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Traffic flows for future WBAN eHealth applications

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posted on 28.03.2022, 18:22 by Justin Bajar
eHealth is an important field that will become widely used as a component to lower health costs and provide coverage to as much of the population as possible. The many systems provide monitoring with classifications of events such as Activity of Daily Living and/or fall detection through the use of wireless networks and data communications. much work has been done in evaluating the performance of those systems under a single network. This paper aims to fill some gaps in the field by providing traffic characteristics of a system that operates over two networks (WBAN & WLAN) under different traffic conditions. AMATLAB simulation was developed which simulates and eHealth system that provides monitoring and classification functions that observes the effects that scaling the number of sensor nodes has on the system. The simulation simulates n number of sensor nodes communicating over a WBAN using CSMA/CA to the BNC where data is forwarded to a local server over a saturated WLAN using CSMA/CA. The results show that the WBAN is capable of supporting a 50 node network with room for more. However, when examining the results over the whole system, from sensor to server, a bottleneck starts to appear early on with 10 sensors at the BNC despite WLAN performing better than WBAN in terms of data rates. This paper attempts to explain the reasoning behind these unexpected results and highlights possible areas of improvement and future work.


Table of Contents

1. Introduction -- 2. Background and related work -- 3. eHeakth system simulation -- 4. Results and discussion -- 5. Conclusion and future work -- 6. Abbreviations -- Appendices -- Bibliography.


Bibliography: page 57 Empirical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis bachelor honours


BSc (Hons), Macquarie University, Faculty of Science and Engineering, School of Engineering

Department, Centre or School

School of Engineering

Year of Award


Principal Supervisor

Rein Vesilo


Copyright Justin Bajar 2016. Copyright disclaimer: http://mq.edu.au/library/copyright




1 online resource (xiii, 57 pages diagrams, graphs, tables)

Former Identifiers

mq:70313 http://hdl.handle.net/1959.14/1262451

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