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SmartBAN Performance Evaluation for Diverse Applications

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Body Area Networks: Smart IoT and Big Data for Intelligent Health Management (BODYNETS 2019)

Abstract

Wireless Body Area Networks (WBANs) envision the realization of several applications which involve the physiological monitoring and/or feedback generations according to the monitored vital signs. These applications range from telehealth or telemedicine to sports and entertainment. SmartBAN provides the physical (PHY) and medium access control (MAC) layer specifications for a simplified and efficient execution of these applications. This paper provides an overview of the existing WBAN use-cases and categorizes them according to their data rate requirements. The SmartBAN performance is thoroughly investigated for the implementation of these diverse applications. For performance evaluation, packet reception rate (PRR), aggregated throughput and latency are taken as the primary quality of service (QoS) criteria. We assume two different channel models, namely static CM3B (S-CM3B) and realistic CM3B (R-CM3B), and different options for the slot durations to further comprehend the results. The simulation results indicate that smaller slot duration performs better in terms of PRR and latency while longer slot durations are more effective to support high data rate application throughput requirements.

This research was supported in part by the European Union’s Horizon 2020 Research and Innovation Program under Grant 668995, in part by the European Union Regional Development Fund through the framework of the Tallinn University of Technology Development Program 2016–2022, and in part by the Estonian Research Council under Grant PUT-PRG424.

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Correspondence to Rida Khan .

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Khan, R., Alam, M.M. (2019). SmartBAN Performance Evaluation for Diverse Applications. In: Mucchi, L., Hämäläinen, M., Jayousi, S., Morosi, S. (eds) Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-030-34833-5_19

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  • DOI: https://doi.org/10.1007/978-3-030-34833-5_19

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-34833-5

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