{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T07:49:51Z","timestamp":1767858591885,"version":"3.49.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:00:00Z","timestamp":1658188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science and Engineering Research Council of Canada","award":["210355"],"award-info":[{"award-number":["210355"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Things"],"published-print":{"date-parts":[[2022,8,31]]},"abstract":"<jats:p>In recent years, big data produced by the Internet of Things has enabled new kinds of useful applications. One such application is monitoring a fleet of vehicles in real time to predict their remaining useful life. The consensus self-organized models (COSMO) approach is an example of a predictive maintenance system. The present work proposes a novel Internet of Things based architecture for predictive maintenance that consists of three primary nodes: the vehicle node, the server leader node, and the root node, which enable on-board vehicle data processing, heavy-duty data processing, and fleet administration, respectively. A minimally viable prototype of the proposed architecture was implemented and deployed to a local bus garage in Gatineau, Canada.<\/jats:p><jats:p>The present work proposes improved consensus self-organized models (ICOSMO), a fleet-wide unsupervised dynamic sensor selection algorithm. To analyze the performance of ICOSMO, a fleet simulation was implemented. The J1939 data gathered from a hybrid bus was used to generate synthetic data in the simulations. Simulation results that compared the performance of the COSMO and ICOSMO approaches revealed that in general ICOSMO improves the average area under the curve of COSMO by approximately 1.5% when using the Cosine distance and 0.6% when using Hellinger distance.<\/jats:p>","DOI":"10.1145\/3530991","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T12:02:49Z","timestamp":1650456169000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Unsupervised Dynamic Sensor Selection for IoT-Based Predictive Maintenance of a Fleet of Public Transport Buses"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8992-3431","authenticated-orcid":false,"given":"Patrick","family":"Killeen","sequence":"first","affiliation":[{"name":"University of Ottawa, Ottawa, Ontario, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9119-9451","authenticated-orcid":false,"given":"Iluju","family":"Kiringa","sequence":"additional","affiliation":[{"name":"University of Ottawa, Ottawa, Ontario, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6039-6751","authenticated-orcid":false,"given":"Tet","family":"Yeap","sequence":"additional","affiliation":[{"name":"University of Ottawa, Ottawa, Ontario, Canada"}]}],"member":"320","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/icter.2015.7377695"},{"key":"e_1_3_2_3_2","volume-title":"Big Data and Internet of Things: A Roadmap for Smart Environments","author":"Bonomi Flavio","year":"2014","unstructured":"Flavio Bonomi, Rodolfo Milito, Preethi Natarajan, and Jiang Zhu. 2014. Fog computing: A platform for Internet of Things and Analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments, Nik Bessis and Ciprian Dobre (Eds.). Studies in Computational Intelligence, Vol. 546. Springer, 169\u2013186."},{"key":"e_1_3_2_4_2","article-title":"A Prognostic and Data Fusion Based Approach to Validating Automotive Electronics","author":"Braden Derek R.","year":"2014","unstructured":"Derek R. Braden and David M. Harvey. 2014. A Prognostic and Data Fusion Based Approach to Validating Automotive Electronics. SAE Technical Paper 2014-01-0724. SAE.","journal-title":"SAE Technical Paper 2014-01-0724. SAE."},{"key":"e_1_3_2_5_2","volume-title":"An IoT Gateway Middleware for Interoperability in SDN Managed Internet of Things","author":"Budakoti Jyoti","year":"2018","unstructured":"Jyoti Budakoti. 2018. An IoT Gateway Middleware for Interoperability in SDN Managed Internet of Things. Ph.D. Dissertation. Carleton University."