Energy-Efficient Relay Transmission for WBAN: Energy Consumption Minimizing Design With Hybrid Supervised/Reinforcement Learning

Li, Shuang; Yang, Hong-Chuan; Xu, Fang; Hu, Huimin; Hu, Fengye

doi:10.1109/jiot.2024.3361772

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Reliable and Energy-Efficient Relay Transmission in WBANs with Wireless Power Transfer: Optimal Design with DRL

Li,

Xu,

Yang

2024

2024 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

View full text Add to dashboard Cite

Reliable and Energy-Efficient Relay Transmission in WBANs with Wireless Power Transfer: Optimal Design with DRL

Li,

Xu,

Yang

2024

2024 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

View full text Add to dashboard Cite

A Survey on Data-Driven Approaches for Reliability, Robustness, and Energy Efficiency in Wireless Body Area Networks

Majumdar,

Roy,

Sikdar

et al. 2024

Sensors

View full text Add to dashboard Cite

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications.

show abstract

Energy-Efficient Relay Transmission for WBAN: Energy Consumption Minimizing Design With Hybrid Supervised/Reinforcement Learning

Cited by 2 publications

References 43 publications

Reliable and Energy-Efficient Relay Transmission in WBANs with Wireless Power Transfer: Optimal Design with DRL

Reliable and Energy-Efficient Relay Transmission in WBANs with Wireless Power Transfer: Optimal Design with DRL

A Survey on Data-Driven Approaches for Reliability, Robustness, and Energy Efficiency in Wireless Body Area Networks

Contact Info

Product

Resources

About