{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T18:45:55Z","timestamp":1785955555630,"version":"3.56.0"},"reference-count":282,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"HORIZON research and innovation program of the European Union","award":["101070177"],"award-info":[{"award-number":["101070177"]}]},{"name":"HORIZON research and innovation program of the European Union","award":["101189589"],"award-info":[{"award-number":["101189589"]}]},{"name":"HORIZON EU program","award":["101070177"],"award-info":[{"award-number":["101070177"]}]},{"name":"HORIZON EU program","award":["101189589"],"award-info":[{"award-number":["101189589"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Intelligent edge machine learning has emerged as a paradigm for deploying smart applications across resource-constrained devices in next-generation network infrastructures. This survey addresses the critical challenges of implementing machine learning models on edge devices within distributed network environments, including computational limitations, memory constraints, and energy-efficiency requirements for real-time intelligent inference. We provide comprehensive analysis of soft computing optimization strategies essential for intelligent edge deployment, systematically examining model compression techniques including pruning, quantization methods, knowledge distillation, and low-rank decomposition approaches. The survey explores intelligent MLOps frameworks tailored for network edge environments, addressing continuous model adaptation, monitoring under data drift, and federated learning for distributed intelligence while preserving privacy in next-generation networks. Our work covers practical applications across intelligent smart agriculture, energy management, healthcare, and industrial monitoring within network infrastructures, highlighting domain-specific challenges and emerging solutions. We analyze specialized hardware architectures, cloud offloading strategies, and distributed learning approaches that enable intelligent edge computing in heterogeneous network environments. The survey identifies critical research gaps in multimodal model deployment, streaming learning under concept drift, and integration of soft computing techniques with intelligent edge orchestration frameworks for network applications. These gaps directly manifest as open challenges in balancing computational efficiency with model robustness due to limited multimodal optimization techniques, developing sustainable intelligent edge AI systems arising from inadequate streaming learning adaptation, and creating adaptive network applications for dynamic environments resulting from insufficient soft computing integration. This comprehensive roadmap synthesizes current intelligent edge machine learning solutions with emerging soft computing approaches, providing researchers and practitioners with insights for developing next-generation intelligent edge computing systems that leverage machine learning capabilities in distributed network infrastructures.<\/jats:p>","DOI":"10.3390\/fi17090417","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T14:23:50Z","timestamp":1757600630000},"page":"417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Intelligent Edge Computing and Machine Learning: A Survey of Optimization and Applications"],"prefix":"10.3390","volume":"17","author":[{"given":"Sebasti\u00e1n A.","family":"Cajas Ord\u00f3\u00f1ez","sequence":"first","affiliation":[{"name":"Ireland\u2019s Centre for Artificial Intelligence (CeADAR), University College Dublin, Belfield, D04 V2N9 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5716-5545","authenticated-orcid":false,"given":"Jaydeep","family":"Samanta","sequence":"additional","affiliation":[{"name":"Ireland\u2019s Centre for Artificial Intelligence (CeADAR), University College Dublin, Belfield, D04 V2N9 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5266-5053","authenticated-orcid":false,"given":"Andr\u00e9s L.","family":"Su\u00e1rez-Cetrulo","sequence":"additional","affiliation":[{"name":"Ireland\u2019s Centre for Artificial Intelligence (CeADAR), University College Dublin, Belfield, D04 V2N9 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo Sim\u00f3n","family":"Carbajo","sequence":"additional","affiliation":[{"name":"Ireland\u2019s Centre for Artificial Intelligence (CeADAR), University College Dublin, Belfield, D04 V2N9 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","article-title":"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing","volume":"107","author":"Zhou","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.1109\/COMST.2020.3007787","article-title":"Communication-Efficient Edge AI: Algorithms and Systems","volume":"22","author":"Shi","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7457","DOI":"10.1109\/JIOT.2020.2984887","article-title":"Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence","volume":"7","author":"Deng","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_4","first-page":"23","article-title":"Edge cloud offloading algorithms: Issues, methods, and perspectives","volume":"52","author":"Wang","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_5","unstructured":"Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., and Hui, P. (2020). Edge Intelligence: Architectures, Challenges, and Applications. arXiv."},{"key":"ref_6","first-page":"97","article-title":"Resource management in fog\/edge computing: A survey on architectures, infrastructure, and algorithms","volume":"52","author":"Hong","year":"2019","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_7","unstructured":"Ashish, V. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_8","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1561\/0600000105","article-title":"Vision-language pre-training: Basics, recent advances, and future trends","volume":"14","author":"Gan","year":"2022","journal-title":"Found. Trends\u00ae Comput. Graph. Vis."},{"key":"ref_11","unstructured":"Chu, X., Qiao, L., Lin, X., Xu, S., Yang, Y., Hu, Y., Wei, F., Zhang, X., Zhang, B., and Wei, X. (2023). Mobilevlm: A fast, reproducible and strong vision language assistant for mobile devices. arXiv."},{"key":"ref_12","unstructured":"Chu, X., Qiao, L., Zhang, X., Xu, S., Wei, F., Yang, Y., Sun, X., Hu, Y., Lin, X., and Zhang, B. (2024). MobileVLM V2: Faster and Stronger Baseline for Vision Language Model. arXiv."},{"key":"ref_13","unstructured":"Ma, S., Wang, H., Ma, L., Wang, L., Wang, W., Huang, S., Dong, L., Wang, R., Xue, J., and Wei, F. (2024). The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bernab\u00e9-S\u00e1nchez, I., Fern\u00e1ndez, A., Billhardt, H., and Ossowski, S. (2023). Problem Detection in the Edge of IoT Applications. IJIMAI, 8.","DOI":"10.9781\/ijimai.2023.07.007"},{"key":"ref_15","unstructured":"Makhija, D., Han, X., Ho, N., and Ghosh, J. (2022). Architecture Agnostic Federated Learning for Neural Networks. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.comcom.2020.01.004","article-title":"Energy aware edge computing: A survey","volume":"151","author":"Jiang","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_17","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A.Y. (2017, January 20\u201322). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the Artificial Intelligence and Statistics, PMLR, Fort Lauderdale, FL, USA."},{"key":"ref_18","unstructured":"Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Kone\u010dn\u00fd, J., Mazzocchi, S., and McMahan, H.B. (2019). Towards Federated Learning at Scale: System Design. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Filho, C.P., Marques, E., Chang, V., dos Santos, L., Bernardini, F., Pires, P.F., Ochi, L., and Delicato, F.C. (2022). A Systematic Literature Review on Distributed Machine Learning in Edge Computing. Sensors, 22.","DOI":"10.3390\/s22072665"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Plastiras, G., Terzi, M., Kyrkou, C., and Theocharidcs, T. (2018, January 10\u201312). Edge Intelligence: Challenges and Opportunities of Near-Sensor Machine Learning Applications. Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors, Milano, Italy.","DOI":"10.1109\/ASAP.2018.8445118"},{"key":"ref_21","first-page":"263","article-title":"Wireless Powered Mobile Edge Computing Networks: A Survey","volume":"55","author":"Wang","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M.A., and Dally, W.J. (2016, January 18\u201322). EIE: Efficient Inference Engine on Compressed Deep Neural Network. Proceedings of the\u20142016 43rd International Symposium on Computer Architecture, Seoul, Republic of Korea.","DOI":"10.1109\/ISCA.2016.30"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","article-title":"Efficient Processing of Deep Neural Networks: A Tutorial and Survey","volume":"105","author":"Sze","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_24","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021). Lora: Low-rank adaptation of large language models. arXiv."