As we are moving towards the Internet of Things (IoT), a significant growth of stationary and mobile sensing & 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 & 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. Keywords Contextual information collection • Internet of Things • timeoptimized stochastic information delivery • optimal stopping theory. 1 Introduction An aspect in context-aware mobile computing is the collection of contextual information (context) from certain sources in an Internet of Things (IoT) environment. We study the collection of context from stationary sources through