We study the problem of aggregate querying over sensor networks where the network topology is continuously evolving. We develop scalable data aggregation techniques that remain efficient and accurate even as nodes move, join or leave the network. We present a novel distributed algorithm called CountTorrent, that enables fast estimation of certain classes of aggregate queries such as COUNT and SUM. CountTorrent does not require a static routing infrastructure, is easily implemented in a

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... ed setting, and can be used to inform all network nodes of the aggregate query result, instead of just the query initiator as is done in traditional query aggregation schemes. We evaluate its robustness and accuracy compared to previous aggregation approaches through simulations of dynamic and mobile sensor network environments and experiments on micaz motes. We show that in networks where the nodes are stationary, CountTorrent can provide 100% accurate aggregate results even in the presence of lossy links. In mobile sensor networks where the nodes constantly move and hence the network topology changes continuously, CountTorrent provides a close (within 10 − 20%) estimate of the accurate aggregate query value to all nodes in the network at all times.