{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T09:48:14Z","timestamp":1775209694064,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s10586-024-04285-x","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T07:02:20Z","timestamp":1708498940000},"page":"6097-6117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Stochastic analysis of fog computing and machine learning for scalable low-latency healthcare monitoring"],"prefix":"10.1007","volume":"27","author":[{"given":"Abdellah","family":"Amzil","sequence":"first","affiliation":[]},{"given":"Mohamed","family":"Abid","sequence":"additional","affiliation":[]},{"given":"Mohamed","family":"Hanini","sequence":"additional","affiliation":[]},{"given":"Abdellah","family":"Zaaloul","sequence":"additional","affiliation":[]},{"given":"Said","family":"El Kafhali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"issue":"8","key":"4285_CR1","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1021\/acsphotonics.2c00898","volume":"9","author":"J Zhao","year":"2022","unstructured":"Zhao, J., et al.: Wearable optical sensing in the medical internet of things (MIoT) for pervasive medicine: opportunities and challenges. Acs Photonics 9(8), 2579-2599.3 (2022)","journal-title":"Acs Photonics"},{"key":"4285_CR2","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1007\/s10586-023-04098-4","volume":"26","author":"K Saidi","year":"2023","unstructured":"Saidi, K., Bardou, D.: Task scheduling and VM placement to resource allocation in cloud computing: challenges and opportunities. Clust. Comput. 26, 3069\u20133087 (2023). https:\/\/doi.org\/10.1007\/s10586-023-04098-4","journal-title":"Clust. Comput."},{"issue":"1","key":"4285_CR3","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s13174-010-0007-6.3","volume":"1","author":"Qi Zhang","year":"2010","unstructured":"Zhang, Qi., et al.: Cloud computing: state-of-the-art and research challenges. J. Internet Services Appl. 1(1), 7\u201318 (2010). https:\/\/doi.org\/10.1007\/s13174-010-0007-6.3","journal-title":"J. Internet Services Appl."},{"key":"4285_CR4","doi-asserted-by":"publisher","first-page":"2897","DOI":"10.1007\/s10586-022-03768-z","volume":"26","author":"Y Chen","year":"2023","unstructured":"Chen, Y., Chen, S., Li, K.C., et al.: DRJOA: intelligent resource management optimization through deep reinforcement learning approach in edge computing. Clust. Comput. 26, 2897\u20132911 (2023). https:\/\/doi.org\/10.1007\/s10586-022-03768-z","journal-title":"Clust. Comput."},{"key":"4285_CR5","doi-asserted-by":"crossref","unstructured":"El Kafhali, S., Chahir, C., Hanini, M., & Salah, K. (2019, October). Architecture to manage Internet of Things Data using Blockchain and Fog Computing. In Proceedings of the 4th International Conference on Big Data and Internet of Things (pp. 1-8)","DOI":"10.1145\/3372938.3372970"},{"key":"4285_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2021.113777","volume":"197","author":"H Zhang","year":"2022","unstructured":"Zhang, H., et al.: Graphene-enabled wearable sensors for healthcare monitoring. Biosensors Bioelectronics 197, 113777 (2022)","journal-title":"Biosensors Bioelectronics"},{"issue":"2","key":"4285_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102888","volume":"59","author":"AG Sreedevi","year":"2022","unstructured":"Sreedevi, A.G., et al.: Application of cognitive computing in healthcare, cybersecurity, big data, and IoT: a literature review. Inf. Process. Manage. 59(2), 102888 (2022)","journal-title":"Inf. Process. Manage."},{"issue":"1","key":"4285_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhepr.2021.100390","volume":"4","author":"N Pierre","year":"2022","unstructured":"Pierre, N., et al.: Early hepatocellular carcinoma detection using magnetic resonance imaging is cost-effective in high-risk patients with cirrhosis. JHEP Rep. 4(1), 100390 (2022)","journal-title":"JHEP Rep."