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Wireless sensor networks (WSNs) are built of cheap, resource constraint devices, capable to collect process and communicate data. WSN applications depend on the data they collect. In other words, the applications require the data to be available, even if some WSN nodes fail. The challenge is that nodes are prone to fail and todays WSNs do not provide highly reliable data storage. Thus, the quality of the service provided by the system, regarding the data handling, is one of the most important factors. Data replication increases the availability of the data and thus, the robustness and quality of the data storage. But the existence of several copies of data items in the WSN induces the data consistency to become of high importance in order to ensure proper behavior of the application. This work investigates the feasibility of data consistency models used in distributed shared memory in WSNs to provide more powerful distributed systems with reliable data exchange. As a starting point WSNs and consistency approaches are introduced. Based on those basics, the mechanisms needed to allow for data consistency are discussed as a theoretical framework for the prototypical implementation of a data consistency providing middleware, which was implemented as part of this work. The middleware adapts the mechanisms known from original memory consistency approaches to be usable in the sensor network area and proposes own, low cost mechanisms, as well. The latter are at least partially based on the idea that within the shared memory of WSNs information is the major concern and that by that the replica update rates can be tailored to the application. In order to allow for ease of use of the middleware the replication schemes and consistency mechanisms can be defined by the application engineer as a policy. The latter is transformed and injected into the middleware code by a pre-compiler, so that the application engineer no longer needs to implement replication and consistency mechanisms herself. The most appropriate memory consistency models are implemented and evaluated using the framework proposed in this thesis.