Optimistic Medium Access Control using Gait Anaysis in Body Sensor Networks

Body Sensor Networks (BSNs) have been used to provide continuous remote health monitoring and analysis of physiological parameter of patient. These devices can be attached to different parts of the human body to capture and wirelessly transmit health statistics in a wearable, non-invasive form factor. However, the small physical size of the sensor node used in BSNs can result in irregular transmission failures caused by body shadowing. Body shadowing can disrupt the radio communication due to body movement preventing the radio signal from passing through. In this work, an innovative approach based on body positioning prediction is applied to minimise the transmission failures and lower the power consumption. By analysing the impact of different leg positions and twiddling of the radio signal, an algorithm to adapt the periodicity of the transmission period for reliable transmission is proposed. The results from hardware experiment have shown that the proposed solution can achieve transmission success rate above 90% with reducing the energy consumption by about 50%.