Data-Centric Routing in Sensor Networks using Biased Walk

We present spiral, a data-centric routing algorithm for short-term communication in unstructured sensor networks. Conventional data-centric routing algorithms are based on flooding or random walk. Flooding returns the shortest route but has a high search cost; random walk has a lower search cost but returns a sub-optimal route. Spiral offers a compromise between these two extremes - it has a lower search cost than flooding and returns better routes than random walk. Spiral is a biased walk that visits nodes near the source before more distant nodes. This results in a spiral-like search path that is not only more likely to find a closer copy of the desired data than random walk, but is also able to compute a shorter route because the network around the source is more thoroughly explored. Our experiments show that in a 500-node network with an average degree of 20 and two copies of every data object, for a short-term communication of 40 packets the total communication cost by spiral is only 72% of that by flooding, 81% of ERS, 74% of random walk, and 73% of DFS