A Review on Human Body Communication: Signal Propagation Model, Communication Performance, and Experimental Issues

Human body communication (HBC), which uses the human body tissue as the transmission medium to transmit health informatics, serves as a promising physical layer solution for the body area network (BAN). The human centric nature of HBC offers an innovative method to transfer the healthcare data, whose transmission requires low interference and reliable data link. Therefore, the deployment of HBC system obtaining good communication performance is required. In this regard, a tutorial review on the important issues related to HBC data transmission such as signal propagation model, channel characteristics, communication performance, and experimental considerations is conducted. In this work, the development of HBC and its first attempts are firstly reviewed. Then a survey on the signal propagation models is introduced. Based on these models, the channel characteristics are summarized; the communication performance and selection of transmission parameters are also investigated. Moreover, the experimental issues, such as electrodes and grounding strategies, are also discussed. Finally, the recommended future studies are provided.

A Least Square-Based Self-Adaptive Localization Method for Wireless Sensor Networks

In the wireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.