},{"key":"ref_25","unstructured":"Xu, Y., Xie, L., Gu, X., Chen, X., Chang, H., Zhang, H., Chen, Z., Zhang, X., and Tian, Q. (2023). Qa-lora: Quantization-aware low-rank adaptation of large language models. arXiv."},{"key":"ref_26","unstructured":"Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L. (2023). Qlora: Efficient finetuning of quantized llms. arXiv."},{"key":"ref_27","unstructured":"Liu, S.Y., Wang, C.Y., Yin, H., Molchanov, P., Wang, Y.C.F., Cheng, K.T., and Chen, M.H. (2024). DoRA: Weight-Decomposed Low-Rank Adaptation. arXiv."},{"key":"ref_28","unstructured":"Wang, H., Ma, S., Dong, L., Huang, S., Wang, H., Ma, L., Yang, F., Wang, R., Wu, Y., and Wei, F. (2023). Bitnet: Scaling 1-bit transformers for large language models. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Alemdar, H., Leroy, V., Prost-Boucle, A., and P\u00e9trot, F. (2017, January 14\u201319). Ternary neural networks for resource-efficient AI applications. Proceedings of the IEEE 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966166"},{"key":"ref_30","unstructured":"Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., and Bengio, Y. (2016). Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or \u22121. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge Distillation: A Survey","volume":"129","author":"Gou","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1109\/JIOT.2015.2505901","article-title":"Operating Systems for Low-End Devices in the Internet of Things: A Survey","volume":"3","author":"Hahm","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_33","unstructured":"Pope, R., Douglas, S., Chowdhery, A., Devlin, J., Bradbury, J., Levskaya, A., Heek, J., Xiao, K., Agrawal, S., and Dean, J. (2022). Efficiently Scaling Transformer Inference. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhu, X., Zhang, J., Cao, B., Bao, W., and Yu, P.S. (2018, January 19\u201323). Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK.","DOI":"10.1145\/3219819.3220106"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.iot.2018.08.005","article-title":"The Internet of Things: Foundational ethical issues","volume":"1\u20132","author":"Allhoff","year":"2018","journal-title":"Internet Things"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Iftikhar, S., Gill, S.S., Song, C., Xu, M., Aslanpour, M.S., Toosi, A.N., Du, J., Wu, H., Ghosh, S., and Chowdhury, D. (2023). AI-based fog and edge computing: A systematic review, taxonomy and future directions. arXiv.","DOI":"10.1016\/j.iot.2022.100674"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hamdan, S., Ayyash, M., and Almajali, S. (2020). Edge-Computing Architectures for Internet of Things Applications: A Survey. Sensors, 20.","DOI":"10.3390\/s20226441"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/COMST.2019.2943405","article-title":"Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective","volume":"22","author":"Rodrigues","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"103044","DOI":"10.1016\/j.adhoc.2022.103044","article-title":"A survey of mobility-aware Multi-access Edge Computing: Challenges, use cases and future directions","volume":"140","author":"Singh","year":"2023","journal-title":"Ad Hoc Netw."},{"key":"ref_41","first-page":"30","article-title":"A Survey on Distributed Machine Learning","volume":"53","author":"Verbraeken","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6900","DOI":"10.1109\/ACCESS.2017.2778504","article-title":"A Survey on the Edge Computing for the Internet of Things","volume":"6","author":"Yu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/JPROC.2024.3437365","article-title":"Green edge AI: A contemporary survey","volume":"112","author":"Mao","year":"2024","journal-title":"Proc. IEEE"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1002\/spe.2660","article-title":"Container-based cluster orchestration systems: A taxonomy and future directions","volume":"49","author":"Rodriguez","year":"2019","journal-title":"Softw. Pract. Exp."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1145\/3510415","article-title":"Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions","volume":"54","author":"Zhong","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Casalicchio, E. (2019). Container Orchestration: A Survey. EAI\/Springer Innovations in Communication and Computing, Springer.","DOI":"10.1007\/978-3-319-92378-9_14"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"154300","DOI":"10.1109\/ACCESS.2019.2946884","article-title":"A survey of distributed data stream processing frameworks","volume":"7","author":"Isah","year":"2019","journal-title":"IEEE Access"},{"key":"ref_48","first-page":"423","article-title":"Multimodal machine learning: A survey and taxonomy","volume":"41","author":"Ahuja","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"14804","DOI":"10.1109\/ACCESS.2023.3243854","article-title":"A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions","volume":"11","author":"Barua","year":"2023","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., and Chen, E. (2023). A Survey on Multimodal Large Language Models. arXiv.","DOI":"10.1093\/nsr\/nwae403"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2939","DOI":"10.1007\/s00371-021-02166-7","article-title":"A survey on deep multimodal learning for computer vision: Advances, trends, applications, and datasets","volume":"38","author":"Bayoudh","year":"2022","journal-title":"Vis. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1109\/COMST.2023.3319952","article-title":"Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey","volume":"26","author":"Liu","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Surianarayanan, C., Lawrence, J.J., Chelliah, P.R., Prakash, E., and Hewage, C. (2023). A Survey on Optimization Techniques for Edge Artificial Intelligence (AI). Sensors, 23.","DOI":"10.3390\/s23031279"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1109\/COMST.2021.3075439","article-title":"Federated Learning for Internet of Things: A Comprehensive Survey","volume":"23","author":"Nguyen","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_56","first-page":"35","article-title":"Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey","volume":"37","author":"Wu","year":"2023","journal-title":"J. ACM"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/COMST.2023.3239579","article-title":"Offloading using Traditional Optimization and Machine Learning in Federated Cloud-Edge-Fog Systems: A Survey","volume":"25","author":"Kar","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s13748-011-0002-6","article-title":"A survey on learning from data streams: Current and future trends","volume":"1","author":"Gama","year":"2012","journal-title":"Prog. Artif. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"118934","DOI":"10.1016\/j.eswa.2022.118934","article-title":"A survey on machine learning for recurring concept drifting data streams","volume":"213","author":"Quintana","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1002\/widm.42","article-title":"Evolving fuzzy systems for data streams: A survey","volume":"1","author":"Baruah","year":"2011","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","article-title":"Ensemble learning for data stream analysis: A survey","volume":"37","author":"Krawczyk","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_62","first-page":"49","article-title":"Anomaly detection and mitigation at Internet scale: A survey","volume":"Volume 7943","author":"Steinberger","year":"2013","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.jss.2017.05.080","article-title":"Dataclay: A distributed data store for effective inter-player data sharing","volume":"131","author":"Queralt","year":"2017","journal-title":"J. Syst. Softw."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M.W., and Keutzer, K. (2021). A Survey of Quantization Methods for Efficient Neural Network Inference. arXiv.","DOI":"10.1201\/9781003162810-13"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1016\/j.rser.2012.02.049","article-title":"A review on the prediction of building energy consumption","volume":"16","author":"Zhao","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1016\/j.eneco.2012.11.022","article-title":"Smart meter devices and the effect of feedback on residential electricity consumption: Evidence from a natural experiment in Northern Ireland","volume":"36","author":"Gans","year":"2013","journal-title":"Energy Econ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1177\/0958305X17741385","article-title":"Smart meters and household electricity consumption: A case study in Ireland","volume":"29","author":"Rausser","year":"2017","journal-title":"Energy Environ."