Data redistribution problem has become a key challenge in the data intensive sensor networks (DISNs), wherein large volume of sensory data are sensed and generated from some sensor nodes about their surrounding physical world. Due to the resource constraints of sensor nodes there is a need to redistribute (offload) the generated data to the nodes with free storage space. However, such data redistribution, if not managed well, could be a serious energy drain not only to the data generators' battery power but also to other sensor nodes involved in the redistribution process. We implement the data redistribution algorithms, which deal with redistribution of generated data and strive to minimize the energy consumption incurred by the data redistribution, while fully utilizing the storage capacity in the DISNs. We first show that our redistribution problem is equivalent to the balanced assignment problem, which can be solved with well-known Hungarian algorithm. However, the Hungarian algorithm gives O(N)3 time complexity where N is the total number of sensor nodes in the network and is the average storage capacity of each node. We design a fully distributed, highly scalable, and efficient data distributed mechanism, which is also adaptable to network dynamics such as dynamic data generating. We show both analytically and experimentally, our proposed distributed mechanism achieves best results. The goal of the thesis is to maximize the storage utilization of the sensor network and minimize the energy consumption required for the whole process of data redistribution. We focus on the in-network data redistribution where the data is redistributed between the highly utilized nodes and lightly utilized nodes.