Distance Energy-Efficient Soft Computing Model for Data Forwarding in Healthcare Sensor Network

Authors

  • T.Sravanti Associate Professor & HoD, Dept of ECE, Pallavi Engineering College, Kuntloor, Hayathnagar, Hyderabad, Telangana, 501 505, India. Author
  • K.Ram Mohan Rao Associate Professor, Dept of ECE, Sri Indu College of Engineering and Technology, Sheriguda, Hyderabad, Telangana, 501 510, India. Author
  • D.Sandhya Rani Associate Professor, Dept of ECE, Sri Indu College of Engineering and Technology, Sheriguda, Hyderabad, Telangana, 501 510, India. Author

DOI:

https://doi.org/10.69996/jsihs.2023001

Keywords:

Sensor environment, soft computing, threshold estimation, congestion control, data forwarding

Abstract

Data forwarding is a crucial process in computer networking and telecommunications. It involves the transmission of data packets from a source to a destination within a network. The forwarding process is fundamental for enabling communication between devices on different segments of a network and plays a vital role in maintaining the overall performance and reliability of the network infrastructure. This paper presents the development and evaluation of the Threshold Congestion Data Forwarding Soft Computing (TCDF-SC) algorithm, designed for healthcare sensor networks. The algorithm combines threshold-based congestion management with soft computing techniques to enhance data forwarding efficiency in dynamic healthcare environments. Through extensive simulations, TCDF-SC demonstrates scalability, achieving high total packets forwarded and maintaining a reliable packet delivery ratio as the network scales. The algorithm minimizes average transmission delay and optimizes energy consumption, ensuring timely and energy-efficient data transmission. Comparative analysis against existing protocols, LEACH and BCP, highlights TCDF-SC’s superior performance across key metrics, positioning it as a promising solution for healthcare sensor networks. This research contributes to the advancement of data transmission optimization in healthcare, addressing critical requirements of reliability, energy efficiency, and adaptability in dynamic healthcare settings. Further validation and real-world experimentation will enhance the algorithm’s applicability in diverse healthcare scenarios, fostering advancements in patient monitoring and healthcare applications.

References

[1] A.N. Bahache, N. Chikouche and F. Mezrag, “Authentication schemes for healthcare applications using wireless medical sensor networks: a survey,” SN Computer Science, vol.3, no.5, pp.382, 2022.

[2] D. Stone, L.Michalkova and V.Machova, “Machine and deep learning techniques, body sensor networks, and Internet of Things-based smart healthcare systems in COVID-19 remote patient monitoring,” American Journal of Medical Research, vol.9, no.1, pp.97-112, 2022.

[3] H.M. Ahmed and A.N. Rashid, “Wireless sensor network technology and adoption in healthcare: a review,” In AIP Conference Proceedings, vol. 2400, no.1, 2022.

[4] J. A. I. S. Masood, M. Jeyaselvi, N. Senthamarai, S. Koteswari, M. Sathya et al., “Privacy preservation in wireless sensor network using energy efficient multipath routing for healthcare data Measurement,” Sensors, vol.29, pp.100867, 2023.

[5] S. Wang, W. Deng, T. Yang, G. Tian, D. Xiong et al., “Body-area sensor network featuring micropyramids for sports healthcare,” Nano Research, vol.16, no.1, pp.1330-1337, 2023.

[6] S. Gherairi, “Healthcare: A priority-based energy harvesting scheme for managing sensor nodes in WBANs,” Ad Hoc Networks, vol.133, pp.102876, 2022.

[7] S.K.S. Durai, B. Duraisamy and J.T. Thirukrishna, “Certain investigation on healthcare monitoring for enhancing data transmission in WSN,” International journal of wireless information networks, vol.30, no.1, pp.103-110, 2023.

[8] K.K.Sahoo, R.Ghosh, S.Mallik, A. Roy, P.K. Singh et al., “Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems,” Scientific Reports, vol.13, no.1, pp.965, 2023.