},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TII.2021.3091774","article-title":"Empowering IoT Predictive Maintenance Solutions with AI: A Distributed System for Manufacturing Plant-Wide Monitoring","volume":"18","author":"Liu","year":"2022","journal-title":"IEEE Trans. Ind. Informat."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Babaghayou, M., Chaib, N., Lagraa, N., Ferrag, M.A., and Maglaras, L. (2023). A Safety-Aware Location Privacy-Preserving IoV Scheme with Road Congestion-Estimation in Mobile Edge Computing. Sensors, 23.","DOI":"10.3390\/s23010531"},{"key":"ref_71","unstructured":"Maheshwari, S., Zhang, W., Seskar, I., Zhang, Y., and Raychaudhuri, D. (May, January 29). EdgeDrive: Supporting Advanced Driver AssistanceSystems using Mobile Edge Clouds Networks. Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France."},{"key":"ref_72","first-page":"2169","article-title":"Mobility-Aware Multi-Hop Task Offloading for Autonomous Driving in Vehicular Edge Computing and Networks","volume":"24","author":"Liu","year":"2022","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Leroux, S., Simoens, P., Lootus, M., Thakore, K., and Sharma, A. (June, January 30). TinyMLOps: Operational Challenges for Widespread Edge AI Adoption. Proceedings of the 2022 IEEE 36th International Parallel and Distributed Processing Symposium Workshops, Lyon, France.","DOI":"10.1109\/IPDPSW55747.2022.00160"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Symeonidis, G., Nerantzis, E., Kazakis, A., and Papakostas, G.A. (, January 26\u201329). MLOps\u2014Definitions, Tools and Challenges. Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, Las Vegas, NV, USA.","DOI":"10.1109\/CCWC54503.2022.9720902"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"107112","DOI":"10.1109\/ACCESS.2020.3000322","article-title":"An Effective Training Scheme for Deep Neural Network in Edge Computing Enabled Internet of Medical Things (IoMT) Systems","volume":"8","author":"Pustokhina","year":"2020","journal-title":"IEEE Access"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MIC.2021.3053711","article-title":"Toward Distributed, Global, Deep Learning Using IoT Devices","volume":"25","author":"Sudharsan","year":"2021","journal-title":"IEEE Internet Comput."},{"key":"ref_77","unstructured":"Paganini, M., and Forde, J. (2020). Streamlining tensor and network pruning in pytorch. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1007\/s10489-022-03486-4","article-title":"Teacher-student collaborative knowledge distillation for image classification","volume":"53","author":"Xu","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_79","unstructured":"Han, S., Mao, H., and Dally, W.J. (2015, January 7\u20139). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. Proceedings of the 4th International Conference on Learning Representations, ICLR 2016\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., and Zhang, C. (2017, January 22\u201329). Learning Efficient Convolutional Networks through Network Slimming. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D. (2018, January 18\u201323). Quantization and training of neural networks for efficient integer-arithmetic-only inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref_82","unstructured":"Choi, J., Wang, Z., Venkataramani, S., Chuang, P.I.J., Srinivasan, V., and Gopalakrishnan, K. (2018). PACT: Parameterized Clipping Activation for Quantized Neural Networks. arXiv."},{"key":"ref_83","unstructured":"Wang, K., Liu, Z., Lin, Y., Lin, J., and Han, S. (2018, January 18\u201323). HAQ: Hardware-Aware Automated Quantization with Mixed Precision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_84","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the Knowledge in a Neural Network. arXiv."},{"key":"ref_85","unstructured":"Jain, A., Bhattacharya, S., Masuda, M., Sharma, V., and Wang, Y. (2020). Efficient Execution of Quantized Deep Learning Models: A Compiler Approach. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., and Zisserman, A. (2014). Speeding up convolutional neural networks with low rank expansions. arXiv.","DOI":"10.5244\/C.28.88"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_88","unstructured":"Anubhav Singh, R.B. (2020). Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter: Build Scalable Real-World Projects to Implement End-to-End Neural Networks on Android and iOS, Packt Publishing Ltd."},{"key":"ref_89","unstructured":"Krishnamoorthi, R. (2018). Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv."},{"key":"ref_90","unstructured":"Xiao, G., Lin, J., Seznec, M., Wu, H., Demouth, J., and Han, S. (2023, January 23\u201329). Smoothquant: Accurate and efficient post-training quantization for large language models. Proceedings of the International Conference on Machine Learning, PMLR, Honolulu, HI, USA."},{"key":"ref_91","unstructured":"Frantar, E., Ashkboos, S., Hoefler, T., and Alistarh, D. (2023, January 1\u20135). OPTQ: Accurate quantization for generative pre-trained transformers. Proceedings of the 11th International Conference on Learning Representations, Kigali, Rwanda."},{"key":"ref_92","unstructured":"Lin, J., Tang, J., Tang, H., Yang, S., Dang, X., and Han, S. (2023). AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration. arXiv."},{"key":"ref_93","first-page":"4396","article-title":"Quip: 2-bit quantization of large language models with guarantees","volume":"36","author":"Chee","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_94","unstructured":"Sung, W., Shin, S., and Hwang, K. (2015). Resiliency of Deep Neural Networks under Quantization. arXiv."},{"key":"ref_95","unstructured":"Chen, G., Choi, W., Yu, X., Han, T., and Chandraker, M. (2017). Learning Efficient Object Detection Models with Knowledge Distillation, Curran Associates, Inc.. Advances in Neural Information Processing Systems."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Yim, J., Joo, D., Bae, J., and Kim, J. (2017, January 21\u201326). A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.754"},{"key":"ref_98","unstructured":"Fang, G., Song, J., Shen, C., Wang, X., Chen, D., and Song, M. (2020). Data-Free Adversarial Distillation. arXiv."},{"key":"ref_99","unstructured":"Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv."},{"key":"ref_100","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020). Language Models are Few-Shot Learners. arXiv."},{"key":"ref_101","unstructured":"Lee, S., and Song, B.C. (2019, January 9\u201312). Graph-based Knowledge Distillation by Multi-head Attention Network. Proceedings of the 30th British Machine Vision Conference 2019, BMVC 2019, Cardiff, UK."},{"key":"ref_102","unstructured":"Polino, A., Pascanu, R., and Alistarh, D. (May, January 30). Model compression via distillation and quantization. Proceedings of the 6th International Conference on Learning Representations, ICLR 2018\u2014Conference Track Proceedings, Vancouver, BC, Canada."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Chuang, Y.S., Su, S.Y., and Chen, Y.N. (2020, January 16\u201320). Lifelong Language Knowledge Distillation. Proceedings of the EMNLP 2020\u20142020 Conference on Empirical Methods in Natural Language Processing, Online Event.","DOI":"10.18653\/v1\/2020.emnlp-main.233"},{"key":"ref_104","unstructured":"Li, C., Farkhoor, H., Liu, R., and Yosinski, J. (2018). Measuring the intrinsic dimension of objective landscapes. arXiv."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Aghajanyan, A., Zettlemoyer, L., and Gupta, S. (2020). Intrinsic dimensionality explains the effectiveness of language model fine-tuning. arXiv.","DOI":"10.18653\/v1\/2021.acl-long.568"},{"key":"ref_106","unstructured":"Xu, L., Xie, H., Qin, S.Z.J., Tao, X., and Wang, F.L. (2023). Parameter-efficient fine-tuning methods for pretrained language models: A critical review and assessment. arXiv."},{"key":"ref_107","unstructured":"Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.F., and Dennison, D. (2015). Hidden Technical Debt in Machine Learning Systems, MIT Press. Advances in Neural Information Processing Systems."