{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T02:29:27Z","timestamp":1774924167090,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Science, Innovation and Universities and FEDER","award":["PID2021-124463OB-100"],"award-info":[{"award-number":["PID2021-124463OB-100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Future data-intensive intelligent applications are required to traverse across the cloud-to-edge-to-IoT continuum, where cloud and edge resources elegantly coordinate, alongside sensor networks and data. However, current technical solutions can only partially handle the data outburst associated with the IoT proliferation experienced in recent years, mainly due to their hierarchical architectures. In this context, this paper presents a reference architecture of a meta-operating system (RAMOS), targeted to enable a dynamic, distributed and trusted continuum which will be capable of facilitating the next-generation smart applications at the edge. RAMOS is domain-agnostic, capable of supporting heterogeneous devices in various network environments. Furthermore, the proposed architecture possesses the ability to place the data at the origin in a secure and trusted manner. Based on a layered structure, the building blocks of RAMOS are thoroughly described, and the interconnection and coordination between them is fully presented. Furthermore, illustration of how the proposed reference architecture and its characteristics could fit in potential key industrial and societal applications, which in the future will require more power at the edge, is provided in five practical scenarios, focusing on the distributed intelligence and privacy preservation principles promoted by RAMOS, as well as the concept of environmental footprint minimization. Finally, the business potential of an open edge ecosystem and the societal impacts of climate net neutrality are also illustrated.<\/jats:p>","DOI":"10.3390\/s22229003","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T05:18:57Z","timestamp":1669094337000},"page":"9003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Reference Architecture for Cloud\u2013Edge Meta-Operating Systems Enabling Cross-Domain, Data-Intensive, ML-Assisted Applications: Architectural Overview and Key Concepts"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5146-5954","authenticated-orcid":false,"given":"Panagiotis","family":"Trakadas","sequence":"first","affiliation":[{"name":"National and Kapodistrian, Department of Port Management & Shipping, University of Athens, Psachna, Evia, 34400 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4755-556X","authenticated-orcid":false,"given":"Xavi","family":"Masip-Bruin","sequence":"additional","affiliation":[{"name":"CRAAX, Universitat Politecnica de Catalunya, 08800 Vilanova i la Geltru, Spain"}]},{"given":"Federico M.","family":"Facca","sequence":"additional","affiliation":[{"name":"Martel Lab, Martel GMBH, 6900 Lugano, Switzerland"}]},{"given":"Sotirios T.","family":"Spantideas","sequence":"additional","affiliation":[{"name":"National and Kapodistrian, Department of Port Management & Shipping, University of Athens, Psachna, Evia, 34400 Athens, Greece"}]},{"given":"Anastasios E.","family":"Giannopoulos","sequence":"additional","affiliation":[{"name":"National and Kapodistrian, Department of Port Management & Shipping, University of Athens, Psachna, Evia, 34400 Athens, Greece"}]},{"given":"Nikolaos C.","family":"Kapsalis","sequence":"additional","affiliation":[{"name":"Four Dot Infinity PC, Leof. Kifisias 208, 15231 Chalandri, Greece"}]},{"given":"Rui","family":"Martins","sequence":"additional","affiliation":[{"name":"Smart Energy Lab, Avenida 24 de Julho, n\u00b0 12, 1249-300 Lisboa, Portugal"}]},{"given":"Enrica","family":"Bosani","sequence":"additional","affiliation":[{"name":"Whirlpool EMEA, Via Carlo Pisacane n. 1, 20016 Pero, Italy"}]},{"given":"Joan","family":"Ramon","sequence":"additional","affiliation":[{"name":"IDNEO Technologies SAU Nextium, Carrer Rec de Dalt, 3 Mollet del Vall\u00e8s, 08100 Barcelona, Spain"}]},{"given":"Ra\u00fcl Gonz\u00e1lez","family":"Prats","sequence":"additional","affiliation":[{"name":"Retevision I SA Cellnex, Avinguda del Parc Logistic, 08040 