Alabadi, Sajad Abed Almahdi Abedali (2017) Applications of machine to machine communication in remote healthcare systems. PhD thesis, University of Greenwich.

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Abstract

Wireless Machine-To-Machine (M2M) communications in healthcare systems will play a large part in the medical sector in the near future, enabling a number of applications to speed up medical treatment, decreasing the cost, and increasing the flexibility and efficiency of the hospitals and medical bodies. The work undertaken in this thesis is to improve the spectrum and energy efficiency of M2M communications in the medical sector by exploiting Cognitive radio (CR) technology.

First, the thesis considered an efficient aggregation-based spectrum assignment algorithm for Cognitive Machine-To-Machine (CM2M) networks. The proposed algorithm takes practical thresholds including Co-Channel Interference (CCI) among CM2M devices, interference to the Primary Users (PUs), and Maximum Aggregation Span (MAS) into consideration. Simulation results clearly show that the developed algorithm outperforms State of The Art (SOTA) algorithms in terms of network capacity and spectrum utilization. The developed algorithm can improve data rate of CM2M devices by at least 23% compared with the SOTA algorithms.

Furthermore, this thesis presents an optimal energy efficient spectrum management mechanism with multiple thresholds. The developed mechanism aims to reduce energy consumption in the system by optimizing spectrum sensing and channel switching, while at the same time decreasing the probability of collision and 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 by at least 35%.

In addition, the thesis considered an Energy-Efficient Channel Selecting (EECS) algorithm for CM2M communications in the healthcare system. The proposed algorithm aims to select the best available channels to improve CM2M communication quality and reduce energy consumption in the system overall. Accordingly, the algorithm reduced the probability of CM2M gateways switching between available channels and improved the energy efficiency by at least 45%. The efficiency of the algorithm is discussed and demonstrated through simulations.

Item Type:	Thesis (PhD)
Uncontrolled Keywords:	Machine-to-machine (M2M) communication; healthcare systems; cognitive machine-to-machine (CM2M) networks; algorithms
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	08 Apr 2019 09:44
URI:	http://gala.gre.ac.uk/id/eprint/23481

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