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Ruf, P., Madan, M., Reich, C., and Ould-Abdeslam, D. (2021). Demystifying mlops and presenting a recipe for the selection of open-source tools. Appl. Sci., 11.","DOI":"10.3390\/app11198861"},{"key":"ref_109","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_110","doi-asserted-by":"crossref","unstructured":"Raj, E., Buffoni, D., Westerlund, M., and Ahola, K. (2021, January 22). Edge MLOps: An Automation Framework for AIoT Applications. Proceedings of the 2021 IEEE International Conference on Cloud Engineering, IC2E 2021, San Francisco, CA, USA.","DOI":"10.1109\/IC2E52221.2021.00034"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"John, M.M., Olsson, H.H., and Bosch, J. (2021, January 3). Towards MLOps: A Framework and Maturity Model. Proceedings of the 2021 47th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2021, Palermo, Italy.","DOI":"10.1109\/SEAA53835.2021.00050"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Tamburri, D.A. (2020, January 1\u20134). Sustainable MLOps: Trends and Challenges. Proceedings of the 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2020, Timisoara, Romania.","DOI":"10.1109\/SYNASC51798.2020.00002"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"31866","DOI":"10.1109\/ACCESS.2023.3262138","article-title":"Machine Learning Operations (MLOps): Overview, Definition, and Architecture","volume":"11","author":"Kreuzberger","year":"2023","journal-title":"IEEE Access"},{"key":"ref_114","first-page":"39","article-title":"Accelerating the Machine Learning Lifecycle with MLflow","volume":"41","author":"Zaharia","year":"2018","journal-title":"IEEE Data Eng. Bull."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Bisong, E. (2019). Kubeflow and kubeflow pipelines. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, Springer.","DOI":"10.1007\/978-1-4842-4470-8"},{"key":"ref_116","unstructured":"Bodor, A., Hnida, M., and Najima, D. MLOps: Overview of current state and future directions. Proceedings of the International Conference on Smart City Applications."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"e13287","DOI":"10.1111\/exsy.13287","article-title":"The orchestration of Machine Learning frameworks with data streams and GPU acceleration in Kafka-ML: A deep-learning performance comparative","volume":"41","author":"Chaves","year":"2023","journal-title":"Expert Syst."},{"key":"ref_118","unstructured":"Klaise, J., Van Looveren, A., Cox, C., Vacanti, G., and Coca, A. (2020). Monitoring and explainability of models in production. arXiv."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"22166","DOI":"10.1109\/ACCESS.2025.3534990","article-title":"MLOps Components, Tools, Process, and Metrics: A Systematic Literature Review","volume":"13","author":"Milczarek","year":"2025","journal-title":"IEEE Access"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Barry, M., Bifet, A., and Billy, J.L. (2023, January 14). StreamAI: Dealing with Challenges of Continual Learning Systems for Serving AI in Production. Proceedings of the 2023 IEEE\/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), Melbourne, Australia.","DOI":"10.1109\/ICSE-SEIP58684.2023.00017"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Vela, D., Sharp, A., Zhang, R., Nguyen, T., Hoang, A., and Pianykh, O.S. (2022). Temporal quality degradation in AI models. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-15245-z"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Lobo, J.L., La\u00f1a, I., Osaba, E., and Del Ser, J. (2023). On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence. arXiv.","DOI":"10.1109\/CAI54212.2023.00081"},{"key":"ref_123","first-page":"4945","article-title":"River: Machine learning for streaming data in python","volume":"22","author":"Montiel","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref_124","unstructured":"Piangerelli, M., Prenkaj, B., Rotalinti, Y., Joshi, A., and Stilo,, G. Adaptive Machine Learning for Resource-Constrained Environments. Proceedings of the Discovering Drift Phenomena in Evolving Landscapes."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., and Yu, H. (2020). Introduction. Federated Learning, Springer International Publishing.","DOI":"10.1007\/978-3-031-01585-4_1"},{"key":"ref_126","first-page":"429","article-title":"Federated Optimization in Heterogeneous Networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref_127","unstructured":"Arivazhagan, M.G., Aggarwal, V., Singh, A.K., and Choudhary, S. (2019). Federated Learning with Personalization Layers. arXiv."},{"key":"ref_128","unstructured":"Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Kone\u010dn\u00fd, J., Kumar, S., and McMahan, H.B. (2021). Adaptive Federated Optimization. arXiv."},{"key":"ref_129","first-page":"8485","article-title":"SplitFed: When Federated Learning Meets Split Learning","volume":"36","author":"Thapa","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_130","first-page":"15","article-title":"Federated transfer learning for disaster classification in social computing networks","volume":"3","author":"Zhang","year":"2022","journal-title":"J. Saf. Sci. Resil."},{"key":"ref_131","unstructured":"Zhu, H., and Jin, Y. (2019). Multi-objective Evolutionary Federated Learning. arXiv."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"4159","DOI":"10.1109\/TII.2020.3012157","article-title":"Privacy-Aware Data Fusion and Prediction with Spatial-Temporal Context for Smart City Industrial Environment","volume":"17","author":"Qi","year":"2021","journal-title":"IEEE Trans. Ind. Informat."},{"key":"ref_133","unstructured":"Liu, X., Shi, T., Xie, C., Li, Q., Hu, K., Kim, H., Xu, X., Li, B., and Song, D. (2022). UniFed: A Benchmark for Federated Learning Frameworks. arXiv."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Kholod, I., Yanaki, E., Fomichev, D., Shalugin, E., Novikova, E., Filippov, E., and Nordlund, M. (2020). Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis. Sensors, 21.","DOI":"10.3390\/s21010167"},{"key":"ref_135","unstructured":"He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., and Qiu, H. (2020). FedML: A Research Library and Benchmark for Federated Machine Learning. arXiv."},{"key":"ref_136","unstructured":"Garcia, M.H., Manoel, A., Diaz, D.M., Mireshghallah, F., Sim, R., and Dimitriadis, D. (2022). FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations. arXiv."},{"key":"ref_137","unstructured":"Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., and van der Maaten, L. (2022). CrypTen: Secure Multi-Party Computation Meets Machine Learning. arXiv."},{"key":"ref_138","first-page":"89","article-title":"Fedtree: A federated learning system for trees","volume":"5","author":"Li","year":"2023","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref_139","unstructured":"Roth, H.R., Cheng, Y., Wen, Y., Yang, I., Xu, Z., Hsieh, Y.T., Kersten, K., Harouni, A., Zhao, C., and Lu, K. (2022). NVIDIA FLARE: Federated Learning from Simulation to Real-World. arXiv."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Ziller, A., Trask, A., Lopardo, A., Szymkow, B., Wagner, B., Bluemke, E., Nounahon, J.M., Passerat-Palmbach, J., Prakash, K., and Rose, N. (2021). Pysyft: A library for easy federated learning. Federated Learning Systems: Towards Next-Generation AI, Springer.","DOI":"10.1007\/978-3-030-70604-3_5"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Reddi, V.J., Cheng, C., Kanter, D., Mattson, P., Schmuelling, G., Wu, C.J., Anderson, B., Breughe, M., Charlebois, M., and Chou, W. (2020\u20133, January 30). Mlperf inference benchmark. Proceedings of the 2020 ACM\/IEEE 47th Annual International Symposium on Computer Architecture (ISCA).","DOI":"10.1109\/ISCA45697.2020.00045"},{"key":"ref_142","first-page":"66","article-title":"A survey on edge performance benchmarking","volume":"54","author":"Varghese","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_143","first-page":"336","article-title":"Mlperf training benchmark","volume":"2","author":"Mattson","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref_144","unstructured":"Saeed, E., and Coutinho, R.W. (2022, January 24\u201328). Performance evaluation of edge computing models for internet of things. Proceedings of the 12th ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, Montreal, QC, Canada."