Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Author(s): Dinesh Babu Mariappan*, R. Saminathan and K.M. Baalamurugan

Volume 17, Issue 7, 2024

Published on: 25 September, 2023

Page: [677 - 686] Pages: 10

DOI: 10.2174/2352096516666230817152400

Price: $65

conference banner
Abstract

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network.

Objective: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system.

Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC).

Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network.

Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

« Previous Next »
Graphical Abstract

[1]
L. Liu, M. Shafiq, V.R. Sonawane, M.Y.B. Murthy, P.C.S. Reddy, and K.M.N.C. Reddy, "Spectrum trading and sharing in unmanned aerial vehicles based on distributed blockchain consortium system", Comput. Electr. Eng., vol. 103, p. 108255, 2022.
[http://dx.doi.org/10.1016/j.compeleceng.2022.108255]
[2]
R. Dhanalakshmi, N.P.G. Bhavani, S.S. Raju, P.C. Shaker Reddy, D. Mavaluru, D.P. Singh, and A. Batu, "Onboard pointing error detection and estimation of observation satellite data using extended kalman filter", Comput. Intell. Neurosci., vol. 2022, pp. 1-8, 2022.
[http://dx.doi.org/10.1155/2022/4340897] [PMID: 36248921]
[3]
L. Sujihelen, R. Boddu, S. Murugaveni, M. Arnika, A. Haldorai, P.C.S. Reddy, S. Feng, and J. Qin, "Node replication attack detection in distributed wireless sensor networks", Wirel. Commun. Mob. Comput., vol. 2022, pp. 1-11, 2022.
[http://dx.doi.org/10.1155/2022/7252791]
[4]
A. Singhal, S. Varshney, T.A. Mohanaprakash, R. Jayavadivel, K. Deepti, P.C.S. Reddy, and M.B. Mulat, "Minimization of latency using multitask scheduling in industrial autonomous systems", Wirel. Commun. Mob. Comput., vol. 2022, pp. 1-10, 2022.
[http://dx.doi.org/10.1155/2022/1671829]
[5]
D. Balamurugan, S.S. Aravinth, P.C.S. Reddy, A. Rupani, and A. Manikandan, "Multiview Objects Recognition Using Deep Learning-Based Wrap-CNN with Voting Scheme", Neural Process. Lett., vol. 54, no. 3, pp. 1495-1521, 2022.
[http://dx.doi.org/10.1007/s11063-021-10679-4]
[6]
P.C. Shaker Reddy, and A. Sureshbabu, "An enhanced multiple linear regression model for seasonal rainfall prediction", Int. J. Sensors Wirel. Commun. Control, vol. 10, no. 4, pp. 473-483, 2020.
[http://dx.doi.org/10.2174/2210327910666191218124350]
[7]
K. Ashok, R. Boddu, S.A. Syed, V.R. Sonawane, R.G. Dabhade, and P.C.S. Reddy, "GAN Base feedback analysis system for industrial IOT networks", Automatika, vol. 64, no. 2, pp. 259-267, 2023.
[http://dx.doi.org/10.1080/00051144.2022.2140391]
[8]
Y. Sucharitha, Y. Vijayalata, and V.K. Prasad, "Predicting election results from twitter using machine learning algorithms", Recent Adv. Comput. Sci. Commun., vol. 14, no. 1, pp. 246-256, 2021.
[9]
P.C. Reddy, Y. Sucharitha, and G.S. Narayana, "Development of rainfall forecasting model using machine learning with singular spectrum analysis", IIUM Eng. J., vol. 23, no. 1, pp. 172-186, 2022.
[http://dx.doi.org/10.31436/iiumej.v23i1.1822]
[10]
R. Shaker, C. Pundru, and Y. Sucharitha, "IoT-enabled energy-efficient multipath power control for underwater sensor networks", Int. J. Sensors Wirel. Commun. Control, vol. 12, no. 6, 2022.
[11]
R. Sabitha, A.P. Shukla, A. Mehbodniya, and L. Shakkeera, "A fuzzy trust evaluation of cloud collaboration outlier detection in wireless sensor networks", Ad Hoc Sens. Wirel. Netw., vol. 53, no. 3/4, pp. 165-188, 2022.
[12]
P.C.S. Reddy, G. Suryanarayana, and S. Yadala, "Data Analytics in Farming: Rice price prediction in Andhra Pradesh", 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). Aligarh, India, 26-27 Nov 2022, pp.1-5.
[13]