Barcelona, Spain"}]},{"given":"George","family":"Ntroulias","sequence":"additional","affiliation":[{"name":"Hydrus Engineering SA, Leof. Mesogeion 515, 15343 Agia Paraskevi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0812-0560","authenticated-orcid":false,"given":"Dimitrios V.","family":"Lyridis","sequence":"additional","affiliation":[{"name":"Laboratory for Maritime Transport 9, School of Naval Architecture and Marine Engineering, National Technical University of Athens, Heroon Polytechniou St, Zografou, 15773 Athens, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"ref_1","unstructured":"Kaloxylos, A., Gavras, A., Camps, D., and Ghoraishi, M. (2021). AI and ML\u2013Enablers for beyond 5G Networks, Centres de Recerca de Catalunya (CERCA)."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20717","DOI":"10.1109\/ACCESS.2021.3054129","article-title":"Machine Learning for Cloud Security: A Systematic Review","volume":"9","author":"Nassif","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Trakadas, P., Nomikos, N., Michailidis, E.T., Zahariadis, T., Facca, F.M., Breitgand, D., Rizou, S., Masip, X., and Gkonis, P. (2019). Hybrid Clouds for Data-Intensive, 5G-Enabled IoT Applications: An Overview, Key Issues and Relevant Architecture. Sensors, 19.","DOI":"10.3390\/s19163591"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5517","DOI":"10.1007\/s11276-019-02042-2","article-title":"Distributed Machine Learning Load Balancing Strategy in Cloud Computing Services","volume":"26","author":"Li","year":"2020","journal-title":"Wirel. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tanaka, R., Papadimitriou, G., Viswanath, S.C., Wang, C., Lyons, E., Thareja, K., Qu, C., Esquivel, A., Deelman, E., and Mandal, A. (2022, January 16\u201319). Automating Edge-to-Cloud Workflows for Science: Traversing the Edge-to-Cloud Continuum with Pegasus. Proceedings of the 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing, Taormina, Italy.","DOI":"10.1109\/CCGrid54584.2022.00098"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101101","DOI":"10.1016\/j.aei.2020.101101","article-title":"Predictive Model-Based Quality Inspection Using Machine Learning and Edge Cloud Computing","volume":"45","author":"Schmitt","year":"2020","journal-title":"Adv. Eng. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Trakadas, P., Sarakis, L., Giannopoulos, A., Spantideas, S., Capsalis, N., Gkonis, P., Karkazis, P., Rigazzi, G., Antonopoulos, A., and Cambeiro, M.A. (2021). A Cost-Efficient 5G Non-Public Network Architectural Approach: Key Concepts and Enablers, Building Blocks and Potential Use Cases. Sensors, 21.","DOI":"10.3390\/s21165578"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"313","DOI":"10.12694\/scpe.v22i3.1941","article-title":"Service Deployment Challenges in Cloud-to-Edge Continuum","volume":"22","author":"Petcu","year":"2021","journal-title":"Scalable Comput. Pract. Exp."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MC.2020.3007297","article-title":"The Edge-to-Cloud Continuum","volume":"53","author":"Milojicic","year":"2020","journal-title":"Computer"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/978-3-030-49190-1_9","article-title":"Programmable Edge-to-Cloud Virtualization for 5G Media Industry: The 5G-MEDIA Approach","volume":"Volume 585","author":"Rizou","year":"2020","journal-title":"IFIP Advances in Information and Communication Technology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1109\/TBC.2019.2901400","article-title":"An Edge-to-Cloud Virtualized Multimedia Service Platform for 5G Networks","volume":"65","author":"Alvarez","year":"2019","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_12","first-page":"100250","article-title":"A UAV-Based Moving 5G RAN for Massive Connectivity of Mobile Users and IoT Devices","volume":"25","author":"Nomikos","year":"2020","journal-title":"Veh. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1504\/IJCC.2012.049763","article-title":"TransOS: A Transparent Computing-Based Operating System for the Cloud","volume":"1","author":"Zhang","year":"2012","journal-title":"Int. J. Cloud Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Joseph, L. (2018). Robot Operating System for Absolute Beginners, Springer.","DOI":"10.1007\/978-1-4842-3405-1"},{"key":"ref_15","unstructured":"Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., and Ng, A. (2022, October 01). ROS: An Open-Source Robot Operating System. Available online: https:\/\/www.researchgate.net\/publication\/233881999_ROS_an_open-source_Robot_Operating_System."