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.
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Artificial Intelligence (AI) Driven Wireless Body Area Networks: Challenges and Directions
Significant growth of internet applications in recent years has raised a lot of challenges to networks. One of the important applications is smart and connected health (SCH), which utilizes sensing, communication networks and artificial intelligent (AI) techniques to offer healthcare services to the users. In SCH applications, Wireless Body Area Networks (WBANs) consisting of a group of Lightweight and wearable devices designed for use within the proximity of the human body, is a key infrastructure. In this short paper, we discussed the possibility of exploring AI techniques for WBANs to improve network performance and enhance health services. In addition, we present the literature review of AI driven networks for SCH, its related challenges and future directions.
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- Award ID(s):
- 1744272
- PAR ID:
- 10201828
- Date Published:
- Journal Name:
- 2019 IEEE International Conference on Industrial Internet (ICII)
- Page Range / eLocation ID:
- 428 to 429
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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