The essential task in nearly all applications of sensor networks is to extract relevant information about the sensed data and deliver it to a desired destination. The overall goal in the design of sensor networks is to execute this task with least consumption of network resources. In this regard, the relevant metrics of interest are 1) the latency (bandwidth) involved in network data acquisition; and 2) the energy-distortion (E-D) tradeoff: given some desired distortion level D, how much energy E does the sensor network consume in extracting and delivering relevant information up to distortion D at a (usually) distant destination. It is generally recognized that given sufficient prior knowledge about the sensed data, there exist distributed processing and communication schemes that have a very favorable E-D tradeoff in the sense that D ↘ 0 as n → ∞ while E grows at most sub-linearly with the number of nodes (n) in the network. However, it is not known whether such schemes exist when little or no prior knowledge about the sensed data is available. In this paper, we present a distributed matched-source channel communication scheme that naturally integrates the operations of processing and communications in a sensor network and is universal in the sense that it provides us with a consistent estimation scheme such that E grows sub-linearly with n even when little prior knowledge about the sensed data is assumed. This universality, however, comes at the price of increased latency (bandwidth) and a less favorable E-D tradeoff and we quantify this price by comparing our scheme to the case when sufficient prior information about the sensed data is available.