Master of Science / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / Decentralized sensor networks are collections of individual local sensors that observe a common phenomenon, quantize their observations, and send this quantized information to a central processor (fusion center) which then makes a global decision about the phenomenon. Most of the existing literature in this field consider only the data fusion aspect of this problem, i.e., the statistical hypothesis testing and optimal combining of the information obtained by the local sensors. In this thesis, we look at both the data detection and the data fusion aspects of the decentralized sensor networks. By data detection, we refer to the communication problem of transmitting quantized information from the local sensors to the fusion center through a multiple access channel. This work first analyzes the data fusion problem in decentralized sensor network when the sensor observations are corrupted by additive white gaussian noise. We optimize both local decision rules and fusion rule for this case. After that, we consider same problem when the observations are corrupted by correlated gaussian noise. We propose a novel parallel genetic algorithm which simultaneously optimizes both the local decision and fusion rules and show that our algorithm matches the results from prior work with considerably less computational cost. We also demonstrate that, irrespective of the fusion rule, the system can provide equivalent performance with an appropriate choice of local decision rules. The second part of this work analyzes the data detection problem in distributed sensor networks. We characterize this problem as a multiple input multiple output (MIMO) system problem, where the local sensors represent the multiple input nodes and the fusion center(s) represent the output nodes. This set up, where the number of input nodes (sensors) is greater than the number of output nodes (fusion center(s)), is known as an overloaded array in MIMO terminology. We use a genetic algorithm to solve this overloaded array problem.