Internet of Things (IoT) technology provides a tremendous and structured solution to tackle service deliverance aspects of healthcare in terms of mobile health and remote patient tracking. In medicine observation applications, IoT and cloud computing serves as an assistant in the health sector and plays an incredibly significant role. Health professionals and technicians have built an excellent platform for people with various illnesses, leveraging principles of wearable technology, wireless channels, and other remote devices for low-cost healthcare monitoring.
OBJECTIVE:
This paper proposed the Fog-IoT-assisted multisensor intelligent monitoring model (FIoT-MIMM) for analyzing the patient’s physical health condition.
METHOD:
The proposed system uses a multisensor device for collecting biometric and medical observing data. The main point is to continually generate emergency alerts on mobile phones from the fog system to users. For the precautionary steps and suggestions for patients’ health, a fog layer’s temporal information is used.
RESULTS:
Experimental findings show that the proposed FIoT-MIMM model has less response time and high accuracy in determining a patient’s condition than other existing methods. Furthermore, decision making based on real-time healthcare information further improves the utility of the suggested model.
MutlagAAAbd GhaniMKArunkumarNAMohammedMAMohdO. Enabling technologies for fog computing in healthcare IoT systems. Future Generation Computer Systems.2019; 90: 62-78.
2.
HsuCHManogaranGPanchatcharamPVivekanandanS. A New Approach for Prediction of Lung Carcinoma Using Back Propagation Neural Network with Decision Tree Classifiers. In 2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2) 2018 Nov 18 (pp. 111-115). IEEE.
3.
NamasudraSDeviDKadrySSundarasekarRShanthiniA. Towards DNA based data security in the cloud computing environment. Computer Communications.2020 Feb 1; 151: 539-47.
4.
SantosGLEndoPTda Silva LisboaMFFda SilvaLGFSadokDKelnerJLynnT. Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures. Journal of Cloud Computing.2018; 7(1): 16.
5.
AminRKumarNBiswasGPIqbalRChangV. A light weight authentication protocol for IoT-enabled devices in distributed Cloud Computing environment. Future Generation Computer Systems.2018 Jan 1; 78: 1005-19.
6.
de Moura CostaHJda CostaCAda Rosa RighiRAntunesRS. Fog computing in health: A systematic literature review. Health and Technology.2020; 10: 1025-1044.
7.
KaurKGargSAujlaGSKumarNRodriguesJJGuizaniM. Edge computing in the industrial internet of things environment: Software-defined-networks-based edge-cloud interplay. IEEE communications magazine.2018 Feb 13; 56(2): 44-51.
8.
FarahaniBFirouziFChangVBadarogluMConstantNMankodiyaK. Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems.2018; 78: 659-676.
9.
ElhosenyMAbdelazizASalamaASRiadAMMuhammadKSangaiahAK. A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems.2018 Sep 1; 86: 1383-94.
10.
MoghadasERezazadehJFarahbakhshR. An IoT patient monitoring based on fog computing and data mining: Cardiac arrhythmia usecase. Internet of Things.2020; 11: 100251.
11.
DarwishAHassanienAEElhosenyMSangaiahAKMuhammadK. The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing.2019 Oct 1; 10(10): 4151-66.
12.
HabibCMakhoulADaraziRCouturierR. Health risk assessment and decision-making for patient monitoring and decision-support using wireless body sensor networks. Information Fusion.2019; 47: 10-22.
13.
KumarPMLokeshSVaratharajanRBabuGCParthasarathyP. Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Generation Computer Systems.2018 Sep 1; 86: 527-34.
14.
YacchiremaDCSarabia-JácomeDPalauCEEsteveM. A smart system for sleep monitoring by integrating IoT with big data analytics. IEEE Access.2018; 6: 35988-36001.
15.
VaratharajanRManogaranGPriyanMK. A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications.2018 Apr 1; 77(8): 10195-215.
16.
ForooghifarFAminifarAAtienzaD. Resource-aware distributed epilepsy monitoring using self-awareness from edge to cloud. IEEE Transactions on Biomedical Circuits and Systems.2019; 13(6): 1338-1350.
17.
MolanoJILovelleJMMontenegroCEGranadosJJCrespoRG. Metamodel for integration of internet of things, social networks, the cloud and industry 4.0. Journal of Ambient Intelligence and Humanized Computing.2018 Jun 1; 9(3): 709-23.
18.
AlbahriOSZaidanAAZaidanBBHashimMAlbahriASAlsalemMA. Real-time remote health-monitoring Systems in a Medical Centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of Medical Systems.2018; 42(9): 164.
