A Framework for Accurate Drought Forecasting System Using Semantics-Based Data Integration Middleware

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 171)


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.


Middleware Internet of things Drought forecasting Semantic integration Ontology Interoperability Semantic technology 


  1. 1.
    Chester, D.: Natural Hazards by ea Bryant. Cambridge University Press, Cambridge (1991). price:40(hardback); 14.95 (paperback). isbn 0 521 37295 x (hardback); 0 521 37889 3 (paperback), (1993)Google Scholar
  2. 2.
    Mishra, A.K., Singh, V.P.: A review of drought concepts. J. Hydrol. 391(1), 202–216 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Peuquet, D.J., Duan, N.: An event-based spatiotemporal data model (estdm) for temporal analysis of geographical data. Int. J. Geogr. Inf. Syst. 9(1), 7–24 (1995)CrossRefGoogle Scholar
  4. 4.
    Mugabe, F., Mubaya, C., Nanja, D., Gondwe, P., Munodawafa, A., Mutswangwa, E., Chagonda, I., Masere, P., Dimes, J., Murewi, C.: Use of indigenous knowledge systems and scientific methods for climate forecasting in Southern Zambia and North Western Zimbabwe. Zimbabwe J. Technol. Sci. 1(1), 19–30 (2010)Google Scholar
  5. 5.
    Masinde, M., Bagula, A.: Itiki: bridge between african indigenous knowledge and modern science of drought prediction. Knowl. Manage. Dev. J. 7(3), 274–290 (2011)CrossRefGoogle Scholar
  6. 6.
    Sillitoe, P.: The development of indigenous knowledge: a new applied anthropology 1. Current anthropology 39(2), 223–252 (1998)CrossRefGoogle Scholar
  7. 7.
    Akanbi, A.K., Muthoni, M.: Towards semantic integration of heterogeneous sensor data with indigenous knowledge for drought forecasting. In: Proceedings of the Doctoral Symposium of the 16th International Middleware Conference. ACM (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  1. 1.Department of Information TechnologyCentral University of TechnologyBloemfonteinSouth Africa

Personalised recommendations