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Tu, X., Mallik, A., Chen, D., Han, K., Altintas, O., Wang, H., and Xie, J. (2023, January 6\u20139). Unveiling energy efficiency in deep learning: Measurement, prediction, and scoring across edge devices. Proceedings of the Eighth ACM\/IEEE Symposium on Edge Computing, Wilmington, DE, USA.","DOI":"10.1145\/3583740.3628442"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Fan, T., Qiu, Y., Jiang, C., and Wan, J. (2018, January 12). Energy aware edge computing: A survey. Proceedings of the International Workshop on High Performance Computing for Advanced Modeling and Simulation in Nuclear Energy and Environmental Science, Beijing, China.","DOI":"10.1007\/978-981-32-9987-0_8"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"100273","DOI":"10.1016\/j.iot.2020.100273","article-title":"Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research","volume":"12","author":"Aslanpour","year":"2020","journal-title":"Internet Things"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Caiazza, C., Luconi, V., and Vecchio, A. (2022, January 22). Saving energy on smartphones through edge computing: An experimental evaluation. Proceedings of the ACM SIGCOMM Workshop on Networked Sensing Systems for a Sustainable Society, Antipolis, France.","DOI":"10.1145\/3538393.3544935"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MM.2021.3066343","article-title":"The vision behind mlperf: Understanding ai inference performance","volume":"41","author":"Reddi","year":"2021","journal-title":"IEEE Micro"},{"key":"ref_150","unstructured":"Vo, T., Dave, P., Bajpai, G., and Kashef, R. (2022). Edge, Fog, and Cloud Computing: An Overview on Challenges and Applications. arXiv."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/978-981-13-7399-2_7","article-title":"Energy conservation in IoT-based smart home and its automation","volume":"206","author":"Singh","year":"2019","journal-title":"Stud. Syst. Decis. Control"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TCSS.2021.3072693","article-title":"Collaborative Edge Computing for Social Internet of Things: Applications, Solutions, and Challenges","volume":"9","author":"Dong","year":"2022","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"110480","DOI":"10.1109\/ACCESS.2021.3101397","article-title":"Energy-Efficient Edge-Fog-Cloud Architecture for IoT-Based Smart Agriculture Environment","volume":"9","author":"Alharbi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"107524","DOI":"10.1016\/j.compeleceng.2021.107524","article-title":"An edge AI-enabled IoT healthcare monitoring system for smart cities","volume":"96","author":"Rathi","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Kamruzzaman, M.M. (2021, January 7\u201311). New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Smart Cities. Proceedings of the 2021 IEEE Globecom Workshops, GC Wkshps 2021, Madrid, Spain.","DOI":"10.1109\/GCWkshps52748.2021.9682055"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.comcom.2020.02.069","article-title":"Applications of Artificial Intelligence and Machine learning in smart cities","volume":"154","author":"Ullah","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.tra.2015.04.003","article-title":"Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations","volume":"77","author":"Fagnant","year":"2015","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"106889","DOI":"10.1016\/j.cie.2020.106889","article-title":"Predictive maintenance in the Industry 4.0: A systematic literature review","volume":"150","author":"Zonta","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"9664","DOI":"10.1109\/JIOT.2020.3023667","article-title":"Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes","volume":"8","author":"Sodhro","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1080\/15325008.2013.832439","article-title":"Smart Home Activities: A Literature Review","volume":"42","author":"Ahmed","year":"2014","journal-title":"Electr. Power Compon. Syst."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Zhang, S., and Zhang, H. (2012, January 15\u201317). A review of wireless sensor networks and its applications. Proceedings of the IEEE International Conference on Automation and Logistics, ICAL, Zhengzhou, China.","DOI":"10.1109\/ICAL.2012.6308240"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"395","DOI":"10.3233\/AIS-170440","article-title":"Internet of things for smart agriculture: Technologies, practices and future direction","volume":"9","author":"Ray","year":"2017","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"21986","DOI":"10.1109\/ACCESS.2021.3056082","article-title":"Identification of Fruit Tree Pests with Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying","volume":"9","author":"Chen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Y.S., Xu, S.P., Hu, W.W., and Wu, Y.J. (2021, January 28\u201330). Design and Implementation of Online Monitoring System for Soil Salinity and Alkalinity in Yangtze River Delta Tideland. Proceedings of the 2021 IEEE International Conference on Artificial Intelligence and Industrial Design, Guangzhou, China.","DOI":"10.1109\/AIID51893.2021.9456521"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Sakthi, U., and Rose, J.D. (2020, January 20\u201322). Smart agricultural knowledge discovery system using IoT technology and fog computing. Proceedings of the 3rd International Conference on Smart Systems and Inventive Technology, ICSSIT, Tirunelveli, India.","DOI":"10.1109\/ICSSIT48917.2020.9214102"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1007\/s11277-015-2480-5","article-title":"Deploying 5G-Technologies in Smart City and Smart Home Wireless Sensor Networks with Interferences","volume":"81","author":"Lynggaard","year":"2015","journal-title":"Wirel. Pers. Commun."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1007\/s11277-018-5383-4","article-title":"Smart Lightning Detection System for Smart-City Infrastructure Using Artificial Neural Network","volume":"106","author":"Ullah","year":"2019","journal-title":"Wirel. Pers. Commun."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.cities.2013.12.010","article-title":"Current trends in Smart City initiatives: Some stylised facts","volume":"38","author":"Neirotti","year":"2014","journal-title":"Cities"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"7596","DOI":"10.1109\/JSEN.2017.2735539","article-title":"Advances on sensing technologies for smart cities and power grids: A review","volume":"17","author":"Morello","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Mainetti, L., Patrono, L., Stefanizzi, M.L., and Vergallo, R. (2015, January 14\u201316). A Smart Parking System based on IoT protocols and emerging enabling technologies. Proceedings of the IEEE World Forum on Internet of Things, WF-IoT, Milan, Italy.","DOI":"10.1109\/WF-IoT.2015.7389150"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"e3","DOI":"10.4108\/eai.31-10-2018.162221","article-title":"IoT enabled Smart Fog Computing for Vehicular Traffic Control","volume":"5","author":"Bhardwaj","year":"2019","journal-title":"EAI Endorsed Trans. Internet Things"},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Sapienza, M., Guardo, E., Cavallo, M., La Torre, G., Leombruno, G., and Tomarchio, O. (2016, January 18\u201320). Solving Critical Events through Mobile Edge Computing: An Approach for Smart Cities. Proceedings of the 2016 IEEE International Conference on Smart Computing SMARTCOMP, St. Louis, MO, USA.","DOI":"10.1109\/SMARTCOMP.2016.7501719"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Sigwele, T., Hu, Y.F., Ali, M., Hou, J., Susanto, M., and Fitriawan, H. (2018, January 9\u201313). Intelligent and Energy Efficient Mobile Smartphone Gateway for Healthcare Smart Devices Based on 5G. Proceedings of the 2018 IEEE Global Communications Conference, GLOBECOM, Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOM.2018.8648031"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., and Nikolopoulos, D.S. (2016, January 18\u201320). Challenges and Opportunities in Edge Computing. Proceedings of the 2016 IEEE International Conference on Smart Cloud SmartCloud, New York, NY, USA.","DOI":"10.1109\/SmartCloud.2016.18"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"100117","DOI":"10.1016\/j.sintl.2021.100117","article-title":"Telemedicine for healthcare: Capabilities, features, barriers, and applications","volume":"2","author":"Haleem","year":"2021","journal-title":"Sens. Int."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1177\/1932296817717007","article-title":"Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things","volume":"11","author":"Klonoff","year":"2017","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"e3710","DOI":"10.1002\/ett.3710","article-title":"Edge computing in smart health care systems: Review, challenges, and research directions","volume":"33","author":"Hartmann","year":"2022","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Kaur, A., and Jasuja, A. (2017, January 5\u20136). Health monitoring based on IoT using Raspberry PI. Proceedings of the IEEE International Conference on Computing, Communication and Automation, ICCCA, Greater Noida, India.","DOI":"10.1109\/CCAA.2017.8230004"},{"key":"ref_179","unstructured":"Zheng, H., Paiva, A.R., and Gurciullo, C.S. (2020). Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT. arXiv."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"49355","DOI":"10.1109\/ACCESS.2021.3069137","article-title":"Edge intelligence for data handling and predictive maintenance in IIoT","volume":"9","author":"Hafeez","year":"2021","journal-title":"IEEE Access"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"4665","DOI":"10.1109\/TII.2018.2842821","article-title":"Deep Learning for Smart Industry: Efficient Manufacture Inspection System with Fog Computing","volume":"14","author":"Li","year":"2018","journal-title":"IEEE Trans. Ind. Informat."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"10535","DOI":"10.1109\/JIOT.2020.3001218","article-title":"Offloading Autonomous Driving Services via Edge Computing","volume":"7","author":"Cui","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_183","doi-asserted-by":"crossref","unstructured":"Agarwal, P., Mittal, M., Ahmed, J., and Idrees, S.M. (2022). Smart Technologies for Energy and Environmental Sustainability, Springer.","DOI":"10.1007\/978-3-030-80702-3"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/MIC.2021.3059189","article-title":"Intelligent and Scalable Air Quality Monitoring with 5G Edge","volume":"25","author":"Su","year":"2021","journal-title":"IEEE Internet Comput."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1007\/s40747-021-00476-w","article-title":"An IoT enabled system for enhanced air quality monitoring and prediction on the edge","volume":"7","author":"Moursi","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"46018","DOI":"10.1007\/s11356-022-19014-3","article-title":"Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring","volume":"29","author":"Bhardwaj","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"4371","DOI":"10.1109\/JIOT.2019.2952593","article-title":"A Survey of Internet of Things (IoT) for Geohazard Prevention: Applications, Technologies, and Challenges","volume":"7","author":"Mei","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_188","first-page":"246","article-title":"Forest fire prediction using IoT and deep learning","volume":"9","author":"Ananthi","year":"2022","journal-title":"Int. J. Adv. Technol. Eng. Explor."},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Huang, X.R., Chen, W.H., Pai, W.Y., Huang, G.Z., Hu, W.C., and Chen, L.B. (2022, January 7\u20139). An AI Edge Computing-Based Robotic Automatic Guided Vehicle System for Cleaning Garbage. Proceedings of the 2022 IEEE 4th Global Conference on Life Sciences and Technologies, Osaka, Japan.","DOI":"10.1109\/LifeTech53646.2022.9754931"},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"8835","DOI":"10.1109\/ACCESS.2017.2704444","article-title":"Virtualization on internet of things edge devices with container technologies: A performance evaluation","volume":"5","author":"Morabito","year":"2017","journal-title":"IEEE Access"},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., Gay, D., Hill, J., Welsh, M., and Brewer, E. (2005). TinyOS: An operating system for sensor networks. Ambient Intelligence, Springer.","DOI":"10.1007\/3-540-27139-2_7"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MCOM.2016.7432173","article-title":"An edge operating system enabling anything-as-a-service","volume":"54","author":"Manzalini","year":"2016","journal-title":"IEEE Commun. Mag."},{"key":"ref_193","doi-asserted-by":"crossref","unstructured":"Li, B., Fan, H., Gao, Y., and Dong, W. (July, January 24). ThingSpire OS: A WebAssembly-based IoT operating system for cloud-edge integration. Proceedings of the MobiSys 2021 19th Annual International Conference on Mobile Systems, Applications, and Services, Virtual.","DOI":"10.1145\/3458864.3466910"},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"10375","DOI":"10.1109\/JIOT.2019.2939008","article-title":"Operating Systems for Internet of Things Low-End Devices: Analysis and Benchmarking","volume":"6","author":"Silva","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"4428","DOI":"10.1109\/JIOT.2018.2815038","article-title":"RIOT: An Open Source Operating System for Low-End Embedded Devices in the IoT","volume":"5","author":"Baccelli","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_196","unstructured":"Borgohain, T., Kumar, U., and Sanyal, S. (2015). Survey of Operating Systems for the IoT Environment. arXiv."},{"key":"ref_197","doi-asserted-by":"crossref","unstructured":"Nakano, W., Shinohara, Y., and Ishiura, N. (2021, January 7\u201310). Full Hardware Implementation of FreeRTOS-Based Real-Time Systems. Proceedings of the IEEE Region 10 Annual International Conference, Auckland, New Zealand.","DOI":"10.1109\/TENCON54134.2021.9707328"},{"key":"ref_198","doi-asserted-by":"crossref","unstructured":"Borges, M., Paiva, S., Santos, A., Gaspar, B., and Cabral, J. (2020, January 9\u201311). Azure RTOS ThreadX Design for Low-End NB-IoT Device. Proceedings of the 2020 IEEE 2nd International Conference on Societal Automation (SA) 2021, Madeira, Portugal.","DOI":"10.1109\/SA51175.2021.9507191"},{"key":"ref_199","first-page":"54","article-title":"Machine Learning at the Network Edge: A Survey","volume":"170","author":"Murphy","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_200","doi-asserted-by":"crossref","unstructured":"Wang, S., Tuor, T., Salonidis, T., Leung, K.K., Makaya, C., He, T., and Chan, K. (2018, January 15\u201319). When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning. Proceedings of the IEEE INFOCOM, Honolulu, HI, USA.","DOI":"10.1109\/INFOCOM.2018.8486403"},{"key":"ref_201","first-page":"1331","article-title":"ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices","volume":"70","author":"Gupta","year":"2017","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_202","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., and Seth, K. (November, January 30). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the ACM Conference on Computer and Communications Security, Dallas, TX, USA."},{"key":"ref_203","unstructured":"Mankins, J.C. (1995). Technology Readiness Levels, NASA."},{"key":"ref_204","first-page":"e01748","article-title":"A review & analysis of current IoT maturity & readiness models and novel proposal","volume":"21","author":"Benotmane","year":"2023","journal-title":"Sci. Afr."},{"key":"ref_205","doi-asserted-by":"crossref","unstructured":"Kosta, S., Aucinas, A., Hui, P., Mortier, R., and Zhang, X. (2012, January 25\u201330). ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. Proceedings of the IEEE INFOCOM, Orlando, FL, USA.","DOI":"10.1109\/INFCOM.2012.6195845"},{"key":"ref_206","doi-asserted-by":"crossref","unstructured":"Chun, B.G., Ihm, S., Maniatis, P., Naik, M., and Patti, A. (2011, January 10). CloneCloud. Proceedings of the Sixth Conference on Computer Systems, New York, NY, USA.","DOI":"10.1145\/1966445.1966473"},{"key":"ref_207","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_208","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.1109\/JIOT.2018.2876279","article-title":"Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning","volume":"6","author":"Chen","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"10119","DOI":"10.1109\/JIOT.2019.2935543","article-title":"Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing","volume":"6","author":"Lei","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Xu, X., Li, H., Xu, W., Liu, Z., Yao, L., and Dai, F. (2022). Artificial Intelligence for Edge Service Optimization in Internet of Vehicles: A Survey, Tsinghua University Press (TUP). Technical Report.","DOI":"10.26599\/TST.2020.9010025"},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"9266","DOI":"10.1109\/JIOT.2020.2995162","article-title":"Semisupervised Distributed Learning with Non-IID Data for AIoT Service Platform","volume":"7","author":"Chiu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1109\/TITS.2023.3239599","article-title":"Edge Computation Offloading With Content Caching in 6G-Enabled IoV","volume":"25","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MWC.2018.1700303","article-title":"Cooperative Content Caching in 5G Networks with Mobile Edge Computing","volume":"25","author":"Zhang","year":"2018","journal-title":"IEEE Wirel. Commun."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/JIOT.2019.2944889","article-title":"JointRec: A Deep-Learning-Based Joint Cloud Video Recommendation Framework for Mobile IoT","volume":"7","author":"Duan","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/OJCS.2020.2996184","article-title":"FedVision: Federated Video Analytics With Edge Computing","volume":"1","author":"Deng","year":"2020","journal-title":"IEEE Open J. Comput. Soc."