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In wireless sensor networks, distributed consensus algorithms can be employed for distributed detection. Each sensor node can compute its log-likelihood ratio (LLR) from local observations for a target event and using an iterative distributed algorithm, the average of sensors' LLRs can be available to all the sensor nodes. While the average of sensors' LLRs allows each sensor node to make a final decision as a decision statistic for an overall detection problem with all sensors' LLRs, it may be desirable if all sensors' LLRs or local observations, which form a full information vector and denoted by x, could be available to each sensor for other purposes more than the detection of a target event In this paper, we show that each sensor can have not only the average of local observations, but also full information vector, x, (or its estimate) using a well-known iterative distributed algorithm. We extend the proposed approach to estimate x when x is sparse based on the notion of compressed sensing.