},{"key":"e_1_3_2_6_2","volume-title":"Predictive Maintenance Framework for a Vehicular IoT Gateway Node Using Active Database Rules","author":"Butylin Sergei","year":"2018","unstructured":"Sergei Butylin. 2018. Predictive Maintenance Framework for a Vehicular IoT Gateway Node Using Active Database Rules. Master\u2019s Thesis. University of Ottawa. https:\/\/ruor.uottawa.ca\/handle\/10393\/38568."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/icma.2013.6618034"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2011.03.002"},{"key":"e_1_3_2_9_2","first-page":"1","volume-title":"Proceedings of the Workshop on Interactive Data Mining","author":"Calikus Ece","year":"2019","unstructured":"Ece Calikus, Yuantao Fan, Slawomir Nowaczyk, and Anita Sant\u2019Anna. 2019. Interactive-COSMO: Consensus self-organized models for fault detection with expert feedback. In Proceedings of the Workshop on Interactive Data Mining. 1\u20139."},{"key":"e_1_3_2_10_2","first-page":"325","volume-title":"Proceedings of the American Conference on Applied Mathematics (MATH\u201908)","author":"Cha Sung-Hyuk","year":"2008","unstructured":"Sung-Hyuk Cha. 2008. Taxonomy of nominal type histogram distance measures. In Proceedings of the American Conference on Applied Mathematics (MATH\u201908). 325\u2013330."},{"key":"e_1_3_2_11_2","volume-title":"A Rule-Based Expert System for Predictive Maintenance of a Hybrid Bus","author":"Chen Wenjie","year":"2020","unstructured":"Wenjie Chen. 2020. A Rule-Based Expert System for Predictive Maintenance of a Hybrid Bus. Master\u2019s Thesis. University of Ottawa. https:\/\/ruor.uottawa.ca\/handle\/10393\/40661."},{"key":"e_1_3_2_12_2","first-page":"213","volume-title":"Proceedings of the 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP\u201920)","author":"Chira Codrin-Mihai","year":"2020","unstructured":"Codrin-Mihai Chira, Raluca Portase, Ramona Tolas, Camelia Lemnaru, and Rodica Potolea. 2020. A system for managing and processing industrial sensor data: SMS. In Proceedings of the 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP\u201920). IEEE, Los Alamitos, CA, 213\u2013220."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835813"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/WF-IoT.2014.6803221","volume-title":"Proceedings of the 2014 IEEE World Forum on Internet of Things (WF-IoT\u201914)","author":"Datta Soumya Kanti","year":"2014","unstructured":"Soumya Kanti Datta, Christian Bonnet, and Navid Nikaein. 2014. An IoT gateway centric architecture to provide novel M2M services. In Proceedings of the 2014 IEEE World Forum on Internet of Things (WF-IoT\u201914). IEEE, Los Alamitos, CA, 514\u2013519."},{"key":"e_1_3_2_15_2","volume-title":"Eclipse Mosquitto Home Page","author":"Mosquitto Eclipse","year":"2018","unstructured":"Eclipse Mosquitto. 2018. Eclipse Mosquitto Home Page. Retrieved December 14, 2021 from https:\/\/mosquitto.org\/."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107098"},{"key":"e_1_3_2_17_2","first-page":"63","volume-title":"Proceedings of the 2018 Internal Conference on Data Science (ICDATA\u201918).","author":"Farouq Shiraz","year":"2018","unstructured":"Shiraz Farouq, Stefan Byttner, and Mohamed-Rafik Bouguelia. 2018. On monitoring heat-pumps with a group-based conformal anomaly detection approach. In Proceedings of the 2018 Internal Conference on Data Science (ICDATA\u201918).63\u201369."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103492"},{"key":"e_1_3_2_19_2","first-page":"392","volume-title":"Proceedings of the International Conference on Modelling and Simulation for Autonomous Systems","author":"Furch Jan","year":"2017","unstructured":"Jan Furch, Tomas Turo, Zdenek Krobot, and Jiri Stastny. 2017. Using telemetry for maintenance of special military vehicles. In Proceedings of the International Conference on Modelling and Simulation for Autonomous Systems. 392\u2013401."