{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T03:11:19Z","timestamp":1794539479178,"version":"4.9.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-27818-9","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T14:05:25Z","timestamp":1764943525000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Deploying TinyML for energy-efficient object detection and communication in low-power edge AI systems"],"prefix":"10.1038","volume":"15","author":[{"given":"Ch Madhu","family":"Bhushan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priya","family":"Koppuravuri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nomitha","family":"Prasanthi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firoj","family":"Gazi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Muzakkir","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Abdussami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aguru Aswani","family":"Devi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamilurahman","family":"Faizi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"27818_CR1","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1007\/s10462-024-10877-1","volume":"57","author":"P Mittal","year":"2024","unstructured":"Mittal, P. A comprehensive survey of deep learning-based lightweight object detection models for edge devices. Artif.\u00a0Intell. Rev. 57, 242 (2024).","journal-title":"Artif.\u00a0Intell. Rev."},{"key":"27818_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3555802","volume":"55","author":"H Hua","year":"2023","unstructured":"Hua, H. et al. Edge computing with artificial intelligence: A machine learning perspective. ACM Comput. Surv. 55, 1\u201335. https:\/\/doi.org\/10.1145\/3555802 (2023).","journal-title":"ACM Comput. Surv."},{"key":"27818_CR3","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/JPROC.2019.2921977","volume":"107","author":"J Chen","year":"2019","unstructured":"Chen, J. & Ran, X. Deep learning with edge computing: A review. Proc. IEEE 107, 1655\u20131674 (2019).","journal-title":"Proc. IEEE"},{"key":"27818_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2022.103419","volume":"205","author":"D Soni","year":"2022","unstructured":"Soni, D. & Kumar, N. Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy. J. Netw. Comput. Appl. 205, 103419 (2022).","journal-title":"J. Netw. Comput. Appl."},{"key":"27818_CR5","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/s41528-021-00107-x","volume":"5","author":"SMA Iqbal","year":"2021","unstructured":"Iqbal, S. M. A., Mahgoub, I., Du, E., Leavitt, M. A. & Asghar, W. Advances in healthcare wearable devices. npj Flex. Electron. 5, 9. https:\/\/doi.org\/10.1038\/s41528-021-00107-x (2021).","journal-title":"npj Flex. Electron."},{"key":"27818_CR6","doi-asserted-by":"publisher","first-page":"10167","DOI":"10.1007\/s11042-023-15608-2","volume":"83","author":"Y Zhou","year":"2024","unstructured":"Zhou, Y., Xia, L., Zhao, J., Yao, R. & Liu, B. Efficient convolutional neural networks and network compression methods for object detection: a survey. Multimed. Tools Appl. 83, 10167\u201310209 (2024).","journal-title":"Multimed. Tools Appl."},{"key":"27818_CR7","doi-asserted-by":"publisher","first-page":"65","DOI":"10.52339\/tjet.v42i2.853","volume":"42","author":"HG Msuya","year":"2023","unstructured":"Msuya, H. G. & Maiseli, B. J. Deep learning model compression techniques: Advances, opportunities, and perspective. Tanzania J. Eng. Technol. 42, 65\u201383. https:\/\/doi.org\/10.52339\/tjet.v42i2.853 (2023).","journal-title":"Tanzania J. Eng. Technol."},{"key":"27818_CR8","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. MobileNetV2: Inverted residuals and linear bottlenecks (2018). Arxiv:1801.04381.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"27818_CR9","doi-asserted-by":"publisher","unstructured":"Dong, K., Zhou, C., Ruan, Y. & Li, Y. Mobilenetv2 model for image classification. In 2020 2nd International Conference on Information Technology and Computer Application (ITCA), 476\u2013480, https:\/\/doi.org\/10.1109\/ITCA52113.2020.00106 (2020).","DOI":"10.1109\/ITCA52113.2020.00106"},{"key":"27818_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120131","volume":"660","author":"H Shen","year":"2024","unstructured":"Shen, H., Wang, Z., Zhang, J. & Zhang, M. L-net: A lightweight convolutional neural network for devices with low computing power. Inf. Sci. 660, 120131 (2024).","journal-title":"Inf. Sci."},{"key":"27818_CR11","doi-asserted-by":"crossref","unstructured":"Agarwal, P. & Alam, M. A lightweight deep learning model for human activity recognition on edge devices. Procedia Comput. Sci.