Energy efficient CM2M communications in E-healthcare systems
Alabadi, Sajid, Wu, Ruiheng and Habtay, Yehdego (2017) Energy efficient CM2M communications in E-healthcare systems. In: 2017 40th International Conference on Telecommunications and Signal Processing (TSP). IEEE, pp. 140-143. ISBN 978-1509039838 (doi:10.1109/TSP.2017.8075954)
Preview |
PDF (Author Accepted Manuscript)
17130 WU_Energy_Efficient_CM2M_Communications_2017.pdf - Accepted Version Download (392kB) | Preview |
Abstract
In this paper, cognitive machine to machine (CM2M) gateways in e-healthcare systems are presented by accessing a number of channels via periodic sensing and spectrum handoff. An optimal energy efficient spectrum management mechanism with multiple thresholds is proposed. The developed mechanism aims to reduce energy consumption in the system by optimizing the spectrum sensing and the channel switching, while decreasing the probability of collision, with assuring the reliability thresholds, throughput, and delay. Subsequently an antenna selection sensing (ASS) scheme is used to improve sensing accuracy. The simulation results show that the energy efficiency of the CM2M gateways can be improved significantly.
Item Type: | Conference Proceedings |
---|---|
Title of Proceedings: | 2017 40th International Conference on Telecommunications and Signal Processing (TSP) |
Additional Information: | The 2017 40th International Conference on Telecommunications and Signal Processing (TSP) was held in Barcelona, Spain, 5-7 July 2017. |
Uncontrolled Keywords: | M2M, Cognitive Radio, CM2M, Energy efficiency, e-healthcare |
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
Faculty / School / Research Centre / Research Group: | Faculty of Engineering & Science Faculty of Engineering & Science > Biomedical Engineering Research Theme Faculty of Engineering & Science > School of Engineering (ENG) |
Last Modified: | 04 Dec 2018 12:24 |
URI: | http://gala.gre.ac.uk/id/eprint/17130 |
Actions (login required)
View Item |
Downloads
Downloads per month over past year