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Spatial Reuse Superframe for High Throughput Cluster-Based WBAN with CSMA/CA
Pham Thanh Hiep

Since the elderly population is increasing all over the world, health care market keeps growing and there is a need for monitoring of health issues. Body area networks (BANs) consist of wireless sensors attached on or inside human body for monitoring vital health related problems, i.e. Electro Cardiogram (ECG), ElectroEncephalogram (EEG), Electronystagmogram (ENG) etc. Moreover, the quick development in entertainment devices, e.g. music/video players, games using vital data, wireless earphones and so on, requests high data rate communications and low energy consumption. In order to improve the energy-efficiency, cluster-based wireless sensor networks (WSN), included multiple hops cluster-based WSN have been analyzed. Since the network topology in IEEE802.15.6 is defined as one hop star plus one, the conventional cluster-based with CSMA/CA scheme is taken into considered. We focus on the system throughput and propose a spatial reuse superframe to increase the throughput. The performance of spatial reuse superframe is analyzed and compared to the conventional cluster-based scenario. The calculation result indicates that the spatial reuse superframe outperforms the conventional cluster-based when the access probability and/or the total number of sensors are high. There are optimal the number of clusters, the access probability and the total number of sensors that obtain the highest throughput. However, the number of spatial reuse superframes (k) that achieves the highest throughput depends on the system model. The optimization method of k is proposed to obtain the higher throughput and k increases when system parameters increase, excepted the payload. k is constant while the payload is varying. The proposed method can be applied to not only IEEE802.15.6 WBAN but also another WSN.

Keywords: Cluster-based WBAN, IEEE802.15.6 standard, optimal number of spatial reuse superframes, control on MAC layer, maximal throughput, number
of clusters, total number of sensors, access probability.

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