},{"issue":"12","key":"4285_CR9","doi-asserted-by":"publisher","first-page":"10693","DOI":"10.1007\/s13369-020-04847-2","volume":"45","author":"S El Kafhali","year":"2020","unstructured":"El Kafhali, S., et al.: Dynamic scalability model for containerized cloud services. Arabian J. Sci. Eng. Springer 45(12), 10693\u201310708 (2020)","journal-title":"Arabian J. Sci. Eng. Springer"},{"key":"4285_CR10","doi-asserted-by":"crossref","unstructured":"El Kafhali, S., Salah, K., & Alla, S. B. (2018). Performance evaluation of IoT-fog-cloud deployment for healthcare services. In 2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech) (pp. 1-6). IEEE","DOI":"10.1109\/CloudTech.2018.8713355"},{"issue":"3","key":"4285_CR11","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.artmed.2012.09.003","volume":"56","author":"MD Chan","year":"2012","unstructured":"Chan, M. D., et al.: Smart wearable systems: current status and future challenges. Artif. Intell. Med. 56(3), 137\u2013156 (2012)","journal-title":"Artif. Intell. Med."},{"issue":"3","key":"4285_CR12","doi-asserted-by":"publisher","DOI":"10.1039\/d1bm01136g","volume":"10","author":"H Liu","year":"2022","unstructured":"Liu, H., et al.: Recent progress in the fabrication of flexible materials for wearable sensors. Biomater. Sci. 10(3), 614632 (2022). https:\/\/doi.org\/10.1039\/d1bm01136g","journal-title":"Biomater. Sci."},{"issue":"12","key":"4285_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109929","volume":"257","author":"T Shaik","year":"2022","unstructured":"Shaik, T., et al.: Personalized activity monitoring using stacked federated learning. Knowledge-Based Syst. 257(12), 109929 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109929","journal-title":"Knowledge-Based Syst."},{"key":"4285_CR14","volume-title":"Minimization of Task Offloading Latency for COVID-19 IoT Devices, The International Conference on Intelligent Systems and Smart Technologies","author":"A Abdellah","year":"2023","unstructured":"Abdellah, A., et al.: Minimization of Task Offloading Latency for COVID-19 IoT Devices, The International Conference on Intelligent Systems and Smart Technologies. Springer, Settat (2023)"},{"issue":"20","key":"4285_CR15","doi-asserted-by":"publisher","first-page":"4364","DOI":"10.3390\/app9204364","volume":"9","author":"F Bousefsaf","year":"2019","unstructured":"Bousefsaf, F., et al.: 3d convolutional neural networks for remote pulse rate measurement and mapping from facial video. Appl. Sci. 9(20), 4364 (2019). https:\/\/doi.org\/10.3390\/app9204364","journal-title":"Appl. Sci."},{"key":"4285_CR16","doi-asserted-by":"crossref","unstructured":"Cho, Y., et al. (2017). DeepBreath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings. In the Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE","DOI":"10.1109\/ACII.2017.8273639"},{"key":"4285_CR17","doi-asserted-by":"crossref","unstructured":"Khalid, W. B., et al. (2022). Contactless vitals measurement robot. In 8th International Conference on Automation, Robotics and Applications (ICARA). IEEE","DOI":"10.1109\/ICARA55094.2022.9738523"},{"key":"4285_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104378","volume":"148","author":"J Laurie","year":"2021","unstructured":"Laurie, J., et al.: An evaluation of a video magnification-based system for respiratory rate monitoring in an acute mental health setting. Int. J. Med. Inf. 148, 104378 (2021)","journal-title":"Int. J. Med. Inf."