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103","DOI":"10.25103\/jestr.111.12","article-title":"Boosting the Cloud Meta-Operating System with Heterogeneous Kernels. A Novel Approach Based on Containers and Microservices","volume":"11","author":"Debab","year":"2018","journal-title":"Artic. J. Eng. Sci. Technol. Rev."},{"key":"ref_17","first-page":"475","article-title":"Swarm Intelligence: A Review of Algorithms","volume":"10","author":"Chakraborty","year":"2017","journal-title":"Model. Optim. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kennedy, J. (2006). Swarm Intelligence. Handbook of Nature-Inspired and Innovative Computing, Springer.","DOI":"10.1007\/0-387-27705-6_6"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Harmon, R.R., Castro-Leon, E.G., and Bhide, S. (2015, January 2\u20136). Smart Cities and the Internet of Things. Proceedings of the 2015 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR, USA.","DOI":"10.1109\/PICMET.2015.7273174"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Masip-Bruin, X., Mar\u00edn-Tordera, E., S\u00e1nchez-L\u00f3pez, S., Garcia, J., Jukan, A., Ferrer, A.J., Queralt, A., Salis, A., Bartoli, A., and Cankar, M. (2021). Managing the Cloud Continuum: Lessons Learnt from a Real Fog-to-Cloud Deployment. Sensors, 21.","DOI":"10.20944\/preprints202104.0074.v1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s10723-021-09589-5","article-title":"MiCADO-Edge: Towards an Application-Level Orchestrator for the Cloud-to-Edge Computing Continuum","volume":"19","author":"Ullah","year":"2021","journal-title":"J. Grid Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Luckow, A., Rattan, K., and Jha, S. (2021, January 17\u201321). Pilot-Edge: Distributed Resource Management along the Edge-to-Cloud Continuum. Proceedings of the 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Portland, OR, USA.","DOI":"10.1109\/IPDPSW52791.2021.00130"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.cose.2017.08.016","article-title":"A Cybersecurity Framework to Identify Malicious Edge Device in Fog Computing and Cloud-of-Things Environments","volume":"74","author":"Sohal","year":"2018","journal-title":"Comput. Secur."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pan, J., and Yang, Z. (2018, January 21). Cybersecurity Challenges and Opportunities in the New \u201cEdge Computing + IoT\u201d World. Proceedings of the 2018 ACM International Workshop on Security in Software Defined. Networks & Network Function Virtualization, Tempe, AZ, USA.","DOI":"10.1145\/3180465.3180470"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100283","DOI":"10.1109\/ACCESS.2019.2930000","article-title":"Self-Service Cybersecurity Monitoring as Enabler for DevSecops","volume":"7","author":"Mena","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Radovanovic, A., Koningstein, R., Schneider, I., Chen, B., Duarte, A., Roy, B., Xiao, D., Haridasan, M., Hung, P., and Care, N. (2021). Carbon-Aware Computing for Datacenters. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pihkola, H., Hongisto, M., Apilo, O., and Lasanen, M. (2018). Evaluating the Energy Consumption of Mobile Data Transfer\u2014From Technology Development to Consumer Behaviour and Life Cycle Thinking. Sustainability, 10.","DOI":"10.3390\/su10072494"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Spantideas, S.T., Giannopoulos, A.E., Kapsalis, N.C., Kalafatelis, A., Capsalis, C.N., and Trakadas, P. (2021, January 7\u201310). Joint Energy-Efficient and Throughput-Sufficient Transmissions in 5G Cells with Deep Q-Learning. Proceedings of the 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), Athens, Greece.","DOI":"10.1109\/MeditCom49071.2021.9647592"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"39580","DOI":"10.1109\/ACCESS.2022.3166160","article-title":"Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI\/ML Workflow, and Use