Distributed Source Coding: Theory and Applications
Imagine a dense sensor network consisting of many tiny sensors deployed for information gathering. Readings from neighboring sensors will often be highly correlated. This can be exploited to significantly reduce the amount of information that each sensor needs to send to a central point, thus reducing power consumption and prolonging the life of the nodes and the network. Communication among sensors is often not feasible as it increases the complexity of the sensors that in turn leads to additional cost and power consumption. How then is it possible to exploit statistical dependency of the readings in different sensor nodes without information exchange among sensors? The answer lies in distributed source coding.