},{"issue":"4","key":"4285_CR19","doi-asserted-by":"publisher","first-page":"607","DOI":"10.3390\/diagnostics11040607","volume":"11","author":"N El-Rashidy","year":"2021","unstructured":"El-Rashidy, N., et al.: Mobile health in remote patient monitoring for chronic diseases: principles, trends, and challenges. Diagnostics 11(4), 607 (2021). https:\/\/doi.org\/10.3390\/diagnostics11040607","journal-title":"Diagnostics"},{"key":"4285_CR20","doi-asserted-by":"crossref","unstructured":"Sharma, P., et al. (2018). Sleep scoring with a UHF RFID tag by near-field coherent sensing. In 2018 IEEE\/MTT-s International Microwave Symposium IMS. IEEE. https:\/\/doi.org\/10.1109\/MWSYM.2018.8439216","DOI":"10.1109\/MWSYM.2018.8439216"},{"key":"4285_CR21","doi-asserted-by":"crossref","unstructured":"Hui, X.,et al. (2018). Accurate extraction of heartbeat intervals with near-field coherent sensing. In 2018 IEEE International Conference on Communications (ICC). IEEE. https:\/\/doi.org\/10.1109\/icc.2018.8423000","DOI":"10.1109\/ICC.2018.8423000"},{"key":"4285_CR22","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.jpdc.2018.08.010","volume":"123","author":"MZ Uddin","year":"2019","unstructured":"Uddin, M.Z.: A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system. J. Parallel Distrib. Comput. 123, 46\u201353 (2019)","journal-title":"J. Parallel Distrib. Comput."},{"issue":"1","key":"4285_CR23","first-page":"173","volume":"22","author":"S Vimal","year":"2021","unstructured":"Vimal, S., et al.: Iot based smart health monitoring with cnn using edge computing. J. Internet Technol. 22(1), 173\u2013185 (2021)","journal-title":"J. Internet Technol."},{"key":"4285_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/8016525","volume":"2021","author":"AI Siam","year":"2021","unstructured":"Siam, A.I., et al.: Secure health monitoring communication systems based on IoT and cloud computing for medical emergency applications. Comput. Intell. Neurosci. 2021, 1\u201323 (2021). https:\/\/doi.org\/10.1155\/2021\/8016525","journal-title":"Comput. Intell. Neurosci."},{"key":"4285_CR25","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1007\/s10586-023-04100-z","volume":"26","author":"S Taheri-abed","year":"2023","unstructured":"Taheri-abed, S., Eftekhari Moghadam, A.M., Rezvani, M.H.: Machine learning-based computation offloading in edge and fog: a systematic review. Clust. Comput. 26, 3113\u20133144 (2023). https:\/\/doi.org\/10.1007\/s10586-023-04100-z","journal-title":"Clust. Comput."},{"issue":"12","key":"4285_CR26","doi-asserted-by":"publisher","DOI":"10.2196\/14473","volume":"7","author":"S Park","year":"2019","unstructured":"Park, S., et al.: Clustering insomnia patterns by data from wearable devices: algorithm development and validation study. JMIR mHealth uHealth 7(12), e14473 (2019)","journal-title":"JMIR mHealth uHealth"},{"issue":"5","key":"4285_CR27","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.3390\/s22051887","volume":"22","author":"F Sabry","year":"2022","unstructured":"Sabry, F., et al.: Towards on-device dehydration monitoring using machine learning from wearable device\u2019s data. Sensors 22(5), 1887 (2022)","journal-title":"Sensors"},{"issue":"9","key":"4285_CR28","first-page":"3502","volume":"68","author":"AS Weddell","year":"2021","unstructured":"Weddell, A.S., et al.: Toward edge machine learning with RRAM-based hybrid digital\/analog computing. IEEE Trans. Circuits Syst. II: Exp. Briefs 68(9), 3502\u20133506 (2021)","journal-title":"IEEE Trans. Circuits Syst. II: Exp. Briefs"},{"issue":"10","key":"4285_CR29","first-page":"5589","volume":"20","author":"F Xiao","year":"2020","unstructured":"Xiao, F., et al.: A data reduction scheme for physiological signal processing on wearable devices. IEEE Sens. J. 20(10), 5589\u20135598 (2020)","journal-title":"IEEE Sens. J."},{"issue":"2","key":"4285_CR30","first-page":"359","volume":"19","author":"F Chen","year":"2020","unstructured":"Chen, F., et al.: Dynamic scheduling of wearable sensor data with minimum delay and energy consumption. IEEE Trans. Mobile Comput. 