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

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... isits 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. I. INTRODUCTION We present a data-centric routing algorithm called Spiral for short-term communication in wireless sensor networks. Spiral balances the cost of route discovery and route length, making it efficient for short-term communication of tens or hundreds of messages. This reduces the overall communication overhead, and hence the energy consumption, for short-term data transmission in wireless sensor networks. Spiral is intended for data-centric routing [1]-[3]. Unlike address-centric routing in which the source node searches for a route to the destination node, in data-centric routing the source searches for a particular data object stored on an unknown subset of nodes. Hence the routing problem is actually a query problem -the routing algorithm must search for the route to a node with the desired data, then the data can be transmitted along the discovered route. Due to the large data redundancy in sensor networks, data-centric routing has proven to be a good scheme for minimizing communication overhead and energy consumption. Data-centric routing normally has two phases: route discovery and communication. The route discovery phase determines the best route between the source node and a node with the desired data. During the communication phase the data are transmitted from the destination to the source. The relative lengths of these two phases influences the choice of routing algorithm. At the two extremes either the communication is long-lived, or it consists of a single response to the query (one-shot). For the former the route discovery overhead is less important, as it will be amortized over the long communication [1] . What is important is the resulting route length, as the subsequent communication must traverse the route and a sub-optimal route will have high overhead. For the latter extreme the route discovery cost is much more important than the discovered route length; it may not pay to find a short route if the communication is one-shot [3, 4] .