Co-operative and Energy Efficient Body Area and Wireless Sensor Networks for Healthcare Applications

Co-operative and Energy Efficient Body Area and Wireless Sensor Networks for Healthcare Applications
Author :
Publisher : Academic Press
Total Pages : 60
Release :
ISBN-10 : 9780128008317
ISBN-13 : 0128008318
Rating : 4/5 (318 Downloads)

Book Synopsis Co-operative and Energy Efficient Body Area and Wireless Sensor Networks for Healthcare Applications by : Akram Alomainy

Download or read book Co-operative and Energy Efficient Body Area and Wireless Sensor Networks for Healthcare Applications written by Akram Alomainy and published by Academic Press. This book was released on 2014-02-18 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advances in small and low-cost radio transceivers and RF front-ends development, the possibility of applying ubiquitous and non-invasive sensors integrated into user's daily clothing and living activities seems more feasible. The ability to share data increases the usefulness of personal information devices, providing features not possible with independent isolated devices. Current wireless sensor solutions are limited in that they do not provide the means to overcome obstacles and shadowing of propagating radio waves. Thus for reliable communications an increase in power consumption is required, reducing battery life. This book addresses the limitations outlined above by designing efficient and compact antenna systems. These systems will be cooperative and also aware of the surrounding environment and neighboring units, providing efficient and low power wireless connectivity for personal area network (PAN) and body area network (BAN) applications. - Analysis of wearable antenna design and performance - Addresses the Influence of body-worn antennas on radio channels and radio device performance from a power and error rate perspective. - Cooperative networking principles applied to body area networks, showing the pros and cons of such concepts - Real life case scenarios using ECG sample signals for potential application to healthcare monitoring.


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