19(2), 359\u2013371 (2020)","journal-title":"IEEE Trans. Mobile Comput."},{"issue":"4","key":"4285_CR31","first-page":"2504","volume":"8","author":"M Cai","year":"2021","unstructured":"Cai, M., et al.: Joint computation offloading and resource allocation for wearable devices in cloud computing. IEEE Internet Things J. 8(4), 2504\u20132514 (2021)","journal-title":"IEEE Internet Things J."},{"key":"4285_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115250","volume":"271","author":"J Yuan","year":"2020","unstructured":"Yuan, J., Zhu, R.: A fully self-powered wearable monitoring system with systematically optimized flexible thermoelectric generator. Appl. Energy 271, 115250 (2020). https:\/\/doi.org\/10.1016\/j.apenergy.2020.115250","journal-title":"Appl. Energy"},{"key":"4285_CR33","doi-asserted-by":"crossref","unstructured":"X. Fafoutis, et al. (2018) Extending the Battery Lifetime of Wearable Sensors with Embedded Machine Learning, In: Proceedings of the IEEE 4th World Forum on Internet of Gings (WF-IoT), pp. 269\u2013274, Singapore","DOI":"10.1109\/WF-IoT.2018.8355116"},{"issue":"4","key":"4285_CR34","volume":"26","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., et al.: Motion artifact reduction in wearable photoplethysmography using gyroscope signals and spectral filtering. J. Biomed. Opt. 26(4), 047003 (2021)","journal-title":"J. Biomed. Opt."},{"issue":"18","key":"4285_CR35","first-page":"2020","volume":"3","author":"B Bent","year":"2020","unstructured":"Bent, B., et al.: Investigating sources of inaccuracy in wearable optical heart rate sensors. Npj Digital Med. 3(18), 2020 (2020)","journal-title":"Npj Digital Med."},{"key":"4285_CR36","doi-asserted-by":"crossref","first-page":"18256","DOI":"10.1109\/ACCESS.2019.2896640","volume":"7","author":"J Singh","year":"2019","unstructured":"Singh, J., et al.: Security and privacy in edge computing: a review. IEEE Access 7, 18256\u201318277 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"4285_CR37","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1049\/iet-net.2018.5067","volume":"8","author":"S El Kafhali","year":"2019","unstructured":"El Kafhali, S., Salah, K.: Performance modeling and analysis of IoT-enabled healthcare monitoring systems. IET Networks 8(1), 48\u201358 (2019)","journal-title":"IET Networks"},{"issue":"7","key":"4285_CR38","first-page":"7","volume":"6","author":"A Kishor","year":"2021","unstructured":"Kishor, A., et al.: A novel fog computing approach for minimization of latency in healthcare using machine learning. Int. J. Interact. Multimed 6(7), 7 (2021)","journal-title":"Int. J. Interact. Multimed"},{"issue":"1","key":"4285_CR39","first-page":"400","volume":"23","author":"BS Kumar","year":"2019","unstructured":"Kumar, B.S., et al.: A novel architecture based on deep learning for scene image recognition. Int. J. Psychosoc. Rehabil. 23(1), 400\u201304 (2019)","journal-title":"Int. J. Psychosoc. Rehabil."},{"key":"4285_CR40","volume":"2","author":"O Badawi","year":"2014","unstructured":"Badawi, O., et al.: Making big data useful for health care: a summary of the inaugural MIT critical data conference. JMIR Med. Inf. 2, e3447 (2014)","journal-title":"JMIR Med. Inf."},{"key":"4285_CR41","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s40560-021-00533-z","volume":"9","author":"H Endo","year":"2021","unstructured":"Endo, H., Uchino, S., Hashimoto, S., et al.: Development and validation of the predictive risk of death model for adult patients admitted to intensive care units in Japan: an approach to improve the accuracy of healthcare quality measures. J. Intensive Care 9, 18 (2021)","journal-title":"J. Intensive Care"},{"issue":"2","key":"4285_CR42","first-page":"1","volume":"9","author":"A Abdulhafedh","year":"2022","unstructured":"Abdulhafedh, A.: Comparison between common statistical modeling techniques used in research, including discriminant analysis vs logistic regression, ridge regression vs LASSO, and decision tree vs random forest. Open Access Library J. 9(2), 1\u201319 (2022)","journal-title":"Open Access Library J."