CAs-Based QoS Scheme for Remote Health Monitoring

CAs-Based QoS Scheme for Remote Health Monitoring

B. Sathish Babu, K. Bhargavi, Pallapa Venkatarm
Copyright: © 2013 |Pages: 24
DOI: 10.4018/ijats.2013100103
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Abstract

Integrated Wireless Networks (IWN) is an important area of today's research because of its application in comprehensive services like battlefield surveillance, traffic avoidance and control systems, mobile health monitoring, biological detection and agricultural fields, structural health monitoring, Computer-assisted rehabilitation and therapy, tele-robotic surgery, etc. IWNs are employed to collect voluminous data from different types of networks and correlate them to provide critical medical services with high reliability and efficiency. This paper proposes an architecture which uses Cognitive Agents (CAs) along with Behavior-Observation-Belief (BOB) model in the area of Remote Health Monitoring (RHM), in order to provide better QoS by reducing the latency. The analytical modeling and simulation of the proposed system shows that, there is a considerable reduction in latency compared to the existing Multi-Agent based m-Health Care system.
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1. Introduction

IWN is a network of networks achieved after convergence of different wireless networks and communication technologies, thus continuously providing the best services to the users anytime, anywhere, any location, and so on (E. Yanmaz, O. K. Tonguz, 2004; Sumi Helal et al., 2000). The integration of different technologies is a complex task, which involves issues at all the layers of a protocol stack (Cavalcanti et al., 2005; Yen-Cheng Lai et al., 2009; Pablo Vidales et al., 2004). However IWNs are being extensively used to provide most comprehensive services (J.M. Corchado, 2010; Upkar Varshney, 2007; Yasir Faheem et al., 2010), such as: Personal health-Care monitoring, Monitoring of soldiers in the battlefield, Emergency medical care, Home monitoring, Computer-assisted rehabilitation and therapy, Social network- ing of relatives and peers of chronically ill patients, Wild animal tracking, Wild fire monitoring, Vehicle tracking, storage of potentially relevant activities (thefts, car accidents, etc.), emergency and crisis intervention, Child tracking, elderly person tracking, tele-robotic surgery, Mobile social networking, Location based services, Mobile E-Commerce, Mobile-enabled health care, etc.

1.1. RHM

RHM is a technology to enable monitoring of patients outside of conventional clinical settings i.e., it delivers care right to home. Some of the benefits of RHM are: improved access to information, reduced health care cost, improved quality of care, shared care for diagnosis and treatment, improved access in primary and secondary care, etc. The increased availability, miniaturization, performance, enhanced data rates of IWN will accelerate the development of advanced RHM applications (Ibrahiem Mahmoud Mohamed El Emary, 2010; Seok Su Kim et al., 2010). This will have a powerful impact on the existing health-care services and will reshape some of the existing health-care mechanisms (Myung kyung Suh et al., 2011; H. S. Ng et al., 2006; Devan Rehunathan et al., 2011). For example: Smart intelligent sensors in chronic patient’s home, will allow communi- cation with a personal server in complete mobility, which provides global connectivity to doctor in remote medical server using a wireless network, who in turn specify the medical prescriptions; Physicians can access medical records (eg., X-ray, MRI s, and so on) on a moving vehicle and establish a video conference so that a remotely-based surgeon could provide on screened assistance; Paramedical assistance could be provided to a victim of traffic accident in a remote location; Digital Information Review (DIR) facilitate access and analysis of diagnostic imaging files and other electronic patient information; Audio sound (eg., heart beat, lung sound, voice, and so on.) of a patient can be recorded and transmitted to remote doctor for analysis by which doctor can recognize minute problems like heart murmur and skipping of heart beats and so on; Moni- toring and studying the behavior of elderly people as a means to identify the causes of illness that affect them such as dementia and so on.

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