In pervasive computing environments, wireless sensor networks play an important infrastructure role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time in a decentralised manner; however, sensed data is often faulty. We thus design a decentralised scheme for fault detection and classification in sensor data in which each sensor node does localised fault detection. A combination of neighbourhood voting and time series data analysis techniques are used to detect faults. We also study the comparative accuracy of both the union and the intersection of the two techniques. Then, detected faults are classified into known fault categories. An initial evaluation with SensorScope, an outdoor temperature dataset, confirms that our solution is able to detect and classify faulty readings into four fault types, namely, 1) random, 2) malfunction, 3) bias, and 4) drift with accuracy up to 95%. The results also show that, with the experimental dataset, the time series data analysis technique performs comparable well in most of the cases, whilst in some other cases the support from neighbourhood voting technique and histogram analysis helps our hybrid solution to successfully detects the faults of all types.