Practical Data-Centric Storage

Most data retrieval mechanisms in wireless sensor networks adopt a data-centric approach, in which data is identified directly by name rather than through the location of the node on which it is stored. Initial data-centric methods, such as directed diffusion and TinyDB/TAG, focused on the conveyance of data. One of the advantages of these algorithms is that they do not require pointto-point routing, which has proved to be difficult and costly to implement in wireless sensor networks, and instead require only the simpler and more robust treeconstruction primitives. Some recent data retrieval proposals have extended the data-centric paradigm to storage. Data-centric storage uses in-network placement of data to increase the efficiency of data retrieval in certain circumstances. Unfortunately, all such proposals have been based on pointto-point routing, and therefore have faced a significant deployment barrier. In this paper we hope to make data-centric storage more practical by removing the need for point-to-point routing. To that end, we propose pathDCS, an approach to data-centric storage that requires only standard tree construction algorithms, a primitive already available in many real-world deployments. We describe the design and implementation of pathDCS and evaluate its performance through both high-level and packet-level simulations, as well as through experiments on a sensor testbed.