A Link Quality Estimator for Power-Efficient Communication Over On-Body Channels

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.

[1]  Stephen L. Chiu,et al.  Selecting Input Variables for Fuzzy Models , 1996, J. Intell. Fuzzy Syst..

[2]  Sandeep K. S. Gupta,et al.  Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue , 2005, IEEE Transactions on Biomedical Engineering.

[3]  Hung T. Nguyen,et al.  A First Course in Fuzzy and Neural Control , 2002 .

[4]  Philip Levis,et al.  Four-Bit Wireless Link Estimation , 2007, HotNets.

[5]  Vijay Sivaraman,et al.  Algorithms for Transmission Power Control in Biomedical Wireless Sensor Networks , 2008, 2008 IEEE Asia-Pacific Services Computing Conference.

[6]  M. Hosoz,et al.  Performance prediction of a cooling tower using artificial neural network , 2007 .

[7]  T. M. Nazmy,et al.  Adaptive Neuro-Fuzzy Inference System for classification of ECG signals , 2010, 2010 The 7th International Conference on Informatics and Systems (INFOS).

[8]  Vijay Sivaraman,et al.  Transmission Power Control in Body Area Sensor Networks for Healthcare Monitoring , 2009, IEEE Journal on Selected Areas in Communications.

[9]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[10]  Fabio Di Franco,et al.  The effect of body shape and gender on wireless Body Area Network on-body channels , 2010, IEEE Middle East Conference on Antennas and Propagation (MECAP 2010).

[11]  Doo Seop Eom,et al.  RSSI/LQI-Based Transmission Power Control for Body Area Networks in Healthcare Environment , 2013, IEEE Journal of Biomedical and Health Informatics.

[12]  Edward J. Coyle,et al.  A Kalman Filter Based Link Quality Estimation Scheme for Wireless Sensor Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[13]  Carlos F. García-Hernández,et al.  Wireless Sensor Networks and Applications: a Survey , 2007 .

[14]  Ignacio Fernandez Anitzine,et al.  Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions , 2012 .

[15]  菅野 道夫,et al.  Industrial applications of fuzzy control , 1985 .

[16]  Pablo García Del Valle,et al.  Accurate Human Tissue Characterization for Energy-Efficient Wireless On-Body Communications , 2013, Sensors.

[17]  Tao Liu,et al.  Foresee (4C): Wireless link prediction using link features , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.