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An Intelligent Framework for Energy Efficient Health Monitoring System Using Edge-Body Area Networks

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Abstract:

Body Area Networks (BAN) consists of sensors, microcontrollers interfaced with the wireless transceivers. BAN sensors are implanted or placed on the body's surface which allows for continuous monitoring of patients' health parameters. According to recent studies, BAN is a viable option for an effective transmission of detected parameters to the nearby health care centers. This transmission helps in energy consumption for further better diagnosis. With the advent of machine learning and Internet of Things (IoT), BAN has taken the dimension in achieving the better performance with limited threshold. Although, BANs are light weight implanted nodes, the problem in improving the performance still remains demur for researchers. This paper proposes the edge based BAN which integrates the powerful Bi layered feed forward (BLFF) learning models for efficient data transmission with lower consumption of energy. The proposed model works on the adaptive distance principle of Extreme Learning Machines (ELM) which detects the cluster head BAN network. The extensive experimentation has been carried out to find the consumption of energy in the network. Additionally, the performance of the proposed ELM-BLFF learning model has been compared with the other machine learning models which are integrated in BAN-IoT frameworks. An experimental result demonstrates that the proposed ELM-BLFF model outperforms the traditional learning model with 30% lesser in terms of energy consumption.

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Info:

Periodical:

Advances in Science and Technology (Volume 124)

Pages:

836-843

DOI:

https://doi.org/10.4028/p-3hb4p8

Citation:

Cite this paper

Online since:

February 2023

Authors:

R. Abirami*, E. Poovammal

Keywords:

Cluster Head, Energy, Extreme Learning Machines, Machine Learning, Sensor

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* - Corresponding Author

References

[1] J. Anand and D. Sethi, Comparative analysis of energy efficient routing in WBAN,, 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), 2017, pp.1-6,.

DOI: 10.1109/ciact.2017.7977373

Google Scholar

[2] Sharma Raju, Hardeep. Singh and Ryait Anuj Kumar Gupta, Wireless Body Area Nework-A Review,, An International Journal of Engineering Sciences, (2016).

Google Scholar

[3] Sara Raed, Salah A. Alabady, A Review on Energy Efficient Routing Protocols in Wireless Body Area Networks (WBAN) for Healthcare,, Journal of Network Communications and Emerging Technologies (JNCET) Volume 10, Issue 10, October (2020).

Google Scholar

[4] S. Ahmed, N. Javaid, S. Yousaf, A. Ahmad, M. M. Sandhu, Z. A. Khan, et al., Co-LAEEBA: Cooperative Link Aware and Energy Efficient Protocol for Wireless Body Area Networks,, Articles in Computers in Human Behaviour Reasearch Gate, January (2014).

DOI: 10.1016/j.chb.2014.12.051

Google Scholar

[5] D. Sethi and P. P. Bhattacharya, A Study on Energy Efficient and Reliable Data Transfer (EERDT) Protocol for WBAN,, 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), pp.254-258, (2016).

DOI: 10.1109/cict.2016.57

Google Scholar

[6] Sadaf. Talha, Rizwan. Ahmad and Adnan Khalid. Kiani, Priority Based Energy Aware (PEA) Routing Protocol for WBANs,, IEEE, (2015).

DOI: 10.1109/vtcfall.2015.7391095

Google Scholar

[7] R. Kaur, R. Pasricha, and B. Kaur, A study of wireless body area networksand its routing protocols for healthcare environment,,Recent Advances inElectrical & Electronic Engineering, vol. 13, no. 2, p.136– 152, (2020).

DOI: 10.2174/2352096512666190305152857

Google Scholar

[8] R. Sharma and S. S. Kang, Wban for healthcare applications: A survey ofcurrent challenges and research opportunities,,Journal of Critical Reviews,vol. 7, no. 17, p.2444–2453, (2020).

Google Scholar

[9] A. A. Abdellatif, A. Mohamed, C. F. Chiasserini, M. Tlili and A. Erbad, Edge Computing for Smart Health: Context-Aware Approaches, Opportunities, and Challenges,, in IEEE Network, vol. 33, no. 3, pp.196-203, May/June 2019,.

DOI: 10.1109/mnet.2019.1800083

Google Scholar

[10] A. S. Abiodun, M. H. Anisi and M. K. Khan, Cloud-Based Wireless Body Area Networks: Managing Data for Better Health Care,, in IEEE Consumer Electronics Magazine, vol. 8, no. 3, pp.55-59, May 2019,.

DOI: 10.1109/mce.2019.2892244

Google Scholar

[11] Farhan Aadil1 , Bilal Mehmood1 , Najam Ul Hasan2 , Sangsoon Lim3,*, Sadia Ejaz1 and Noor Zaman, Remote Health Monitoring Using IoT-Based Smart Wireless Body Area Network,, Computers, Materials & Continua,.

DOI: 10.32604/cmc.2021.014647

Google Scholar

[12] Samira Akhbarifar1 & Hamid Haj Seyyed Javadi2 & Amir Masoud Rahmani1 & Mehdi Hosseinzadeh, A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment,, Personal and Ubiquitous Computing https://doi.org/10.1007/s00779-020-014753.

DOI: 10.1007/s00779-020-01475-3

Google Scholar

[13] Verma P, Sood SK, Kalra S, Cloud-centric IoT based student healthcare monitoring framework. J Ambient Intell Humaniz Comput, 2018, 9:1293–1309.

DOI: 10.1007/s12652-017-0520-6

Google Scholar

[14] Kumar, P. M., Lokesh, S., Varatharajan, R., Chandra Babu, G., & Parthasarathy, P. (2018). Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Generation Computer Systems, 86, 527–534.

DOI: 10.1016/j.future.2018.04.036

Google Scholar

[15] Fahad Khan, b Ateeq Ur Rehman, c Jawad Khan, d Muhammad Tariq Sadiq, eRao Muhammad Asif, a Zahid Mahmood, Real-time Health Monitoring System Using Wireless Body Area Network,, Journal of Applied and Emerging Sciences Vol (10), Issue (01)DOI: https://doi.org/10.36785/BUITEMS.JAES.332.

DOI: 10.36785/jaes.101332

Google Scholar

[16] Fan Wu , Taiyang Wu and Mehmet Rasit Yuce, An Internet-of-Things (IoT) Network System for Connected Safety and Health Monitoring Applications,, Sensors 2019, 19, 21;.

DOI: 10.3390/s19010021

Google Scholar

[17] Ashfaq, Z.; Mumtaz, R.; Rafay, A.; Zaidi, S.M.H.; Saleem, H.; Mumtaz, S.; Shahid, A.; Poorter, E.D; Moerman, I. Embedded AI-Based Digi-Healthcare. Appl. Sci. 2022, 12, 519. https://doi.org/10.3390/ app12010519.

DOI: 10.3390/app12010519

Google Scholar

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