Cases","volume":"10","author":"Giannopoulos","year":"2022","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Karamplias, T., Spantideas, S.T., Giannopoulos, A.E., Gkonis, P., Kapsalis, N., and Trakadas, P. (2022, January 7\u201310). Towards Closed-Loop Automation in 5G Open RAN: Coupling an Open-Source Simulator with XApps. Proceedings of the 2022 Joint European Conference on Networks and Communications and 6G Summit (EuCNC\/6G Summit 2022), Grenoble, France.","DOI":"10.1109\/EuCNC\/6GSummit54941.2022.9815658"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Giannopoulos, A., Spantideas, S., Capsalis, N., Gkonis, P., Karkazis, P., Sarakis, L., Trakadas, P., and Capsalis, C. (2022, January 14\u201317). WIP: Demand-Driven Power Allocation in Wireless Networks with Deep q-Learning. Proceedings of the 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2021), Belfast, UK.","DOI":"10.1109\/WoWMoM51794.2021.00045"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/978-3-030-79157-5_9","article-title":"Power Control in 5G Heterogeneous Cells Considering User Demands Using Deep Reinforcement Learning","volume":"Volume 628","author":"Giannopoulos","year":"2021","journal-title":"IFIP Advances in Information and Communication Technology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/MCC.2014.51","article-title":"Containers and Cloud: From LXC to Docker to Kubernetes","volume":"1","author":"Bernstein","year":"2014","journal-title":"IEEE Cloud Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/MWC.2016.7721750","article-title":"Foggy Clouds and Cloudy Fogs: A Real Need for Coordinated Management of Fog-to-Cloud Computing Systems","volume":"23","author":"Tashakor","year":"2016","journal-title":"IEEE Wirel. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lewis, G., Echeverr\u00eda, S., Simanta, S., Bradshaw, B., and Root, J. (2014, January 6\u20138). Tactical Cloudlets: Moving Cloud Computing to the Edge. Proceedings of the 2014 IEEE Military Communications Conference (MILCOM 2014), Baltimore, MD, USA.","DOI":"10.1109\/MILCOM.2014.238"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.comcom.2018.11.011","article-title":"Dynamic Resource Allocation Strategy for Latency-Critical and Computation-Intensive Applications in Cloud\u2013Edge Environment","volume":"134","author":"Tang","year":"2019","journal-title":"Comput. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Maheshwari, S., Raychaudhuri, D., Seskar, I., and Bronzino, F. (2018, January 25\u201327). Scalability and Performance Evaluation of Edge Cloud Systems for Latency Constrained Applications. Proceedings of the 2018 IEEE\/ACM Symposium on Edge Computing, SEC 2018, Seattle, WA, USA.","DOI":"10.1109\/SEC.2018.00028"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ferrag, M.A., Derhab, A., Maglaras, L., Mukherjee, M., and Janicke, H. (2018, January 27\u201331). Privacy-Preserving Schemes for Fog-Based IoT Applications: Threat Models, Solutions, and Challenges. Proceedings of the 7th IEEE International Conference on Smart Communications in Network Technologies (SACONET\u201918), El Oued, Algeria.","DOI":"10.1109\/SaCoNeT.2018.8585538"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hou, S., Li, H., Yang, C., and Wang, L. (2020, January 25\u201328). A New Privacy-Preserving Framework Based on Edge-Fog-Cloud Continuum for Load Forecasting. Proceedings of the 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Republic of Korea.","DOI":"10.1109\/WCNC45663.2020.9120680"},{"key":"ref_40","unstructured":"Bugshan, N., Khalil, I., Moustafa, N., and Rahman, M.S. (2021). Privacy-Preserving Microservices in Industrial Internet of Things Driven Smart Applications. IEEE Internet Things J."