K.A. Muthappa, A.S.A. Nisha, R. Shastri, V. Avasthi, and P.C.S. Reddy, "Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs", Appl. Nanosci., vol. 13, no. 8, pp. 5369-5378, 2023.
[http://dx.doi.org/10.1007/s13204-023-02814-5]
[14]
Y. Yuan, L. Huo, Z. Wang, and D. Hogrefe, "Secure APIT localization scheme against sybil attacks in distributed wireless sensor networks", IEEE Access, vol. 6, pp. 27629-27636, 2018.
[http://dx.doi.org/10.1109/ACCESS.2018.2836898]
[15]
B. Prasanalakshmi, K. Murugan, K. Srinivasan, S. Shridevi, S. Shamsudheen, and Y.C. Hu, "Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography", J. Supercomput., vol. 78, no. 1, pp. 361-378, 2022.
[http://dx.doi.org/10.1007/s11227-021-03861-x]
[16]
D. Liu, Y. Xu, and X. Huang, "Identification of location spoofing in wireless sensor networks in non-line-of-sight conditions", IEEE Trans. Industr. Inform., vol. 14, no. 6, pp. 2375-2384, 2018.
[http://dx.doi.org/10.1109/TII.2017.2767631]
[17]
Y. Chen, J. Sun, Y. Yang, T. Li, X. Niu, and H. Zhou, "PSSPR: A source location privacy protection scheme based on sector phantom routing in WSNs", Int. J. Intell. Syst., vol. 37, no. 2, pp. 1204-1221, 2022.
[http://dx.doi.org/10.1002/int.22666]
[18]
F. Wu, B. Zhou, and X. Zhang, "Identity-based proxy signature with message recovery over NTRU lattice", Entropy, vol. 25, no. 3, p. 454, 2023.
[http://dx.doi.org/10.3390/e25030454] [PMID: 36981342]
[19]
B. Ashreetha, M.R. Devi, U.P. Kumar, M.K. Mani, D.N. Sahu, and P.C.S. Reddy, "Soft optimization techniques for automatic liver cancer detection in abdominal liver images", Int. J. Health Sci., vol. 6, no. S1, pp. 10820-10831, 2022.
[http://dx.doi.org/10.53730/ijhs.v6nS1.7597]
[20]
G. Han, X. Miao, H. Wang, M. Guizani, and W. Zhang, "CPSLP: A Cloud-based scheme for protecting source location privacy in wireless sensor networks using multi-sinks", IEEE Trans. Vehicular Technol., vol. 68, no. 3, pp. 2739-2750, 2019.
[http://dx.doi.org/10.1109/TVT.2019.2891127]
[21]
J. Zhang, B. Chen, X. Cheng, H.T.T. Binh, and S. Yu, "PoisonGAN: Geerative poisoning attacks against federated learning in edge computing systnems", IEEE Internet Things J., vol. 8, no. 5, pp. 3310-3322, 2021.
[http://dx.doi.org/10.1109/JIOT.2020.3023126]
[22]
K. Kumar, S.V. Pande, S. Parvesh, and C. Abhay, "Intelligent controller design and fault prediction using machine learning model", Int. Trans. Electr. Energy Syst., vol. 2023, p. 9, 2023.
[23]
S.S. Ullah, I. Ullah, H. Khattak, M.A. Khan, M. Adnan, S. Hussain, N.U. Amin, and M.A.K. Khattak, "A lightweight identity-based signature scheme for mitigation of content poisoning attack in named data networking with internet of things", IEEE Access, vol. 8, pp. 98910-98928, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2995080]
[24]
Y.E. Sagduyu, Y. Shi, and T. Erpek, "Adversarial deep learning for over-the-air spectrum poisoning attacks", IEEE Trans. Mobile Comput., vol. 20, no. 2, pp. 306-319, 2021.
[http://dx.doi.org/10.1109/TMC.2019.2950398]
[25]
Y. Sucharitha, P.C.S. Reddy, and G. Suryanarayana, "Network intrusion detection of drones using recurrent neural networks", In: Drone Technology: Future Trends and Practical Applications., Wiley Online Library, 2023, pp. 375-392.
[26]
P. Chillakuru, M. Madiajagan, K.V. Prashanth, S. Ambala, P.C. Shaker Reddy, and J. Pavan, "Enhancing wind power monitoring through motion deblurring with modified GoogleNet algorithm", Soft Comput., pp. 1-11, 2023.
[http://dx.doi.org/10.1007/s00500-023-08358-8]
[27]
J. Chen, X. Zhang, R. Zhang, C. Wang, and L. Liu, "De-Pois: An attack-agnostic defense against data poisoning attacks", IEEE Trans. Inf. Forensics Security, vol. 16, pp. 3412-3425, 2021.
[http://dx.doi.org/10.1109/TIFS.2021.3080522]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy