Proactive Personalized Services in Large-Scale IoT-Based Healthcare Application

Abstract

With the IoT technology increasing and aging social have coming, personalized service assisted elder and patient living is a critical application in IoT-Based Healthcare application. However, the scale and complexity of personalized service is increasing with wildly applied to our life, which cause response time decrease and resource waste in large-scale IoT-Based Healthcare application. Therefore, it is necessary of studying on dealing with the large-scale and complexity of personalized services in large-scale IoT-Based Healthcare application. In this paper, we propose proactive personalized service leveraging Complex Event Processing (CEP) to deal with a large number and complexity of personalized services. Firstly, personalized service defined as complex event pattern that expresses in the form of Directed Acyclic Graph (DAG). Secondly, we propose a complex event pattern partitioning and clustering algorithms to optimize the processing of dealing with personalized services. Finally, we realize a prototype system based on proposed our approach named BCEPCare. Experiment result shows that BCEPCare is superior to the traditional ESPER in large-scale IoT-Based healthcare application.