19.
SharmaSLuhachAKSinhaSA. An optimal load balancing technique for cloud computing environment using bat algorithm. Indian J Sci Technol.2016 Jul; 9(28): 1-4.
20.
SharmaSLuhachAKJyotiK. A novel approach of load balancing in cloud computing using computational intelligence. International Journal of Engineering and Technology.2016; 8(1): 124-8.
21.
JaleelAMahmoodTHassanMABanoGKhurshidSK. Towards medical data interoperability through collaboration of healthcare devices. IEEE Access.2020; 8: 132302-132319.
22.
SangeethaTAmalanathanGM. Outlier detection in neutrosophic sets by using rough entropy based weighted density method. CAAI Transactions on Intelligence Technology.2020; 5(2): 121-127. doi: 10.1049/trit.2019.0093.
23.
UddinMZHassanMMAlsanadASavaglioC. A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare. Information Fusion.2020; 55: 105-115.
24.
VeerappanCAlbertB. Multiple-criteria decision analysis process by using prospect decision theory in interval-valued neutrosophic environment. CAAI Transactions on Intelligence Technology.2020; 5(3): 209-221. doi: 10.1049/trit.2020.0040.
25.
Hernández-PeñalozaGBelmonte-HernándezAQuintanaMÁlvarezF. A multi-sensor fusion scheme to increase life autonomy of elderly people with cognitive problems. IEEE Access.2017; 6: 12775-12789.
26.
ManiNSinghANimmagaddaSL. An IoT guided healthcare monitoring system for managing real-time notifications by fog computing services. Procedia Computer Science.2020; 167: 850-859.
27.
GiaTNDhaouIBAliMRahmaniAMWesterlundTLiljebergPTenhunenH. Energy efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease. Future Generation Computer Systems.2019; 93: 198-211.
28.
TiwariKKumarSTiwariRK. FOG assisted healthcare architecture for pre-operative support to reduce latency. Procedia Computer Science.2020; 167: 1312-1324.
29.
Sarabia-JácomeDUsachRPalauCEsteveM. Highly-Efficient Fog-Based Deep Learning Aal Fall Detection System. Internet of Things, 2020; p. 100185.
30.
GumaeiAHassanMMAlelaiwiAAlsalmanH. A hybrid deep learning model for human activity recognition using multimodal body sensing data. IEEE Access.2019; 7: 99152-99160.
31.
KhanSQamarRZaheenRAl-AliARAl NabulsiAAl-NashashH. Internet of things based multisensor patient fall detection system. Healthcare Technology Letters.2019; 6(5): 132-137.
32.
Sánchez-GallegosDDGalaviz-MosquedaAGonzalez-CompeanJLVillarreal-ReyesSPerez-RamosAECarrizales-EspinozaDCarreteroJ. On the continuous processing of health data in edge-fog-cloud computing by using micro/nanoservice composition. IEEE Access.2020; 8: 120255-120281.
33.
LiuBHNguyenNTPhamVT. An Efficient Method for Sweep Coverage with Minimum Mobile Sensor, 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kitakyushu, Japan, 2014, pp. 289-292, doi: 10.1109/IIH-MSP.2014.78..
34.
SoodSKMahajanI. IoT-fog-based healthcare framework to identify and control hypertension attack. IEEE Internet of Things Journal.2018; 6(2): 1920-1927.
35.
HassenHBDghaisWHamdiB. An E-health system for monitoring elderly health based on Internet of Things and Fog computing. Health Information Science and Systems.2019; 7(1): 24.
36.
ZhangCChoHHChenCY. Emergency-level-based healthcare information offloading over fog network. Peer-to-Peer Networking and Applications.2020; 13(1): 16-26.
37.
MukherjeeAGhoshSBehereAGhoshSKBuyyaR. Internet of Health Things (IoHT) for Personalized Health Care using Integrated Edge-Fog-Cloud Network.
38.
DebaucheOMahmoudiSMannebackPAssilaA. Fog IoT for Health: A new Architecture for Patients and Elderly Monitoring. Procedia Computer Science.2019; 160: 289-297.
39.
TuliSBasumataryNGillSSKahaniMAryaRCWanderGSBuyyaR. Healthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments. Future Generation Computer Systems.2020; 104: 187-200.
40.
MahmoudMMRodriguesJJSaleemKAl-MuhtadiJKumarNKorotaevV. Towards energy-aware fog-enabled cloud of things for healthcare. Computers Electrical Engineering.2018; 67: 58-69.
