An SNN Ontology Based Environment Monitoring Method for Intelligent Irrigation System
- 27 Downloads
In order to realize high precision of environment parameters detection in irrigation applications, a sensor and sensor network (SSN) ontology based data fusion method is proposed. An SSN sub-ontology for soilstate monitoring is revised, which includes the sensing devices hierarchies and measurement properties selection according to the detection feature interests. As for sensor data processing, a tuning data method by data pool filtering and clustering is adopted, as well as a useful data fusion method for multi-sensor system. The testing results show that both the accuracy and efficiency of the proposed method are higher after related filtering and clustering process, which enables a thorough monitoring for intelligent irrigation systems and can be extended into environment monitoring and control applications.
Key wordssensor and sensor network (SSN) data fusion intelligent irrigation environment monitoring
CLC numberTP 391
Unable to display preview. Download preview PDF.
- TRäNKLER H, KANOUN O. Improvement of sensory information using multi-sensor and model-based sensor systems [C]//Proceedings of the IEEE Instrumentation and Measurement Technology Conference. Ottawa, Canada-IEEE, 2006: 2259–2263.Google Scholar
- VALVERDE J, ROSELLO V, MUJICA G, et al. Wireless sensor network for environmental monitoring: application in a coffee factory [J]. International Journal of Distributed Sensor Networks, 2012(3): 1–18.Google Scholar
- MAK NH, SEAH W K G. How long is the lifetime of a wireless sensor network? [C]// International Conference on Advanced Information Networking and Applications. Bradford, UK: IEEE, 2009: 763–770.Google Scholar
- TAYLOR K, LEIDINGER L. Ontology-driven complex event processing in heterogeneous sensor networks [C]//Proceedings of the 8th Extended Semantic Web Conference on the Semantic Web: Research and Applications. Heraklion, Greece: Springer-Verlag, 2011: 285–299.Google Scholar
- COMP TON, HENSON C, LEFORT L, et al. A survey of the semantic specification of sensors [C]//Proceedings of 2nd International Semantic Sensor Networks Workshop. Aachen, Germany: CEURWS. org, 2009, 17–32.Google Scholar
- STASCH C, JANOWICZ K, BRÖRING A, et al. A stimulus-centric algebraic approach to sensors and observations [C]//Proceedings of the 3rd International Conference on GeoSensor Networks. Oxford, UK: Springer-Verlag, 2009: 169–179.Google Scholar
- ZHOU J, The research of theory and key technology for information fusion in the Internet of Things [D]. Jilin: Jilin University, 2014 (in Chinese).Google Scholar
- COMPTON M. What now and where next for the W3C semantic sensor networks incubator group sensor ontology [C]//Proceedings of the 4th International Conference on Semantic Sensor Networks. Bonn, Germany: CEUR-WS.org, 2011, 839: 1–8.Google Scholar
- SHIGA M, TAKIGAWA I, MAMITSUKA H. A spectral clustering approach to optimally combining numerical vectors with a modular network [C]//Proceedings of the 13th International Conference on Knowledge Discovery and Data Mining. CA, USA: ACM, 2007: 647–656.Google Scholar
- ZHA H, HE X, DING C, et al. Spectral relaxation for K-means clustering [C]//Proceedings of the 14th International Conference on Neural Information. Vancouver, Canada: MIT Press, 2001: 1057–1064.Google Scholar