},{"key":"ref_41","first-page":"14","article-title":"BlockShare: A Blockchain Empowered System for Privacy-Preserving Verifiable Data Sharing","volume":"1","author":"Peng","year":"2022","journal-title":"Bull. IEEE Comput. Soc. Tech. Comm. Data Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1109\/TPDS.2021.3113873","article-title":"VQL: Efficient and Verifiable Cloud Query Services for Blockchain Systems","volume":"33","author":"Wu","year":"2022","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1111\/jiec.12630","article-title":"Electricity Intensity of Internet Data Transmission: Untangling the Estimates","volume":"22","author":"Aslan","year":"2018","journal-title":"J. Ind. Ecol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Li, J., Peng, Z., Xiao, B., and Hua, Y. (2022, January 22\u201325). Make Smartphones Last a Day: Pre-Processing Based Computer Vision Application Offloading. Proceedings of the 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking-Workshops (SECON Workshops), Seattle, WA, USA.","DOI":"10.1109\/SAHCN.2015.7338347"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.comcom.2016.08.011","article-title":"Smartphone-Assisted Energy Efficient Data Communication for Wearable Devices","volume":"105","author":"Li","year":"2017","journal-title":"Comput. Commun."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Gao, S., Peng, Z., Xiao, B., Xiao, Q., and Song, Y. (2017, January 14\u201316). SCoP: Smartphone Energy Saving by Merging Push Services in Fog Computing. Proceedings of the 2017 IEEE\/ACM 25th International Symposium on Quality of Service (IWQoS), Vilanova i la Geltr\u00fa, Spain.","DOI":"10.1109\/IWQoS.2017.7969114"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2598","DOI":"10.1109\/TNSE.2020.2988052","article-title":"An Intelligent Dynamic Offloading from Cloud to Edge for Smart IoT Systems with Big Data","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"64983","DOI":"10.1109\/ACCESS.2021.3074962","article-title":"Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things","volume":"9","author":"Nezami","year":"2021","journal-title":"IEEE Access"},{"key":"ref_49","first-page":"1171","article-title":"Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption","volume":"3","author":"Deng","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1109\/JSTSP.2020.2969554","article-title":"DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks","volume":"14","author":"Wiedemann","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/MWC.011.2000467","article-title":"Over-the-Air Computing for Wireless Data Aggregation in Massive IoT","volume":"28","author":"Zhu","year":"2021","journal-title":"IEEE Wirel. Commun."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"38457","DOI":"10.1109\/ACCESS.2021.3063291","article-title":"Accelerating Federated Learning for IoT in Big Data Analytics with Pruning, Quantization and Selective Updating","volume":"9","author":"Xu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Masip-Bruin, X., Mar\u00edn-Tordera, E., Ruiz, J., Jukan, A., Trakadas, P., Cernivec, A., Lioy, A., L\u00f3pez, D., Santos, H., and Gonos, A. (2021). Cybersecurity in ICT Supply Chains: Key Challenges and a Relevant Architecture. Sensors, 21.","DOI":"10.3390\/s21186057"},{"key":"ref_54","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., and Ag\u00fcera y Arcas, B. (2017, January 20\u201322). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, Lauderdale, FL, USA."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TWC.2019.2946140","article-title":"Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing","volume":"19","author":"Li","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3710","DOI":"10.1109\/TNNLS.2020.3015958","article-title":"Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization under Privacy Constraints","volume":"32","author":"Sattler","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_57","unstructured":"Sabater, C., Bellet, A., and Ramon, J. (2020). An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging. Mach. Learn., 1\u201345."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5488","DOI":"10.1109\/TWC.2020.2993703","article-title":"Over-the-Air Computation Systems: Optimization, Analysis and Scaling Laws","volume":"19","author":"Liu","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"129358","DOI":"10.1109\/ACCESS.2021.3113501","article-title":"Deep Reinforcement Learning for Energy-Efficient Multi-Channel Transmissions in 5G Cognitive HetNets: Centralized, Decentralized and Transfer Learning Based Solutions","volume":"9","author":"Giannopoulos","year":"2021","journal-title":"IEEE Access"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11783-019-1212-6","article-title":"Real-World Fuel Consumption of Light-Duty Passenger Vehicles Using on-Board Diagnostic (OBD) Systems","volume":"14","author":"Zheng","year":"2020","journal-title":"Front. Environ. Sci. Eng."