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On the energy efficiency of adaptive WBAN systems for mHealth services

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posted on 2024-11-15, 11:23 authored by Miftadi Sudjai, Le Chung TranLe Chung Tran, Farzad SafaeiFarzad Safaei
We recently proposed an adaptive Wireless Body Area Network (WBAN) system for mHealth service, which shows better bit error performance compared to the non-adaptive one. However, analysis and maximization of the energy efficiency as one of the key design considerations of these networks remain an open problem. Therefore, this paper investigates the energy efficiency of the proposed adaptive WBAN system by considering both the transmission power and the power consumed in the circuitry which consists of rate-independent and rate-dependent components. Since the distance between the hub and the wireless access point in WBAN is relatively short, beside the transmission power, the circuitry power also plays an important role. The adaptive WBAN is evaluated in two different multiple input multiple output configurations. It is found that the adaptive WBAN system substantially outperforms the non-adaptive one in terms of energy efficiency in both 2I1O and 2I2O configurations. Furthermore, the 2I2O adaptive WBAN system is 7.9 dB superior to the 2I1O in terms of energy efficiency. This is because the 2I2O configuration has a higher diversity order and a better array gain compared to the 2I1O configuration, thus the former has better capability to mitigate fading and improve the link reliability. This energy related performance coupled with the improved bit error rate performance suggests that the proposed adaptive WBAN scheme is an attractive physical layer option of a WBAN system for mHealth services. Thus, this scheme could provide not only better quality of services, but also energy saving to enhance the battery life of a WBAN system.

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Citation

M. Sudjai, L. C. Tran & F. Safaei, "On the energy efficiency of adaptive WBAN systems for mHealth services," EAI Endorsed Pervasive Health and Technology, vol. 3, (9) pp. 1-12, 2017.

Journal title

EAI Endorsed Transactions on Pervasive Health and Technology

Volume

3

Issue

9

Pagination

152391

Language

English

RIS ID

114986

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