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"103413","DOI":"10.1016\/j.adhoc.2024.103413","article-title":"Cache-it: A distributed architecture for proactive edge caching in heterogeneous iot scenarios","volume":"156","author":"Zyrianoff","year":"2024","journal-title":"Ad Hoc Netw."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"9368","DOI":"10.1109\/TMC.2024.3361876","article-title":"FedCache: A Knowledge Cache-Driven Federated Learning Architecture for Personalized Edge Intelligence","volume":"23","author":"Wu","year":"2024","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1515\/popets-2018-0026","article-title":"Privacy pass: Bypassing internet challenges anonymously","volume":"3","author":"Davidson","year":"2018","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/1083784.1083789","article-title":"Mining data streams","volume":"34","author":"Gaber","year":"2005","journal-title":"ACM SIGMOD Rec."},{"key":"ref_220","doi-asserted-by":"crossref","unstructured":"Cheng, B., Papageorgiou, A., Cirillo, F., and Kovacs, E. (2015, January 14\u201316). GeeLytics: Geo-distributed edge analytics for large scale IoT systems based on dynamic topology. Proceedings of the IEEE World Forum on Internet of Things, WF-IoT 2015, Milan, Italy.","DOI":"10.1109\/WF-IoT.2015.7389116"},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MC.2017.3641638","article-title":"Real-Time Video Analytics: The Killer App for Edge Computing","volume":"50","author":"Ananthanarayanan","year":"2017","journal-title":"Computer"},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jnca.2017.12.001","article-title":"Distributed data stream processing and edge computing: A survey on resource elasticity and future directions","volume":"103","author":"Buyya","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_223","unstructured":"Garg, N. (2013). Apache Kafka, Packt Publishing."},{"key":"ref_224","doi-asserted-by":"crossref","unstructured":"Song, F., Tomov, S., and Dongarra, J. (2013, January 15). Enabling and scaling matrix computations on heterogeneous multi-core and multi-GPU systems. Proceedings of the International Conference on Supercomputing, Salt Lake City, UT, USA.","DOI":"10.1145\/2304576.2304625"},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"103756","DOI":"10.1016\/j.engappai.2020.103756","article-title":"Trust management and evaluation for edge intelligence in the Internet of Things","volume":"94","author":"Qureshi","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Al-Rakhami, M., Alsahli, M., Hassan, M.M., Alamri, A., Guerrieri, A., and Fortino, G. (2018, January 12\u201315). Cost Efficient Edge Intelligence Framework Using Docker Containers. Proceedings of the 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC\/PiCom\/DataCom\/CyberSciTech), Athens, Greece.","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00138"},{"key":"ref_227","first-page":"48","article-title":"Edge Intelligence: Concepts, Architectures, Applications, and Future Directions","volume":"21","author":"Mendez","year":"2022","journal-title":"ACM Trans. Embed. Comput. Syst. (TECS)"},{"key":"ref_228","doi-asserted-by":"crossref","unstructured":"Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012, January 17). Fog computing and its role in the internet of things. Proceedings of the MCC\u201912 1st ACM Mobile Cloud Computing Workshop, Helsinki, Finland.","DOI":"10.1145\/2342509.2342513"},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1109\/TNSM.2023.3240415","article-title":"Cooperative Task Offloading for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning","volume":"20","author":"Yang","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_230","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2017, January 21\u201326). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_231","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2017). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. arXiv.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_232","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_233","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"101587","DOI":"10.1016\/j.iot.2025.101587","article-title":"Self-learning adaptive power management scheme for energy-efficient IoT-MEC systems using soft actor-critic algorithm","volume":"31","author":"Rahmani","year":"2025","journal-title":"Internet Things"},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"6612","DOI":"10.1109\/TCE.2024.3507158","article-title":"EALLR: Energy-aware low-latency routing data driven model in mobile edge computing","volume":"71","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"100992","DOI":"10.1016\/j.suscom.2024.100992","article-title":"An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the Internet of Things","volume":"43","author":"Moghaddasi","year":"2024","journal-title":"Sustain. Comput. Informatics Syst."},{"key":"ref_237","doi-asserted-by":"crossref","unstructured":"Martin, C., Garrido, D., Diaz, M., and Rubio, B. (2019, January 26\u201328). From the edge to the cloud: Enabling reliable IoT applications. Proceedings of the 2019 International Conference on Future Internet of Things and Cloud, FiCloud 2019, Istanbul, Turkey.","DOI":"10.1109\/FiCloud.2019.00011"},{"key":"ref_238","doi-asserted-by":"crossref","unstructured":"Grover, J., and Garimella, R.M. (2018, January 28\u201331). Reliable and Fault-Tolerant IoT-Edge Architecture. Proceedings of the IEEE SENSORS, New Delhi, India.","DOI":"10.1109\/ICSENS.2018.8589624"},{"key":"ref_239","doi-asserted-by":"crossref","first-page":"110376","DOI":"10.1016\/j.knosys.2023.110376","article-title":"Fault tolerance in fog-based Social Internet of Things","volume":"265","author":"Mohammadi","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"ref_240","doi-asserted-by":"crossref","unstructured":"Tan, T., and Cao, G. (2020, January 6\u20139). FastVA: Deep learning video analytics through edge processing and NPU in mobile. Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications, Virtual.","DOI":"10.1109\/INFOCOM41043.2020.9155476"},{"key":"ref_241","doi-asserted-by":"crossref","unstructured":"Indirli, F., Ornstein, A.C., Desoli, G., Buschini, A., Silvano, C., and Zaccaria, V. (2024, January 15\u201317). Layer-wise Exploration of a Neural Processing Unit Compiler\u2019s Optimization Space. Proceedings of the 2024 10th International Conference on Computer Technology Applications, Vienna, Austria.","DOI":"10.1145\/3674558.3674562"},{"key":"ref_242","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/MM.2024.3423692","article-title":"Amd xdna\u2122 npu in ryzen\u2122 ai processors","volume":"44","author":"Rico","year":"2024","journal-title":"IEEE Micro"},{"key":"ref_243","doi-asserted-by":"crossref","unstructured":"Lee, E., Sung, M., Jang, S.J., Park, J., and Lee, S.S. (2021, January 6\u20139). Memory-centric architecture of neural processing unit for edge device. Proceedings of the IEEE 2021 18th International SoC Design Conference (ISOCC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ISOCC53507.2021.9613977"},{"key":"ref_244","doi-asserted-by":"crossref","unstructured":"Shahid, A., and Mushtaq, M. (2020, January 5\u20137). A Survey Comparing Specialized Hardware and Evolution in TPUs for Neural Networks. Proceedings of the 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020, Bahawalpur, Pakistan.","DOI":"10.1109\/INMIC50486.2020.9318136"},{"key":"ref_245","doi-asserted-by":"crossref","unstructured":"Jo, J., Jeong, S., and Kang, P. (2020, January 19\u201322). Benchmarking GPU-accelerated edge devices. Proceedings of the 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020, Busan, Republic of Korea.","DOI":"10.1109\/BigComp48618.2020.00-89"},{"key":"ref_246","doi-asserted-by":"crossref","unstructured":"Seshadri, K., Akin, B., Laudon, J., Narayanaswami, R., and Yazdanbakhsh, A. (2022, January 6\u20138). An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks. Proceedings of the 2022 IEEE International Symposium on Workload Characterization, IISWC 2022, Austin, TX, USA.","DOI":"10.1109\/IISWC55918.2022.00017"},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.iotcps.2023.12.003","article-title":"Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models","volume":"4","author":"Alwahedi","year":"2024","journal-title":"Internet Things Cyber-Phys. Syst."