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