\u00a0167, 2364\u20132373, https:\/\/doi.org\/10.1016\/j.procs.2020.03.289. (International Conference on Computational Intelligence and Data Science, 2020)","DOI":"10.1016\/j.procs.2020.03.289"},{"key":"27818_CR12","unstructured":"Gholami, A. et al. A survey of quantization methods for efficient neural network inference (2021). Arxiv:2103.13630."},{"key":"27818_CR13","unstructured":"Banner, R., Hubara, I., Hoffer, E. & Soudry, D. Scalable methods for 8-bit training of neural networks (2018). Arxiv:1805.11046."},{"key":"27818_CR14","unstructured":"Chmiel, B. et al. Neural gradients are near-lognormal: Improved quantized and sparse training (2020). Arxiv:2006.08173."},{"key":"27818_CR15","doi-asserted-by":"publisher","first-page":"41","DOI":"10.31572\/inotera.Vol10.Iss1.2025.ID433","volume":"10","author":"W Haryono","year":"2025","unstructured":"Haryono, W. Comparative analysis of tcp and udp protocol performance in sending text messages using chatting applications. J. Inotera 10, 41\u201348 (2025).","journal-title":"J. Inotera"},{"key":"27818_CR16","doi-asserted-by":"publisher","unstructured":"Bansal, S., Shorey, R. & Kherani, A. Performance of tcp and udp protocols in multi-hop multi-rate wireless networks. In 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733), 1, 231\u2013236. https:\/\/doi.org\/10.1109\/WCNC.2004.1311548 (2004).","DOI":"10.1109\/WCNC.2004.1311548"},{"key":"27818_CR17","doi-asserted-by":"crossref","unstructured":"Odema, M., Chen, L., Levorato, M. & Al Faruque, M.\u00a0A. Testudo: Collaborative intelligence for latency-critical autonomous systems. IEEE Transactions on Comput. Des. Integr. Circuits Syst.(2022).","DOI":"10.1109\/TCAD.2022.3211480"},{"key":"27818_CR18","unstructured":"Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network (2015). Arxiv:1503.02531."},{"key":"27818_CR19","unstructured":"Polino, A., Pascanu, R. & Alistarh, D. Model compression via distillation and quantization (2018). Arxiv:1802.05668."},{"key":"27818_CR20","unstructured":"Phuong, M. & Lampert, C. Towards understanding knowledge distillation. In Chaudhuri, K. & Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, vol.\u00a097 of Proceedings of Machine Learning Research, 5142\u20135151 (PMLR, 2019)."},{"key":"27818_CR21","volume-title":"MobileNetV3, 125\u2013144","author":"B Koonce","year":"2021","unstructured":"Koonce, B. MobileNetV3, 125\u2013144 (Apress, 2021)."},{"key":"27818_CR22","doi-asserted-by":"crossref","unstructured":"Howard, A. et al. Searching for mobilenetv3. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019).","DOI":"10.1109\/ICCV.2019.00140"},{"key":"27818_CR23","doi-asserted-by":"publisher","unstructured":"Krivokapic, B., Tomovic, S., Radusinovic, I. & Jovanovic, A. Implementation and performance evaluation of convolutional neural network models for low-power microcontrollers with constrained resources. In 2024 28th International Conference on Information Technology (IT), 1\u20134, https:\/\/doi.org\/10.1109\/IT61232.2024.10475765 (2024).","DOI":"10.1109\/IT61232.2024.10475765"},{"key":"27818_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2023.103679","volume":"217","author":"RG Pacheco","year":"2023","unstructured":"Pacheco, R. G., Couto, R. S. & Simeone, O. On the impact of deep neural network calibration on adaptive edge offloading for image classification. J. Netw. Comput. Appl. 217, 103679 (2023).","journal-title":"J. Netw. Comput. Appl."},{"key":"27818_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2021.103660","volume":"47","author":"C Vidal","year":"2022","unstructured":"Vidal, C., Malysz, P., Naguib, M., Emadi, A. & Kollmeyer, P. J. Estimating battery state of charge using recurrent and non-recurrent neural networks. J. Energy Storage 47, 103660. https:\/\/doi.org\/10.1016\/j.est.2021.103660 (2022).","journal-title":"J. Energy Storage"},{"key":"27818_CR26","first-page":"517","volume":"3","author":"C Banbury","year":"2021","unstructured":"Banbury, C. et al. Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers. Proc. Mach. Learn. Syst. 3, 517\u2013532 (2021).","journal-title":"Proc. Mach. Learn. Syst."},{"key":"27818_CR27","unstructured":"Chowdhery, A., Warden, P., Shlens, J., Howard, A. & Rhodes, R. Visual wake words dataset. arXiv preprint arXiv:1906.05721 (2019)."},{"key":"27818_CR28","unstructured":"Howard, A.\u00a0G. et al. MobileNets: Efficient convolutional neural networks for mobile vision applications (2017). Arxiv:1704.04861."