},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"3738","DOI":"10.1007\/s11227-023-05616-2","article-title":"Security in internet of things: A review on approaches based on blockchain, machine learning, cryptography, and quantum computing","volume":"80","author":"Cherbal","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_249","doi-asserted-by":"crossref","unstructured":"Kang, P., and Somtham, A. (2022). An Evaluation of Modern Accelerator-Based Edge Devices for Object Detection Applications. Mathematics, 10.","DOI":"10.3390\/math10224299"},{"key":"ref_250","doi-asserted-by":"crossref","unstructured":"Sufian, A., You, C., and Dong, M. (2021, January 24\u201326). A deep transfer learning-based edge computing method for home health monitoring. Proceedings of the IEEE 2021 55th Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA.","DOI":"10.1109\/CISS50987.2021.9400321"},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"6909","DOI":"10.1109\/JIOT.2020.2976762","article-title":"MobileDA: Toward edge-domain adaptation","volume":"7","author":"Yang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_252","doi-asserted-by":"crossref","unstructured":"Qian, J., Gochhayat, S.P., and Hansen, L.K. (2019, January 21\u201323). Distributed active learning strategies on edge computing. Proceedings of the 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud), Paris, France.","DOI":"10.1109\/CSCloud\/EdgeCom.2019.00029"},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/MCOM.001.1900103","article-title":"Toward an Intelligent Edge: Wireless Communication Meets Machine Learning","volume":"58","author":"Zhu","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_254","unstructured":"Wang, W., Lv, Q., Yu, W., Hong, W., Qi, J., Wang, Y., Ji, J., Yang, Z., Zhao, L., and Song, X. (2023). Cogvlm: Visual expert for pretrained language models. arXiv."},{"key":"ref_255","unstructured":"Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., and Lin, X.V. (2022). Opt: Open pre-trained transformer language models. arXiv."},{"key":"ref_256","unstructured":"Chung, H.W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., and Brahma, S. (2022). Scaling instruction-finetuned language models. arXiv."},{"key":"ref_257","first-page":"34892","article-title":"Visual instruction tuning","volume":"36","author":"Liu","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_258","unstructured":"Li, J., Li, D., Savarese, S., and Hoi, S. (2023). Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv."},{"key":"ref_259","unstructured":"Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., and Vaughan, A. (2024). The llama 3 herd of models. arXiv."},{"key":"ref_260","unstructured":"Lin, B., Tang, Z., Ye, Y., Cui, J., Zhu, B., Jin, P., Zhang, J., Ning, M., and Yuan, L. (2024). Moe-llava: Mixture of experts for large vision-language models. arXiv."},{"key":"ref_261","unstructured":"Li, S., and Tang, H. (2024). Multimodal Alignment and Fusion: A Survey. arXiv."},{"key":"ref_262","first-page":"9694","article-title":"Align before fuse: Vision and language representation learning with momentum distillation","volume":"34","author":"Li","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_263","unstructured":"Qin, J., Xu, Y., Lu, Z., and Zhang, X. (2023). Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method. arXiv."},{"key":"ref_264","unstructured":"Cajas, S.A., Restrepo, D., Moukheiber, D., Kuo, K.T., Wu, C., Chicangana, D.S.G., Paddo, A.R., Moukheiber, M., Moukheiber, L., and Moukheiber, S. (2024). A Multi-Modal Satellite Imagery Dataset for Public Health Analysis in Colombia, PhysioNet."},{"key":"ref_265","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1109\/JPROC.2015.2460697","article-title":"Multimodal data fusion: An overview of methods, challenges, and prospects","volume":"103","author":"Lahat","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_266","doi-asserted-by":"crossref","first-page":"704504","DOI":"10.1155\/2013\/704504","article-title":"A review of data fusion techniques","volume":"2013","author":"Castanedo","year":"2013","journal-title":"Sci. World J."},{"key":"ref_267","doi-asserted-by":"crossref","unstructured":"Snoek, C.G., Worring, M., and Smeulders, A.W. (2005, January 6\u201311). Early versus late fusion in semantic video analysis. Proceedings of the 13th annual ACM international conference on Multimedia, Singapore.","DOI":"10.1145\/1101149.1101236"},{"key":"ref_268","doi-asserted-by":"crossref","first-page":"84283","DOI":"10.1109\/ACCESS.2023.3296098","article-title":"A comparative analysis of early and late fusion for the multimodal two-class problem","volume":"11","author":"Pereira","year":"2023","journal-title":"IEEE Access"},{"key":"ref_269","first-page":"24206","article-title":"Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text","volume":"34","author":"Akbari","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_270","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1038\/s41551-023-01045-x","article-title":"A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics","volume":"7","author":"Zhou","year":"2023","journal-title":"Nat. Biomed. Eng."},{"key":"ref_271","doi-asserted-by":"crossref","unstructured":"Khader, F., Kather, J.N., M\u00fcller-Franzes, G., Wang, T., Han, T., Tayebi Arasteh, S., Hamesch, K., Bressem, K., Haarburger, C., and Stegmaier, J. (2023). Medical transformer for multimodal survival prediction in intensive care: Integration of imaging and non-imaging data. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-37835-1"},{"key":"ref_272","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/TMI.2023.3312524","article-title":"Clinically-inspired multi-agent transformers for disease trajectory forecasting from multimodal data","volume":"43","author":"Nguyen","year":"2023","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_273","unstructured":"Garcia, J., Masip-Bruin, X., Giannopoulos, A., Trakadas, P., Cajas Ordo\u00f1ez, S.A., Samanta, J., Su\u00e1rez-Cetrulo, A.L., Sim\u00f3n Carbajo, R., Michalke, M., and Admela, J. (April, January 30). ICOS An Intelligent MetaOS for the Continuum. Proceedings of the MECC \u201925 2nd International Workshop on MetaOS for the Cloud-Edge-IoT Continuum, New York, NY, USA."},{"key":"ref_274","doi-asserted-by":"crossref","first-page":"5506","DOI":"10.1109\/TWC.2018.2845360","article-title":"Latency optimization for resource allocation in mobile-edge computation offloading","volume":"17","author":"Ren","year":"2018","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_275","doi-asserted-by":"crossref","first-page":"7525","DOI":"10.1109\/JIOT.2022.3143722","article-title":"Energy-Efficient Artificial Intelligence of Things With Intelligent Edge","volume":"9","author":"Zhu","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_276","doi-asserted-by":"crossref","first-page":"7225","DOI":"10.1109\/TII.2021.3067026","article-title":"Energy-Efficient Industrial Internet of Things: Overview and Open Issues","volume":"17","author":"Mao","year":"2021","journal-title":"IEEE Trans. Ind. Informat."},{"key":"ref_277","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s42256-019-0088-2","article-title":"The global landscape of AI ethics guidelines","volume":"1","author":"Jobin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_278","unstructured":"Kumar, A., Zhuang, V., Agarwal, R., Su, Y., Co-Reyes, J.D., Singh, A., Baumli, K., Iqbal, S., Bishop, C., and Roelofs, R. (2024). Training language models to self-correct via reinforcement learning. arXiv."},{"key":"ref_279","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s11023-020-09517-8","article-title":"The Ethics of AI Ethics: An Evaluation of Guidelines","volume":"30","author":"Hagendorff","year":"2020","journal-title":"Minds Mach."},{"key":"ref_280","doi-asserted-by":"crossref","unstructured":"Tsouparopoulos, T., and Koutsopoulos, I. (2025). Explainability and Continual Learning meet Federated Learning at the Network Edge. arXiv.","DOI":"10.23919\/WiOpt66569.2025.11123219"},{"key":"ref_281","doi-asserted-by":"crossref","unstructured":"Chen, T. (2019, January 25). All versus one: An empirical comparison on retrained and incremental machine learning for modeling performance of adaptable software. Proceedings of the ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, Montreal, QC, Canada.","DOI":"10.1109\/SEAMS.2019.00029"},{"key":"ref_282","unstructured":"Aspis, M., Ord\u00f3nez, S.A., Su\u00e1rez-Cetrulo, A.L., and Carbajo, R.S. (2025). DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts. arXiv."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/9\/417\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:44:00Z","timestamp":1760035440000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/9\/417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":282,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["fi17090417"],"URL":"https:\/\/doi.org\/10.3390\/fi17090417","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,11]]}}}