Real-time query processing optimization for cloud-based wireless body area networks

Wireless body area networks (WBANs) have received a lot of attention from both academia and industry due to the increasing need of ubiquitous computing for eHealth applications, the continuous advances in miniaturization of electronic devices, and the ultra-low-power wireless technologies. In these networks, various sensors are attached either on clothes, on human body or even implanted under the skin for real-time health monitoring of patients in order to improve their independent daily lives. The energy constraints of sensors, the vital and large amount of data collected by WBAN nodes require powerful and secure storage, and a query processing mechanism that takes into account both real-time and energy constraints. This paper addresses these challenges and proposes a new architecture that combines a cloud-based WBANs with statistical modeling techniques in order to provide a secure storage infrastructure and optimize the real-time user query processing in terms of energy minimization and query latency. Such statistical model provides good approximate answers to queries with a given probabilistic confidence. Furthermore, the combination of the model with the cloud-based WBAN allows performing a query processing algorithm that uses the error tolerance and the probabilistic confidence interval as query execution criterions. The performance analysis and the experiments based on both real and synthetic data sets demonstrate that the new architecture and its underlying proposed algorithm optimize the real-time query processing to achieve minimal energy consumption and query latency, and provide secure and powerful storage infrastructure.