Sensor node faults are a serious threat to wireless sensor networks. They can cause node crashes or lead to the transmission of corrupted data. Especially the latter endangers the quality of subsequent data analyses. Most related fault detection approaches consider the sensor nodes as black boxes. They neglect vital information available on the node level. Consequently, most of these approaches can not distinguish between (i) irregular but correctly sensed data events and (ii) data corruption caused by soft faults. In this thesis, we present a fault detection approach that integrates node-level diagnostics with the characteristics of the sensor data. We utilize this node-level diagnostic information to present our fault detection approach, which is inspired by the functioning of dendritic cells in the human immune system. We used a tripartite experiment setup consisting of simulations, a lab setup, and a practical sensor network testbed to evaluate the correctness and efficiency of the developed approach. The results show that the approach offers a comparably high fault detection rate in combination with a negligibly low false alarm rate. Moreover, it can reliably differentiate between correct data events and fault-induced data anomalies. At the same time, it consumes a reasonably small overhead of resources, especially concerning the sensor node energy. Also, the approach is generally applicable and minimizes the need for parameter adjustment and optimization.
