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Till date, sensor network research has assumed that the cost of transmitting a sensor reading over the network is much higher than the cost of sampling a sensor. However, this assumption is no longer always valid, due to availability of new generation sensor platform hardware, which utilizes industry standard mesh-networking protocols such as ZigBee on top of relatively high-speed, yet low-power wireless radios. In fact, we have experimentally verified that the energy consumed for acquiring a sample from a sensor can be significantly higher than the energy consumed for transmitting its reading over the network. Hence, new querying strategies need to be formulated, which optimize the order of sampling sensors across the network in such a manner that sensors with expensive acquisition costs are not sampled unless absolutely required. We propose distributed pull-push querying mechanisms, which optimize the query plan by adapting to variable costs of acquiring readings from different sensors across the network. The goal of these mechanisms is to minimize the energy consumption of nodes executing a query while ensuring that the latency of query response does not exceed user-specified bounds. To validate our approach, we also describe experimental results, which analyze the performance of various plan options in terms of energy consumption and latency under the effect of various parameters such as selectivity of data and number of sensors participating in the query.