In the era of big data, sensor networks have been pervasively deployed, producing a large amount of data for various applications. However, because sensor networks are usually placed in hostile environments, managing the huge volume of data is a very challenging issue. In this study, we mainly focus on the data storage reliability problem in heterogeneous wireless sensor networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques. To minimize data delivery and data storage costs, we design an algorithm to jointly optimize data routing and storage node deployment. The problem can be formulated as a binary nonlinear combinatorial optimization problem, and due to its NP-hardness, designing approximation algorithms is highly nontrivial. By leveraging the Markov approximation framework, we elaborately design an efficient algorithm driven by a continuous-time Markov chain to schedule the deployment of the storage node and corresponding routing strategy. We also perform extensive simulations to verify the efficacy of our algorithm.