Abstract
Internet of Things (IoT) in association with cloud technologies are the raising stars in information technology industry, which provides a lot of innovative gadgets to several industries to automate their needs as well as monitoring the events properly without any human interventions. The most common and emerging needs now-a-days is the development of new gadgets to support healthcare industry to rectify the medical flaws and save the human life. In one side the booming technologies such as Internet of Things and Cloud platforms are available and the other hand a drastic need to introduce an intelligent gadget for medical oriented needs to save one’s life in critical situations. This paper aims to create a bridge between the two and introduce a new device to combine healthcare with recent technological developments. The proposed approach introduces a new algorithm called iCloud Assisted Intensive Deep Learning (iCAIDL), which provides support to healthcare medium as well as patients by means of applying the intelligent cloud system along with machine learning strategies and this proposed algorithm is derived from the base of deep learning norms. An iCloud Assisted Intensive Deep Learning algorithm initially begins with the flow of collecting the existing health records from the data repository and train the system with deep learning principles. Once the data training phase ends the proposed algorithm begins to get the live data from patient and this data is assumed as a testing data, which will be processed by using intensive deep learning principles and store the resulting summary into the Cloud repository by means of enabling Internet of Things feature in association with the proposed algorithm called iCAIDL. This is transparent in both the state of users like doctors as well as the patients to monitor the health records in intelligent manner. A Smart Medical Gadget is designed to collect the health record from patients and maintain it into the medical repository for testing phase, in which it collects the patient heart rate, pressure level, blood flow in intensive manner. The logic of IoT is connected with the machine learning process such as: the data accumulated from the smart Medical Gadget needs to be send to the Server end for processing, here the processing is mentioned as the machine learning based processing, in which the received data is considered to be the testing data and the results are emulated accordingly. Once the results are emulated that also will be coming for training part for the upcoming testing data. So, that the data coming from the Medical Gadget is considered to be the testing data and once the processing is done it will be considered to be the training data for further medical summaries. The performance evaluations of the proposed approach is estimated based on the following metrics such as data transfer ratio from Smart medical Gadget to the server end, storage accuracy and the communication efficiency. Empirical results are attained using simulation, in which it produces a drastical improvement of healthcare parameters by merge the proposed algorithm called iCloud Assisted Intensive Deep Learning.
Similar content being viewed by others
References
Aazam M, Huh E-N (2016) Fog computing: the cloud- IoT\IoE middleware paradigm. IEEE Potentials 35(3):40–44
Ahmad M, Amin MB, Hussain S, Kang BH, Cheong T, Lee S (2016) Health fog: a novel framework for health and wellness applications. J Supercomput 72(10):3677–3695
Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805
Barcelo M, Correa A, Llorca J, Tulino AM, Vicario JL, Morell A (2016) IoT-cloud service optimization in next generation smart environments. IEEE Journal on Selected Areas in Communications 34(12):4077–4090
Biswas AR, Giaffreda R (2014) IoT and cloud convergence: opportunities and challenges. In: World IEEE (ed) Forum on internet of things (WF-IoT), Seoul, South Korea, 6–8 march. IEEE, New York, pp 375–376
Catarinucci L, De Donno D, Mainetti L et al (2015) An IoTaware architecture for smart healthcare systems. IEEE Internet Things J 2(6):515–526
Cook DJ, Das SK (2004) Smart environments: technology, protocols and applications, vol 43. John Wiley & Sons, New York
Cook DJ, Schmitter-Edgecombe M (2009) Assessing the quality of activities in a smart environment. Methods Inf Med 48(5):480–485
Dohr A, Modre-Opsrian R, Drobics M, et al. (2010) The internet of things for ambient assisted living. In: Seventh international conference on information technology: new generations, Las Vegas, NV, 12–14 April, pp. 804–809. New York: IEEE.
Gelogo YE, Hwang HJ, Kim H-K (2015) Internet of things (IoT) framework for u-healthcare system. International Journal of Smart Home 9(11):323–330
Ghanavati S, Abawajy JH, Izadi D, Alelaiwi AA (2017) Cloudassisted IoT-based health status monitoring framework. Clust Comput 20(2):1843–1853
Gomes E, Dantas MAR, de Macedo DDJ, et al. (2016) Towards an infrastructure to support big data for a smart city project. In: 2016 IEEE 25th International conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), Paris, 13–15 June, pp. 107–112. New York: IEEE.
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Futur Gener Comput Syst 29(7):1645–1660
Haller S, Karnouskos S and Schroth C (2009) The internet of things in an enterprise context. In: Domingue J, Fensel D and Traverso P (eds) Future internet—FIS2008. Berlin; Heidelberg: Springer, pp. 14–28.
Islam SR, Kwak D, Kabir MH et al (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708
Junior GL, do Nascimento RPC, Dantas MAR, et al. (2017) A platform for vehicular networks in the cloud to applications in intelligent transportation systems. In: 2017 IEEE 26th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), Poznan, 21–23 June, pp. 101–106. New York: IEEE
Kavitha Y, Lavanya M, Mounika A et al (2017) A secure IoT-based modern healthcare system using body sensor network. International Journal of Innovative Research in Science, Engineering and Technology 6(3):156–160
Kim S (2015) Nested game-based computation offloading scheme for mobile cloud IoT systems. EURASIP J Wirel Commun Netw 2015(1):229
Kreutz D, Malichevskyy O, Feitosa E, Cunha H, da Rosa Righi R, de Macedo DDJ (2016) A cyberresilient architecture for critical security services. J Netw Comput Appl 63:173–189
Mekala MS, Viswanathan P (2017) A survey: smart agriculture IoT with cloud computing. In: International conference on microelectronic devices, circuits and systems (ICMDCS), Vellore, India, 10–12 August, pp. 1–7. New York: IEEE.
Munir A, Kansakar P, Khan SU (2017) IFCIoT: integrated fog cloud IoT: a novel architectural paradigm for the future internet of things. IEEE Consumer Electronics Magazine 6(3):74–82
Varga A (2001) The OMNeT++ discrete event simulation system. In: proceedings of the European simulation multiconference, Prague, 6–9 June, pp. 319–324. New York: ACM.
Vijayakumar K, Arun C (2017) Analysis and selection of risk assessment frameworks for cloud based enterprise applications. Biomed Res
Vijayakumar K, Kumar KPM, Jesline D (2019) Implementation of software agents and advanced AoA for disease data analysis. J Med Syst
World Health Organization (2018) Ageing and life course. Available at: http://www.who.int/ageing/en/
Yang Z, Zhou Q, Lei L, Zheng K, Xiang W (2016) An IoT-cloud based wearable ECG monitoring system for smart healthcare. J Med Syst 40(12):286
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kondaka, L.S., Thenmozhi, M., Vijayakumar, K. et al. An intensive healthcare monitoring paradigm by using IoT based machine learning strategies. Multimed Tools Appl 81, 36891–36905 (2022). https://doi.org/10.1007/s11042-021-11111-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-021-11111-8