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.5555\/1855075"},{"key":"e_1_3_2_21_2","first-page":"225","volume-title":"Proceedings of the 2015 IEEE International Conference on Automation Science and Engineering (CASE\u201915)","author":"Jin Chao","year":"2015","unstructured":"Chao Jin, Dragan Djurdjanovic, Hossein D. Ardakani, Keren Wang, Matthew Buzza, Behrad Begheri, Patrick Brown, and Jay Lee. 2015. A comprehensive framework of factory-to-factory dynamic fleet-level prognostics and operation management for geographically distributed assets. In Proceedings of the 2015 IEEE International Conference on Automation Science and Engineering (CASE\u201915). IEEE, Los Alamitos, CA, 225\u2013230."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00354-006-0002-4"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/1835804.1835812","volume-title":"Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Kargupta Hillol","year":"2010","unstructured":"Hillol Kargupta, Kakali Sarkar, and Michael Gilligan. 2010. MineFleet\u00ae: An overview of a widely adopted distributed vehicle performance data mining system. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 37\u201346."},{"key":"e_1_3_2_24_2","unstructured":"John D. Kelleher Brian Mac Namee and Aoife D\u2019Arcy. 2015. Fundamentals of Machine Learning for Predictive Analytics : Algorithms Worked Examples and Case Studies . MIT Press Cambridge MA."},{"key":"e_1_3_2_25_2","volume-title":"Knowledge-Based Predictive Maintenance for Fleet Management","author":"Killeen Patrick","year":"2020","unstructured":"Patrick Killeen. 2020. Knowledge-Based Predictive Maintenance for Fleet Management. Master\u2019s Thesis. University of Ottawa. https:\/\/ruor.uottawa.ca\/handle\/10393\/40086."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2019.04.184"},{"key":"e_1_3_2_27_2","volume-title":"An AHP-Based Evaluation of Real-Time Stream Processing Technologies in IoT","author":"Killeen Patrick","year":"2018","unstructured":"Patrick Killeen and Alireza Parvizimosaed. 2018. An AHP-Based Evaluation of Real-Time Stream Processing Technologies in IoT. Technical Report. University of Ottawa. https:\/\/www.mudlakebiodiversity.ca\/papers\/ahp-based-evaluation-iot-2018.pdf."},{"issue":"2","key":"e_1_3_2_28_2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.microrel.2009.09.016","article-title":"Parameter selection for health monitoring of electronic products","volume":"50","author":"Kumar Sachin","year":"2010","unstructured":"Sachin Kumar, Eli Dolev, and Michael Pecht. 2010. Parameter selection for health monitoring of electronic products. Microelectronics Reliability 50, 2 (2010), 161\u2013168.","journal-title":"Microelectronics Reliability"},{"key":"e_1_3_2_29_2","volume-title":"Fault Detection in a Network of Similar Machines Using Clustering Approach","author":"Lapira Edzel R.","year":"2012","unstructured":"Edzel R. Lapira. 2012. Fault Detection in a Network of Similar Machines Using Clustering Approach. Ph.D. Dissertation. University of Cincinnati."},{"key":"e_1_3_2_30_2","volume-title":"LoRaWAN Coverage for LATAM and Asia-Pacific on Its IoT Sensor Platform","year":"2018","unstructured":"Libelium. 2018. LoRaWAN Coverage for LATAM and Asia-Pacific on Its IoT Sensor Platform. Retrieved November 20, 2018 from http:\/\/www.libelium.com\/libelium-expands-lorawan-coverage-for-latam-and-asia-pacific-on-its-iot-sensor-platform\/?utm_source=NewsletterLB&utm_medium=Email&utm_campaign=NLB-301018."},{"key":"e_1_3_2_31_2","volume-title":"Cyber-Physical System Augmented Prognostics and Health Management for Fleet-Based Systems","author":"Liu Zongchang","year":"2018","unstructured":"Zongchang Liu. 2018. Cyber-Physical System Augmented Prognostics and Health Management for Fleet-Based Systems. Ph.D. Dissertation. University of Cincinnati."