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Energy Efficient Data Maximization using Distributed Spatial Correlation Algorithm in Wireless Sensor Networks with Slepian Wolf Coding

“Energy efficient data maximization using distributed spatial correlation algorithm wireless sensor networks with Slepian Wolf coding”, is aimed at maximizing the samples using distribution correlation algorithm wherein it collects the raw data from the sensors and compress them before transmission. When the sensed data is compressed the number of transmissions decreases. The processed data is again compressed using Slepian Wolf coding providing energy efficiency due to reduced transmissions. In this project distributed spatial correlation algorithm is used to reduce the transmissions between the source and the sink node. It analyzes the characteristics of data samples by nodes. Spatial correlation between neighboring nodes is a method to compress the raw data and reduce the number of transmissions in WSN. Slepian wolf coding can achieve the compression rate as the optimal single encoder that has all correlated data streams as inputs. It can be used for multiple correlated data streams. The effect of this is cluster of nodes can exchange information