Knowledge of sensor network topology is useful for understanding the structure of the sensor network, and also important for resource management and redeployment. Additionally, it is a crucial component of sensor network tomography techniques. In this paper we propose a new algorithm, namely hamming distance and hop count based classification algorithm (HHC), to infer network topology by using end-to-end data in sensor network. Specifically, we consider the case of inferring sensor network topology during the aggregation of the data from a collection of sensor nodes to a sink node. The HHC algorithm identifies sensor network topology using hamming distance of the sequences on receipt/loss of data maintained in the sink node and incorporating the hop count available at each node. We implement the algorithms in a simulated network and validate the algorithm's performance in accuracy and efficiency.
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