Research Article
Cooperative and Low-Power Wireless Sensor Network for Efficient Body-Centric Communications in Healthcare Applications
@INPROCEEDINGS{10.1007/978-3-642-37893-5_39, author={Raffaele Bari and Akram Alomainy and Yang Hao}, title={Cooperative and Low-Power Wireless Sensor Network for Efficient Body-Centric Communications in Healthcare Applications}, proceedings={International Workshop on “Advances in Wireless Physical Layer Communications for Emerging Healthcare Applications”}, proceedings_a={IWAWPLC}, year={2013}, month={4}, keywords={Body-centric wireless communication co-operative networks energy efficiency healthcare monitoring}, doi={10.1007/978-3-642-37893-5_39} }- Raffaele Bari
Akram Alomainy
Yang Hao
Year: 2013
Cooperative and Low-Power Wireless Sensor Network for Efficient Body-Centric Communications in Healthcare Applications
IWAWPLC
Springer
DOI: 10.1007/978-3-642-37893-5_39
Abstract
Body Sensor Networks are an interesting emerging application to improve healthcare and the quality of life monitoring. In this paper, we compare the performances of multi-hop cooperative and single-hop networks with real-world sensor networks based on Zigbee technology. The network reliability, the data flow rate, the packet delivery ratio and the energy consumption are included as performances criteria. It is shown experimentally that the cooperative approach can provide a network more robust to link losses at the expenses of a lower bit rate and higher energy consumption. Specifically, for a packet delivery ratio >0.9, the cooperative scheme can provide the network with a link gain up to 14 dB traded off with an energy demand up to 30.7% higher and a data flow rate about 20% lower than a single-hop system. This work is a first exercise step in assessing reliability and life time trade-off with real-world platforms for body area sensor networks. Follow-up studies will address wireless ECG emulators with higher number of sensors (e.g. up to 10 for a typical 12-leads ECG system) employing ultra-low power chipsets in different specific health monitoring environments.
