Research Article
An Intelligent and Secured Privacy Preserving Framework For Wireless Body Area Networks (WBANs)
@ARTICLE{10.4108/eai.15-3-2022.173609, author={Muhammad Shoaib Akhtar and Tao Feng}, title={An Intelligent and Secured Privacy Preserving Framework For Wireless Body Area Networks (WBANs)}, journal={EAI Endorsed Transactions on Creative Technologies}, volume={9}, number={30}, publisher={EAI}, journal_a={CT}, year={2022}, month={3}, keywords={WBANs, Blockchain, eHealth}, doi={10.4108/eai.15-3-2022.173609} }- Muhammad Shoaib Akhtar
Tao Feng
Year: 2022
An Intelligent and Secured Privacy Preserving Framework For Wireless Body Area Networks (WBANs)
CT
EAI
DOI: 10.4108/eai.15-3-2022.173609
Abstract
In recent years, developments in information technology and emerging technologies have greatly benefited e-health. The adoption of Wireless Body Area Networks is a perfect example (WBAN). The WBAN is a popular device. Medical care can be delivered remotely to patients in their own homes via tele-homecare (also known as tele-diagnosis). Diabetes, dementia, falls, congestive heart failure, asthma, and infertility are among the conditions for which they are prescribed. An emergency response time could be sped up by using WBANs to monitor patients' health in real time. From a more traditional strategy to a more updated patient-centered one has been seen in recent years. Telemedicine and telemonitoring are two examples of current patient-centered practises that use technology to make it easier for patients to provide personal information. Telemonitoring has been discovered as a feasible research subject using the most up-to-date methods of turning raw data into meaningful information. WBAN stability is dependent on preventing node overheating and conserving energy. LB-EESAA routing mechanism for WBANs and the early warning system for attacks during Blockchain transactions are discussed in this study. Results show that LB-EESAA performs best in terms of both the number of live and dead nodes and the length of protocol stability. We were able to raise the level of security of the organisation after increasing the efficiency of WBANs. We examined the read throughput and basic transaction throughput of a blockchain-enabled system. After assuring and safeguarding the privacy of the system, we used the Logi-XGB prediction model machine learning to forecast assaults. Using the Logi-XGB model, 95.7 percent of the assault could be predicted in its early stages.
Copyright © 2022 Muhammad Shoaib Akhtar et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