},{"key":"ref_61","first-page":"2000","article-title":"Interfacing to the On-Board Diagnostic System","volume":"4","author":"Godavarty","year":"2000","journal-title":"IEEE Veh. Technol. Conf."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sadhu, V., Zonouz, S., and Pompili, D. (August, January 31). On-Board Deep-Learning-Based Unmanned Aerial Vehicle Fault Cause Detection and Identification. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197071"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.engappai.2016.10.002","article-title":"Fault Diagnosis Network Design for Vehicle On-Board Equipments of High-Speed Railway: A Deep Learning Approach","volume":"56","author":"Yin","year":"2016","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1109\/TSG.2021.3082622","article-title":"Generalizability Improvement of Deep Learning-Based Non-Intrusive Load Monitoring System Using Data Augmentation","volume":"12","author":"Rafiq","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1109\/TSG.2016.2532885","article-title":"Non-Intrusive Load Monitoring Using Semi-Supervised Machine Learning and Wavelet Design","volume":"8","author":"Gillis","year":"2017","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10462-018-9613-7","article-title":"Machine Learning Approaches for Non-Intrusive Load Monitoring: From Qualitative to Quantitative Comparation","volume":"52","author":"Nalmpantis","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Lee, P.T.W., Kwon, O.K., and Ruan, X. (2019). Sustainability Challenges in Maritime Transport and Logistics Industry and Its Way Ahead. Sustainability, 11.","DOI":"10.3390\/su11051331"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.trpro.2020.03.059","article-title":"Improvement of the Sustainability of Ports Logistics by the Development of Innovative Green Infrastructure Solutions","volume":"45","author":"Twrdy","year":"2020","journal-title":"Transp. Res. Procedia"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Angelopoulos, A., Michailidis, E.T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., and Zahariadis, T. (2019). Tackling Faults in the Industry 4.0 Era\u2014A Survey of Machine-Learning Solutions and Key Aspects. Sensors, 20.","DOI":"10.3390\/s20010109"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.cirpj.2011.03.007","article-title":"A Framework for Modelling Energy Consumption within Manufacturing Systems","volume":"4","author":"Seow","year":"2011","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4198","DOI":"10.1021\/acs.energyfuels.2c00238","article-title":"Toward Net-Zero Emission Fertilizers Industry: Greenhouse Gas Emission Analyses and Decarbonization Solutions","volume":"36","author":"Ouikhalfan","year":"2022","journal-title":"Energy Fuels"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.techfore.2018.06.040","article-title":"The Role of Existing Infrastructure of Fuel Stations in Deploying Solar Charging Systems, Electric Vehicles and Solar Energy: A Preliminary Analysis","volume":"137","author":"Alghoul","year":"2018","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1109\/JSTSP.2014.2336624","article-title":"Optimized Electric Vehicle Charging with Intermittent Renewable Energy Sources","volume":"8","author":"Jin","year":"2014","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/978-3-031-08337-2_7","article-title":"Allocating Orders to Printing Machines for Defect Minimization: A Comparative Machine Learning Approach","volume":"Volume 647","author":"Angelopoulos","year":"2022","journal-title":"IFIP Advances in Information and Communication Technology"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/9003\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:22:46Z","timestamp":1760145766000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/9003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,21]]},"references-count":74,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22229003"],"URL":"https:\/\/doi.org\/10.3390\/s22229003","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,21]]}}}