[9] F. Serpush, M.B. Menhaj, B. Masoumi and B. Karasfi, “Wearable sensor-based human activity recognition in the smart healthcare system,” Computational intelligence and neuroscience, vol.2022, 2022.

[10] S. Akbar, M.M. Mehdi, M.H. Jamal, I. Raza, S.A. Hussain et al., “Multipath routing in wireless body area sensor network for healthcare monitoring,” In Healthcare, vol. 10, no. 11, pp. 2297, 2022.

[11] T. Jabeen, I. Jabeen, H. Ashraf, N.Z. Jhanjhi, A. Yassine et al., “An intelligent healthcare system using iot in wireless sensor network,” Sensors, vol.23, no.11, pp.5055, 2023.

[12] H. Jeong, S.W. Lee, M. Hussain Malik, E. Yousefpoor, M.S. Yousefpoor et al., “SecAODV: a secure healthcare routing scheme based on hybrid cryptography in wireless body sensor networks,” Frontiers in Medicine, vol.9, pp.829055, 2022.

[13] M.D.K.J. Bahadur and L. Lakshmanam, “Wireless sensor network optimization for multi-sensor analytics in smart healthcare system,” Specialusis Ugdymas, vol.1, no.43, pp.7807-7819, 2022.

[14] A. Petrenko and O. Petrenko, “Wireless sensor networks for healthcare on SOA,” In System analysis and artificial intelligence, Cham: Springer Nature Switzerland, pp. 101-116, 2023.

[15] M. Ghahramani and R. Javidan, “Time dependency: An efficient biometric-based authentication for secure communication in wireless healthcare sensor networks,” Journal of Computer Virology and Hacking Techniques, vol.19, no.2, pp.303-317, 2023.

[16] M. Hosseinzadeh, A.H. Mohammed, A. M. Rahmani, F.Alenizi, S.M.Zandavi et al., “A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare,” Plos one, vol.18, no.10, pp.e0290119, 2023.

[17] O.B.J. Rabie, S. Selvarajan, T. Hasanin, G.B. Mohammed, A.M. Alshareef et al., “A full privacypreserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (hwmsns),” International Journal of Information Security, pp.1-30, 2023.

[18] S. Yu and Y. Park, “A robust authentication protocol for wireless medical sensor networks using blockchain and physically unclonable functions.” IEEE Internet of Things Journal, vol.9, no.20, pp.20214-20228, 2022.

[19] J.S. Priyanka, M.S. Kiran and P. Nalla, “A secured IoT-based health care monitoring System using body sensor network,” In Emergent Converging Technologies and Biomedical Systems: Select Proceedings of ETBS 2021, Singapore: Springer Singapore, pp. 483-490, 2022.

[20] P. Singh, S. Khan, Y.V. Singh and R.S. Singh, “A secure and stable humanoid healthcare information processing and supervisory method with iot-based sensor network,” Journal of Sensors, vol.2022, 2022.

[21] L. Deng, B. Wang, Y. Gao, Z. Chen and S. Li, “Certificateless anonymous signcryption scheme with provable security in the standard model suitable for healthcare wireless sensor networks,” IEEE Internet of Things Journal, vol.10, no.18, pp. 15953 – 15965, 2023.

[22] C. Pandey, S. Sharma and P. Matta, “Data analysis and modeling of body sensor network in healthcare application,” In 2022 6th International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, pp. 590-596, 2022.

Downloads

Published

2023-12-31

How to Cite

T.Sravanti, K.Ram Mohan Rao, & D.Sandhya Rani. (2023). Distance Energy-Efficient Soft Computing Model for Data Forwarding in Healthcare Sensor Network. Journal of Sensors, IoT & Health Sciences (JSIHS,ISSN: 2584-2560), 1(1), 1-14. https://doi.org/10.69996/jsihs.2023001