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2015.06.076"},{"key":"e_1_3_2_33_2","first-page":"1","volume-title":"Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management (ICPHM\u201919)","author":"Michau Gabriel","year":"2019","unstructured":"Gabriel Michau and Olga Fink. 2019. Unsupervised fault detection in varying operating conditions. In Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management (ICPHM\u201919). IEEE, Los Alamitos, CA, 1\u201310."},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1016\/j.procir.2020.03.043","article-title":"Intelligent anomaly detection of machine tools based on mean shift clustering","volume":"93","author":"Netzer Markus","year":"2020","unstructured":"Markus Netzer, Jonas Michelberger, and J\u00fcrgen Fleischer. 2020. Intelligent anomaly detection of machine tools based on mean shift clustering. Procedia CIRP 93 (2020), 1448\u20131453.","journal-title":"Procedia CIRP"},{"key":"e_1_3_2_35_2","first-page":"41","volume-title":"Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning","author":"Nowaczyk S\u0142awomir","year":"2018","unstructured":"S\u0142awomir Nowaczyk, Anita Sant\u2019Anna, Ece Calikus, and Yuantao Fan. 2018. Monitoring equipment operation through model and event discovery. In Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning. 41\u201353."},{"key":"e_1_3_2_36_2","volume-title":"Smart Cities and Homes: Key Enabling Technologies","author":"Obaidat Mohammad S.","year":"2016","unstructured":"Mohammad S. Obaidat and Petros Nicopolitidis. 2016. Smart Cities and Homes: Key Enabling Technologies. Morgan Kaufmann."},{"key":"e_1_3_2_37_2","volume-title":"Home | OC Transpo","author":"Transpo OC","year":"2021","unstructured":"OC Transpo. 2021. Home | OC Transpo. Retrieved December 7, 2021 from https:\/\/www.octranspo.com\/."},{"key":"e_1_3_2_38_2","volume-title":"Distributed Collaborative Prognostics","author":"Palau Adri\u00e0 Salvador","year":"2020","unstructured":"Adri\u00e0 Salvador Palau. 2020. Distributed Collaborative Prognostics. Ph.D. Dissertation. University of Cambridge."},{"key":"e_1_3_2_39_2","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.engappai.2019.07.013","article-title":"An industrial multi agent system for real-time distributed collaborative prognostics","volume":"85","author":"Palau Adri\u00e0 Salvador","year":"2019","unstructured":"Adri\u00e0 Salvador Palau, Maharshi Harshadbhai Dhada, Kshitij Bakliwal, and Ajith Kumar Parlikad. 2019. An industrial multi agent system for real-time distributed collaborative prognostics. Engineering Applications of Artificial Intelligence 85 (2019), 590\u2013606.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"8","key":"e_1_3_2_40_2","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1007\/s10845-019-01478-9","article-title":"Multi-agent system architectures for collaborative prognostics","volume":"30","author":"Palau Adri\u00e0 Salvador","year":"2019","unstructured":"Adri\u00e0 Salvador Palau, Maharshi Harshadbhai Dhada, and Ajith Kumar Parlikad. 2019. Multi-agent system architectures for collaborative prognostics. Journal of Intelligent Manufacturing 30, 8 (2019), 2999\u20133013.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2015.02.009"},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1007\/s10845-019-01516-6","article-title":"A six-layer architecture for the digital twin: A manufacturing case study implementation","author":"Redelinghuys A. J. H.","year":"2020","unstructured":"A. J. H. Redelinghuys, A. H. Basson, and K. Kruger. 2020. A six-layer architecture for the digital twin: A manufacturing case study implementation. Journal of Intelligent Manufacturing 31 (2020), 1383\u20131402.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/SASO.2014.22"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-017-0538-6"},{"key":"e_1_3_2_45_2","volume-title":"STO | Soci\u00e9t\u00e9 de Transport d l\u2019Outaouais","author":"l\u2019Outaouais Soci\u00e9t\u00e9 de transport d","year":"2021","unstructured":"Soci\u00e9t\u00e9 de transport d l\u2019Outaouais. 2021. STO | Soci\u00e9t\u00e9 de Transport d l\u2019Outaouais. Retrieved December 7, 2021 from http:\/\/www.sto.ca\/."