Wireless sensor networks consist of unreliable and energy-constrained sensors connecting to each other wirelessly. As measured data may be lost due to sensor failures, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challenge in sensor networks, without the use of centralized servers. To cope with node failures, while providing convenient access to measured data, we propose geometric random linear codes, to encode data in a hierarchical fashion in geographic regions with different sizes, such that data are easy to access, if the original sensors producing the data are alive. Otherwise, data are persistently available elsewhere in the network. Although our coding scheme is simple, we have shown that it enjoys the same low encoding cost as sparse random linear codes, while dramatically decreasing the decoding cost. We present extensive analytical and experimental results to show the effectiveness of geometric random linear codes.