Abstract
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 and proposes a context-aware search/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 preferences, accuracy, sensing range, power consumption, etc. 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 and demonstrate its applicability 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 lower power consumption, as compared with traditional text-based search schemes, which further extends the lifetime of sensor networks.