI":"10.1109\/JIOT.2024.3367692","article-title":"Industrial internet of things intelligence empowering smart manufacturing: a literature review","volume":"11","author":"Hu","year":"2024","journal-title":"IEEE Internet Things J"},{"key":"10.1016\/j.array.2026.100940_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.array.2026.100775","article-title":"A systematic literature review of large language models in phishing attack generation and detection","volume":"30","author":"Sivaneswaran","year":"2026","journal-title":"Array"},{"key":"10.1016\/j.array.2026.100940_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2024.102927","article-title":"Exploring the integration of cloud manufacturing and cyber-physical systems in the era of industry 4.0\u2013An OPC UA approach","volume":"93","author":"Ji","year":"2025","journal-title":"Robot Comput Integrated Manuf"},{"issue":"2","key":"10.1016\/j.array.2026.100940_bib34","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s42979-025-03695-x","article-title":"Intelligent IoT-Driven advanced predictive maintenance system for industrial applications","volume":"6","author":"Dhinakaran","year":"2025","journal-title":"SN Comput Sci"},{"issue":"1","key":"10.1016\/j.array.2026.100940_bib35","article-title":"Vibration analysis for machine monitoring and diagnosis: a systematic review","volume":"2021","author":"Mohd Ghazali","year":"2021","journal-title":"Shock Vib"},{"key":"10.1016\/j.array.2026.100940_bib36","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.psep.2024.12.068","article-title":"Anomaly detection for compressor systems under variable operating conditions","volume":"194","author":"Lv","year":"2025","journal-title":"Process Saf Environ Prot"},{"key":"10.1016\/j.array.2026.100940_bib37","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ijrefrig.2022.08.008","article-title":"Fault detection and diagnosis in refrigeration systems using machine learning algorithms","volume":"144","author":"Soltani","year":"2022","journal-title":"Int J Refrig"},{"key":"10.1016\/j.array.2026.100940_bib38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10589759.2025.2562912","article-title":"Predictive modelling of dynamic compressors using machine learning and deep learning techniques","author":"Krishnan","year":"2025","journal-title":"Nondestr Test Eval"},{"issue":"4","key":"10.1016\/j.array.2026.100940_bib39","doi-asserted-by":"crossref","first-page":"4569","DOI":"10.1109\/TITS.2025.3642410","article-title":"Lightweight semantic feature extraction model with direction awareness for aerial traffic object detection","volume":"27","author":"Shen","year":"2026","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"12","key":"10.1016\/j.array.2026.100940_bib40","doi-asserted-by":"crossref","first-page":"24330","DOI":"10.1109\/TITS.2022.3203715","article-title":"An anchor-free lightweight deep convolutional network for vehicle detection in aerial images","volume":"23","author":"Shen","year":"2022","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"16","key":"10.1016\/j.array.2026.100940_bib41","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.6002","article-title":"From distributed machine learning to federated learning: in the view of data privacy and security","volume":"34","author":"Shen","year":"2022","journal-title":"Concurrency Comput Pract Ex"},{"issue":"1","key":"10.1016\/j.array.2026.100940_bib42","doi-asserted-by":"crossref","DOI":"10.1049\/ise2\/8432654","article-title":"Enhancing IoT security via federated learning: a comprehensive approach to intrusion detection","volume":"2025","author":"Bai","year":"2025","journal-title":"IET Inf Secur"}],"container-title":["Array"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626002638?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626002638?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T07:54:27Z","timestamp":1783756467000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2590005626002638"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":42,"alternative-id":["S2590005626002638"],"URL":"https:\/\/doi.org\/10.1016\/j.array.2026.100940","relation":{},"ISSN":["2590-0056"],"issn-type":[{"value":"2590-0056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A 5G-enabled edge\u2013cloud collaborative framework for anomaly detection in industrial compressor PLC data","name":"articletitle","label":"Article Title"},{"value":"Array","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.array.2026.100940","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"100940"}}