article · Figshare · 2025

Fault-Tolerant Healthcare Wireless Sensor Networks via Golden Sine Seahorse Optimization and Quantum Dirichlet Convolutional Learning

Sugumar, Venkatesh, Karuppiah, Devi, Govindasamy, Kumaresan, Sundararaman, Shanthi

Cited by 0Open access

Abstract

Over the past few years, the application of Wireless Sensor Networks has rapidly increased in the healthcare sector due to their ability to continuously monitor crucial health metrics, including heart rate, blood pressure, and temperature. Traditional approaches have faced challenges in achieving fault tolerance, energy efficiency, and optimal data transmission in healthcare wireless sensor networks. To solve this challenges, this study aims to develop an advanced optimization framework. The proposed framework introduces a novel Golden Sine SeaHorse Optimization Boosted Quantum Dirichlet Convolutional Learning model. Golden Sine Optimization, SeaHorse Optimization, and Quantum Dirichlet Convolutional Learning are the advanced optimization techniques incorporated through this proposed framework to address network failures, improve energy efficiency, and optimize data delivery. Golden Sine Optimization and Quantum Dirichlet Convolutional Learning algorithms select the most economical and energy-efficient routes to optimize the exploration and exploitation balance of alternate paths. Network topology and communication paths are dynamically modified through the Quantum Dirichlet Convolutional Learning model. The proposed model achieved a packet delivery ratio of 96%, with an energy consumption of 119 millijoule and a fault recovery time of 45 milliseconds. The proposed framework exhibits significant potential for optimizing healthcare-based wireless sensor networks and fault-tolerant healthcare monitoring systems.

Cite this paper

APA 7

Sugumar, V., Karuppiah, D., Govindasamy, K., & Sundararaman, S. (2025). Fault-Tolerant Healthcare Wireless Sensor Networks via Golden Sine Seahorse Optimization and Quantum Dirichlet Convolutional Learning. Figshare. https://doi.org/10.6084/m9.figshare.30673702

MLA 9

Sugumar, Venkatesh, et al. “Fault-Tolerant Healthcare Wireless Sensor Networks via Golden Sine Seahorse Optimization and Quantum Dirichlet Convolutional Learning.” Figshare, 2025, https://doi.org/10.6084/m9.figshare.30673702.

Chicago

Sugumar, Venkatesh, Devi Karuppiah, Kumaresan Govindasamy, and Shanthi Sundararaman. 2025. “Fault-Tolerant Healthcare Wireless Sensor Networks via Golden Sine Seahorse Optimization and Quantum Dirichlet Convolutional Learning.” Figshare. https://doi.org/10.6084/m9.figshare.30673702.

Harvard

Sugumar, V. et al. (2025) ‘Fault-Tolerant Healthcare Wireless Sensor Networks via Golden Sine Seahorse Optimization and Quantum Dirichlet Convolutional Learning’, Figshare. Available at: https://doi.org/10.6084/m9.figshare.30673702.

BibTeX

@article{sugumar2025fault,
  title = {{Fault-Tolerant Healthcare Wireless Sensor Networks via Golden Sine Seahorse Optimization and Quantum Dirichlet Convolutional Learning}},
  author = {Sugumar, Venkatesh and Karuppiah, Devi and Govindasamy, Kumaresan and Sundararaman, Shanthi},
  journal = {Figshare},
  year = {2025},
  doi = {10.6084/m9.figshare.30673702}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.