Design of a Sensing Service Architecture for Internet of Things with Semantic Sensor Selection

The Internet of Things comprises a large number of "smart things" bringing in physical world data that help resolve the challenges (e.g., Environmental pollution) we face every day. With the prevalence of IoT, some platforms emerged (e.g., Xively) on which people are allowed to share or retrieve sensor data and deploy applications. It is, however, a challenge to search/select appropriate sensors among enormous sensors for a particular application in an efficient and effective way. This paper investigates context-aware sensor selection, identification of sensors characteristics, and the process of publishing services. We propose a sensing service architecture with a sensor search and selection method to efficiently select relevant sensors among a large set of available sensors. The context-dependent parameters for sensor selection proposed in this paper include user requirements and context properties of sensors (e.g., Accuracy, sensing range, residual energy). Moreover, the proposed architecture attempts to reduce excessive network traffic caused by frequent change of sensor states. We develop a prototype of the proposed architecture with semantic sensor selection which can be applied to many applications. The prototype can select proper sensors intelligently for users according to their requirements to develop their personalized applications. Our simulation results show that the proposed search/selection method can achieve longer network lifetime, as compared with traditional keyword-based search methods.