Intelligent contextual information collection in internet of things

As we are moving towards the internet of things (IoT), a significant growth of stationary and mobile sensing and computing IoT devices continuously generate enormous amounts of contextual information, e.g., environmental data. Contextual information collection, reasoning, and inference plays critical role in IoT. In this paper, we consider the contextual information collection and harvesting problem in which stationary sensing and computing devices (sources), which are incapable to communicate with each other either due to their long distance, or for energy efficiency, or spatially dispersed network, rely on mobile IoT devices (collectors) to ‘drain’ their acquired contextual information. (e.g., generating from IoT applications: smart cities, smart metering, and smart agriculture). At the contact instances with the collectors, sources have to decide whether to deliver the contextual information obtained so far or postpone their delivery for later hitting epochs in an effort to sense fresher (or more critical) contextual information. We rest on the principles of Optimal Stopping Theory and propose an intelligent context collection scheme in IoT environments. We show through simulations with synthetic and real mobility data the effectiveness of our scheme compared to other approaches