},{"key":"27818_CR29","doi-asserted-by":"publisher","unstructured":"Zhou, Y., Chen, S., Wang, Y. & Huan, W. Review of research on lightweight convolutional neural networks. In 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), 1713\u20131720, https:\/\/doi.org\/10.1109\/ITOEC49072.2020.9141847 (2020).","DOI":"10.1109\/ITOEC49072.2020.9141847"},{"key":"27818_CR30","doi-asserted-by":"crossref","unstructured":"Deng, J. et al. Imagenet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 248\u2013255 (IEEE, 2009).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"27818_CR31","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G.\u00a0E. Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C., Bottou, L. & Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol.\u00a025 (Curran Associates, Inc., 2012)."},{"key":"27818_CR32","doi-asserted-by":"crossref","unstructured":"Moosmann, J., Giordano, M., Vogt, C. & Magno, M. Tinyissimoyolo: A quantized, low-memory footprint, tinyml object detection network for low power microcontrollers. In 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 1\u20135 (IEEE, 2023).","DOI":"10.1109\/AICAS57966.2023.10168657"},{"key":"27818_CR33","doi-asserted-by":"crossref","unstructured":"Howard, A. et al. Searching for MobileNetV3 (2019). Arxiv:1905.02244.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"27818_CR34","volume":"1651","author":"J Dai","year":"2020","unstructured":"Dai, J. Real-time and accurate object detection on edge device with tensorflow lite. J. Physics: Conf. Ser. 1651, 012114 (2020).","journal-title":"J. Physics: Conf. Ser."},{"key":"27818_CR35","doi-asserted-by":"publisher","first-page":"357","DOI":"10.11591\/eei.v14i1.8131","volume":"14","author":"H Lokhande","year":"2025","unstructured":"Lokhande, H. & Ganorkar, S. R. Object detection in video surveillance using MobileNetV2 on resource-constrained low-power edge devices. Bull. Electr. Eng. Informatics 14, 357\u2013365 (2025).","journal-title":"Bull. Electr. Eng. Informatics"},{"key":"27818_CR36","first-page":"244","volume":"18","author":"T Jaiswal","year":"2025","unstructured":"Jaiswal, T., Pandey, M. & Tripathi, P. Real-time multiple object detection using raspberry pi and tiny-ml approach. Recent Adv. Electr. & Electron. Eng. 18, 244\u2013255 (2025).","journal-title":"Recent Adv. Electr. & Electron. Eng."},{"key":"27818_CR37","doi-asserted-by":"crossref","unstructured":"Stojanovi\u0107, D., Senti\u0107, S., Stojanovi\u0107, N. & Stamenkovi\u0107, T. Resource-aware object detection and recognition using edge ai across the edge\u2013fog computing continuum. Comput. Sci. Inf. Syst.\u00a020\u201320 (2025).","DOI":"10.2298\/CSIS240503020S"},{"key":"27818_CR38","doi-asserted-by":"crossref","unstructured":"El Zeinaty, C., Hamidouche, W. & Herrou, G. & Menard, D. Foundations, comparative analysis, challenges, and emerging solutions.\u00a0ACM Comput. Surv.\u00a0(2025).","DOI":"10.1145\/3744339"},{"key":"27818_CR39","doi-asserted-by":"publisher","first-page":"2984","DOI":"10.3390\/s21092984","volume":"21","author":"P-E Novac","year":"2021","unstructured":"Novac, P.-E., Boukli Hacene, G., Pegatoquet, A., Miramond, B. & Gripon, V. Quantization and deployment of deep neural networks on microcontrollers. Sensors 21, 2984 (2021).","journal-title":"Sensors"},{"key":"27818_CR40","first-page":"1","volume":"6","author":"E Liberis","year":"2023","unstructured":"Liberis, E. & Lane, N. D. Differentiable neural network pruning to enable smart applications on microcontrollers. Proc. ACM on Interactive, Mobile, Wearable and Ubiquitous Technol. 6, 1\u201319 (2023).","journal-title":"Proc. ACM on Interactive, Mobile, Wearable and Ubiquitous Technol."},{"key":"27818_CR41","unstructured":"Warden, P. & Situnayake, D. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers (O\u2019Reilly Media, 2019)."},{"key":"27818_CR42","unstructured":"O\u2019Shea, K. & Nash, R. An introduction to convolutional neural networks. CoRR abs\/1511.08458 (2015). Arxiv:1511.08458."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-27818-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-27818-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-27818-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T10:51:09Z","timestamp":1766400669000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-27818-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,5]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["27818"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-27818-9","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-7162879\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,5]]},"assertion":[{"value":"19 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"44299"}}