Abstract
Wireless technologies are pervasive to support ubiquitous healthcare applications. However, RF transmission in wireless technologies can lead to electromagnetic interference (EMI) on medical sensors under a healthcare scenario, and a high level of EMI may lead to a critical malfunction of medical sensors. In view of EMI to medical sensors, we propose a joint power and rate control algorithm under game theoretic framework to schedule data transmission at each of wireless sensors. The objective of such a game is to maximize the utility of each wireless user subject to the EMI constraints for medical sensors. We show that the proposed game has a unique Nash equilibrium and our joint power and rate control algorithm would converge to the Nash equilibrium. Numerical results illustrate that the proposed algorithm can achieve robust performance against the variations of mobile hospital environments.
Highlights
Recent developments in cellular networks (e.g., Universal Mobile Telecommunication System, UMTS Network) have enabled the innovative application of E-health anytime and anywhere
We propose a game of power and rate control in a mobile hospital environment and address a robust joint power and rate control algorithm, which is shown to converge to the Nash equilibrium of game
We addressed a noncooperative game to maximize the utility of wireless users by controlling their transmit power and rate under a mobile hospital scenario
Summary
Recent developments in cellular networks (e.g., Universal Mobile Telecommunication System, UMTS Network) have enabled the innovative application of E-health anytime and anywhere. The control of interference (e.g., through a joint power and rate control) is a critical issue to E-health and should be addressed under the environment of mobile hospital, which is defined as Internet of vehicles for E-health applications in this paper. We alternatively use the terms of mobile hospital and Internet of vehicles for E-health applications. Soomro and Cavalcanti in [3] address the possibilities of using wireless technologies in a medical environment. Zhou et al in [4] present the scheduling of heterogeneous data over body sensor networks. Rodrigues et al in [5] present the data visualization for body sensor networks
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