Prediction of Channel and Enhancement of System Energy in Wireless Body Area Network (WBAN) by Convolution Based Deep Learning Method

In this paper, we discussed the prediction of a channel and energy enhancement in wireless body area networks (WBANs). WBANs are becoming increasingly popular due to their low cost, ease of use and wide-area coverage. They are used mostly for monitoring and tracking of medical applications in addition to sensing, communication and localization in modern computing applications. The channel predictions used in wireless body area can be done either through deterministic methods or stochastic methods. The most widely used method for channel predicting in WBANs is deterministic models, such as ray-tracing and single-cell-based modeling. Stochastic methods use statistical measurements based on multipath fading environments. Furthermore, energy enhancements are key criteria for WBANs, especially in energy sensitive applications such as medical monitoring. To improve energy efficiency in WBANs, there are some techniques that can be used such as routing optimization, power management and resource scheduling. These techniques can lead to improved energy utilization and enhanced availability of WBANs. We have discussed the different strategies used to predict a channel and to enhance system energy in WBANs. We have also presented some recent developments related to the enhancement of energy in WBANs. We have also discussed some open issues related to the energy prediction and enhancement in WBANs. This review paper provides a comprehensive view of the field of channel and energy prediction and enhancement in WBANs.