Skip navigation

Energy efficient CM2M communications in E-healthcare systems

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:https://doi.org/10.1109/TSP.2017.8075954)

[img]
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 / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Biomedical Engineering Research Theme
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 04 Dec 2018 12:24
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT c
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/17130

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics