A Sharing Data Model for Wireless Body Sensor Networks in Different Application Scenarios of Heterogeneous Platforms

Body sensor networks (BSNs) represent an emerging technology which has attracted much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, and social interaction. At present, the semantic expressions of BSNs data depend on the specific application scenarios, then have no unified standard. Such a lack of compatibility in the standard will lead to a subsequent bottleneck in data sharing of BSNs. This paper presents a model that addresses the issue, from low-layer data encapsulation to higher layer data storage, to top-layer application scenarios in BSNs. The physiological data from BSNs are encapsulated with some metadata, including BSNs context, sensor position, time and human health state in the model. The proposed model is cross-platform in terms of sharing physiological data. The results of experiments validate the effectiveness of data sharing in BSNs under the proposed model.