The evolution of new technologies has drastically changed how wearable devices connect to networks. The advances in technologies such as artificial intelligence (AI), edge computing, Internet of Things (IoT), and 5G and beyond communication networks can be integrated into wireless body area networks (WBANs) to enhance their performance multiple times. Furthermore, wearable devices have become a major trend in healthcare and everyday life. From smartwatches to smart clothing, experts are finding innovative ways to integrate the human body and technology seamlessly. In many cases, the wearables are connected to smart devices and transfer the data through wireless signals. The major challenge here is that the network latency becomes very low when there is a broad range of smart devices, and it accesses the networks simultaneously. This is where the new enabling technologies such as IoT, edge computing, and AI can make a difference for e-health services.
The ample potential of the new enabling technologies that can help detect, capture, and recognize human movement in e-health applications is increasing and has attained a lot of research attention in recent years. Yet, perhaps for body sensor networks to reach their complete potential, it has become crucial to find new ways to enhance the performance of WBANs. Finding new enabling technologies and architectures promises novel use cases in healthcare, fitness, and well-being. The evolution of 5G networks has tremendous potential to transform the way people interact with body sensors and benefit from them. Furthermore, the use of IoT, edge computing, and AI technologies helps improve sensing, processing, and communication capabilities in e-health applications. The effective use of this technology helps in elderly care and provides services in a safe and socially acceptable manner.
E-health promises to transform the healthcare sector. Monitoring vital signs is an essential part of medical care and is currently performed by either wired sensors or loose wireless devices. The e-health community has recently turned its attention to WBANs, a technology based on miniaturized and lightweight sensors that can be stapled or pasted on the patient’s body. WBANs will allow for continuous, non-invasive monitoring of vital signs in a variety of environments. However, standard technologies similar to what is currently used for cellular networks will not be suitable for WBANs, as these networks need paradigm-shifting designs to achieve a sustainable coexistence with WLAN/WiFi and other wireless technologies. Hence, exploring more advanced technologies such as IoT, AI, and edge computing has become crucial.
This special section intends to bring out some of the new enabling technologies, architectures, and design protocols for WBANs. We welcome researchers and experts to present their novel and innovative solutions. Topics of interest include, but are not limited to, the following:
- Futuristic 5G and beyond wireless transmission for WBANs and e-health services
- AI-enabled edge computation for WBANs and e-health services
- Advance machine learning for predictive analytics in WBANs
- Functional architecture for WBAN-based healthcare systems for patient monitoring
- Privacy and security enhancement mechanism in WBANs
- Inter-WBAN and Intra-WBAN protocols for e-health services
- Investigation on interoperability and reliability in WBANs
- Spectrum management in WBANs using cognitive intelligence
- Advanced algorithms for information pre-processing in WBANs
- Energy-efficient routing algorithms for WBANs and e-health services
Manuscript Submission Deadline: 26 July 2022
Authors Notification Date: 11 September 2022
Revised Papers Due: 13 December 2022
Final Notification Date: 14 January 2023
For author information and guidelines on submission criteria, please visit the OJ-CS Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
- Tu Nguyen (Lead Guest Editor), Kennesaw State University, USA
- Vincenzo Piuri, University of Milan, Italy
- Joel Rodrigues, Federal University of Piauí (UFPI), Teresina – PI, Brazil
- B. B. Gupta, National Institute of Technology, Kurukshetra, India
- Lianyong Qi, Qufu Normal University, China
- Warren Huang-Chen Lee, National Chung Cheng University, Taiwan