},{"issue":"1","key":"4285_CR43","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.dcan.2018.10.008","volume":"5","author":"QD La","year":"2019","unstructured":"La, Q.D., et al.: Enabling intelligence in fog computing to achieve energy and latency reduction. Digit. Commun. Networks 5(1), 3\u20139 (2019)","journal-title":"Digit. Commun. Networks"},{"issue":"1","key":"4285_CR44","first-page":"19","volume":"44","author":"S El Kafhali","year":"2017","unstructured":"El Kafhali, S., Hanini, M.: Stochastic modeling and analysis of feedback control on the QoS VoIP traffic in a single cell IEEE 802.16e networks. IAENG Int. J. Comput. Sci. 44(1), 19\u201328 (2017)","journal-title":"IAENG Int. J. Comput. Sci."},{"issue":"3","key":"4285_CR45","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1287\/opre.9.3.383","volume":"9","author":"JDC Little","year":"1961","unstructured":"Little, J.D.C.: A proof for the queuing formula: L = $$\\lambda$$W. Operations Res. 9(3), 383\u201387 (1961)","journal-title":"Operations Res."},{"issue":"23","key":"4285_CR46","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","journal-title":"Circulation"},{"issue":"2","key":"4285_CR47","first-page":"16","volume":"4","author":"M Soni","year":"2021","unstructured":"Soni, M., et al.: A review on privacy-preserving data preprocessing. J. Cybersecur. Inf. Manag. 4(2), 16\u20136 (2021)","journal-title":"J. Cybersecur. Inf. Manag."},{"key":"4285_CR48","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10479-018-2891-2","volume":"276","author":"A Kocheturov","year":"2019","unstructured":"Kocheturov, A., et al.: Massive datasets and machine learning for computational biomedicine: trends and challenges. Ann. Operations Res. 276, 5\u201334 (2019)","journal-title":"Ann. Operations Res."},{"key":"4285_CR49","volume-title":"Statistical Learning Theory","author":"VN Vapnik","year":"1998","unstructured":"Vapnik, V.N., Vapnik, V.: Statistical Learning Theory, vol. 2. Wiley, New York (1998)"},{"key":"4285_CR50","volume-title":"Readings in Uncertain Reasoning","author":"G Shafer","year":"1990","unstructured":"Shafer, G., Pearl, J.: Readings in Uncertain Reasoning. Morgan Kaufmann Publishers Inc., Burlington (1990)"},{"key":"4285_CR51","volume-title":"Applied Logistic Regression","author":"DW Hosmer Jr","year":"2004","unstructured":"Hosmer, D.W., Jr., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (2004)"},{"key":"4285_CR52","first-page":"182","volume-title":"Learning Classification Trees. Artificial Intelligence Frontiers in Statistics","author":"W Buntine","year":"2020","unstructured":"Buntine, W.: Learning Classification Trees. Artificial Intelligence Frontiers in Statistics, pp. 182\u2013201. Chapman and Hall\/CRC, Boca Raton (2020)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04285-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04285-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04285-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T14:40:20Z","timestamp":1723473620000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04285-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,21]]},"references-count":52,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["4285"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04285-x","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,21]]},"assertion":[{"value":"29 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2024","order":4,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}