Optimal Stopping of the Context Collection Process in Mobile Sensor Networks

Contextual data collection is a major challenge in mobile sensor networks, in which sources generate quality-stamped context and mobile nodes (collectors) attempt to gather context of high quality. We deal with the context collection problem, in which collectors forage for high quality context and, then, deliver it to mobile context-aware applications. Collectors undergo a context collection process by exchanging contextual data with neighbouring collectors and/or sources in light of receiving context of better quality. The quality indicator of context normally decreases with time. Hence, the collectors cannot prolong such process forever, where the delivered context might be useless for the application. We propose a context collection scheme based on the optimal stopping theory (OST), which supports collectors with time-optimized context delivery decisions. We compare the performance of the proposed scheme with pre-existing OST-based context collection mechanism and quantify the benefits stemming for its adoption.