Technological advancement in Wireless Sensor Networks (WSN) has made itbecome an invaluable component of a reliable environmental monitoring system;they form the digital skin' through which to 'sense' and collect the context ofthe surroundings and provides information on the process leading to complexevents such as drought. However, these environmental properties are measured byvarious heterogeneous sensors of different modalities in distributed locationsmaking up the WSN, using different abstruse terms and vocabulary in most casesto denote the same observed property, causing data heterogeneity. Addingsemantics and understanding the relationships that exist between the observedproperties, and augmenting it with local indigenous knowledge is necessary foran accurate drought forecasting system. In this paper, we propose the frameworkfor the semantic representation of sensor data and integration with indigenousknowledge on drought using a middleware for an efficient drought forecastingsystem.
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