The demand for Wireless Body Sensor Networks (WBSNs) is rapidly increasing due to the revolution in wearable systems demonstrated by the penetration of on-the-body sensors in hospitals, sports medicine and general health-care practices. In WBSN, the body acts as a communication channel for the propagation of electromagnetic (EM) waves, where losses are mainly due to absorption of power in the tissue. This paper shows the effects of the dielectric properties of biological tissues in the signal strength and, for the first time, relates these effects with the human body composition. After a careful analysis of results, this work proposes a reactive algorithm for power transmission to alleviate the effect of body movement and body type. This policy achieves up to 40.8% energy savings in a realistic scenario with no performance overhead.
The growing interest in the extraction of useful knowledge from data with the aim of being beneficial for the data owner is giving rise to multiple data mining tools. Research community is specially aware of the importance of open source data mining software to ensure and ease the dissemination of novel data mining algorithms. The availability of these tools at no cost, and also the chance of better understanding of the approaches by examining their source code, provides the research community with an opportunity to tune and improve the algorithms. Documentation, updating, variety of algorithms, extensibility, and interoperability among others can be major issues to motivate users for opting for a specific open source data mining tool. The aim of this paper is to evaluate 19 open source data mining tools and to provide the research community with an extensive study based on a wide set of features that any tool should satisfy. The evaluation is carried out by following two methodologies. The first one is based on scores provided by experts to produce a subjective judgment of each tool. The second procedure performs an objective analysis about which features are satisfied by each tool. The ultimate aim of this work is to provide the research community with an extensive study on different features included in any data mining tool, either from a subjective and an objective point of view. Results reveal that RapidMiner, Konstanz Information Miner, and Waikato Environment for Knowledge Analysis are the tools that include higher percentage of these features. WIREs Data Mining Knowl Discov 2017, 7:e1204. doi: 10.1002/widm.1204 This article is categorized under: Application Areas > Data Mining Software Tools Technologies > Computer Architectures for Data Mining
In wireless body sensor network (WBSNs), the human body has an important effect on the performance of the communication due to the temporal variations caused and the attenuation and fluctuation of the path loss. This fact suggests that the transmission power must adapt to the current state of the link in a way that it ensures a balance between energy consumption and packet loss. In this paper, we validate our two transmission power level policies (reactive and predictive approaches) using the Castalia simulator. The integration of our experimental measurements in the simulator allows us to easily evaluate complex scenarios, avoiding the difficulties associated with a practical realization. Our results show that both schemes perform satisfactorily, providing overall energy savings of 24% and 22% for a case of study, as compared to the maximum transmission power mode.
Abstract-The human body has an important effect on the performance of on-body wireless communication systems. Given the dynamic and complex nature of the on-body channels, link quality estimation models are crucial in the design of mobility management protocols and power control protocols. In order to achieve a good estimation of link quality in WBSNs, we combine multiple body-related factors into a model that includes: the transmission power, the body position, the body shape and composition characteristics and the received signal strength indicator (RSSI) as an indicator of link quality. In this paper, we propose the Anfis Link Quality Estimator (A-LQE) that has been trained with RSSI values measured at different transmission power levels in a sample of 37 human subjects. Once the accuracy and reliability of our proposed model have been analysed, we apply the model to adapt the transmission power to the link characteristics for energy optimization. The obtained average energy savings reach the 26% in comparison with the maximum transmission power mode.
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