},{"key":"e_1_3_2_46_2","volume-title":"J1939 Digital Annex October 2017","author":"International Society of Automotive Engineers","year":"2017","unstructured":"Society of Automotive Engineers International. 2017. J1939 Digital Annex October 2017. Retrieved April 18, 2022 from https:\/\/www.sae.org\/standards\/content\/j1939da_201710\/."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/is.2008.4670481"},{"key":"e_1_3_2_48_2","first-page":"1","volume-title":"Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM\u201916)","author":"Teng Xudong","year":"2016","unstructured":"Xudong Teng, Yuantao Fan, and S\u0142awomir Nowaczyk. 2016. Evaluation of micro-flaws in metallic material based on a self-organized data-driven approach. In Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM\u201916). IEEE, Los Alamitos, CA, 1\u20135."},{"key":"e_1_3_2_49_2","first-page":"1","volume-title":"Proceedings of the 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks, and Information Processing (ISSNIP\u201914)","author":"Thangavel Dinesh","year":"2014","unstructured":"Dinesh Thangavel, Xiaoping Ma, Alvin Valera, Hwee-Xian Tan, and Colin Keng-Yan Tan. 2014. Performance evaluation of MQTT and CoAP via a common middleware. In Proceedings of the 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks, and Information Processing (ISSNIP\u201914). IEEE, Los Alamitos, CA, 1\u20136."},{"key":"e_1_3_2_50_2","first-page":"245","volume-title":"Proceedings of the IFIP International Conference on Advances in Production Management Systems","author":"Stietencron Moritz von","year":"2020","unstructured":"Moritz von Stietencron, Marco Lewandowski, Katerina Lepenioti, Alexandros Bousdekis, Karl Hribernik, Dimitris Apostolou, and Gregoris Mentzas. 2020. Streaming analytics in edge-cloud environment for logistics processes. In Proceedings of the IFIP International Conference on Advances in Production Management Systems. 245\u2013253."},{"key":"e_1_3_2_51_2","volume-title":"Proceedings of the International Manufacturing Science and Engineering Conference","volume":"49903","author":"Wu Dazhong","year":"2016","unstructured":"Dazhong Wu, Janis Terpenny, Li Zhang, Robert Gao, and Thomas Kurfess. 2016. Fog-enabled architecture for data-driven cyber-manufacturing systems. In Proceedings of the International Manufacturing Science and Engineering Conference, Vol. 49903."},{"key":"e_1_3_2_52_2","volume-title":"Secured System Architecture for the Internet of Things Using a Two Factor Authentication Protocol","author":"Xiong Chuan","year":"2020","unstructured":"Chuan Xiong. 2020. Secured System Architecture for the Internet of Things Using a Two Factor Authentication Protocol. Ph.D. Dissertation. University of Ottawa."},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","unstructured":"Yilu Zhang Xinyu Du and Mutasim Salman. 2012. Peer-to-peer collaborative vehicle health management\u2014The concept and an initial study. In Proceedings of the Annual Conference of the Prognostics and Health Management Society .","DOI":"10.36001\/phmconf.2012.v4i1.2127"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2009.2020484"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11161"}],"container-title":["ACM Transactions on Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3530991","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3530991","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:27Z","timestamp":1750183767000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3530991"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,19]]},"references-count":54,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,8,31]]}},"alternative-id":["10.1145\/3530991"],"URL":"https:\/\/doi.org\/10.1145\/3530991","relation":{},"ISSN":["2691-1914","2577-6207"],"issn-type":[{"value":"2691-1914","type":"print"},{"value":"2577-6207","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,19]]},"assertion":[{"value":"2021-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}