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A polling access control with exhaustive service in wireless body area networks for mobile healthcare using the sleeping schema

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Abstract

Wireless body area networks (WBANs) play an important role in human health monitoring for mobile healthcare. The improvement of service performance and low-power consumption are the two challenges for these medical WBANs, because those energy-limited wireless medical sensors must transmit the monitoring data to the personal server (PS) via intra-WBAN in time. Further, precise mathematical modeling combining with the sleeping state to measure this improvement theoretically is still lack. Therefore, a polling control with exhaustive service using the sleeping schema is proposed to address the problems in WBANs communication for smart health. Based on the managed access phase (MAP) specified in the IEEE 802.15.6 standard, it has been attempting to improve the energy efficiency through a self-managing sleeping schema for both the sensor nodes and the PS according to the load in intra-WBANs, in which the node will soon turn to sleep upon completion of transmitting all its current data packets. Additionally, by employing the embedded Markov chain and probability generating function, the proposed model is established, and the performance characteristics—the mean cyclic period and queue length at the polling moment—were accurately obtained to justify the service performance can be guaranteed. In addition, the expressions of the quantitative relationship among sleeping time, performance characteristics and network parameters were derived in closed form, which can easily evaluate the energy efficiency. Simulations were carried out to justify this model.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No.61463049, No.61761045).

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Correspondence to Zheng Guan.

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He, M., Guan, Z., Wu, Z. et al. A polling access control with exhaustive service in wireless body area networks for mobile healthcare using the sleeping schema. J Ambient Intell Human Comput 10, 3761–3774 (2019). https://doi.org/10.1007/s12652-019-01410-2

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