Predictive Data Delivery to Mobile Users through
Mobility Learning in Wireless Sensor Networks
Abstract— We consider applications, such as indoor navigation, evacuation, or targeted advertising, where mobile users equipped with a smart-phone class device require access to sensor network data measured in their proximity. Speciﬁcally, we focus on efﬁcient communication protocols between static sensors and users with changing location. Our main contribution is to predict a set of possible future paths for each user and store data at sensor nodes that the user is likely to associate with. We use historical data of radio connectivity between users and static sensor nodes to predict the future user-node associations and propose a network optimization process, called data stashing, which uses the predictions < guess >to minimize network and energy overheads of packet transmissions.
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