LAUSR

When sensor network applications are deployed in an inaccessible or inhospitable region, or under extreme weather, it is not viable to install a long-term base station in the field to collect data. The generated sensory data is therefore stored inside the network and waiting to be uploaded. When more data is generated than available storage spaces in the entire network can possibly store, and uploading opportunities have not arrived, data loss becomes inevitable. We refer to this problem as overall storage overflow in sensor networks. To solve this problem, we propose a unified framework and design two energy-efficient data replication algorithms to integrate data aggregation and data offloading. We refer to our approach as DAO-R. We give a sufficient condition under which DAO-R is solved optimally. Via extensive simulations, we show that DAO-R outperforms the existing research by around 30% in terms of energy consumption under different network parameters.