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dc.contributor.advisorMoodley, Deshendran.
dc.contributor.advisorRens, Gavin B.
dc.contributor.advisorAdewumi, Aderemi Oluyinka.
dc.creatorAdeleke, Jude Adekunle.
dc.date.accessioned2018-10-15T12:31:01Z
dc.date.available2018-10-15T12:31:01Z
dc.date.created2017
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10413/15647
dc.descriptionDoctor of Philosophy in Computer Science, University of KwaZulu-Natal, Westville, 2017.en_US
dc.description.abstractObserving and monitoring of the natural and built environments is crucial for main- taining and preserving human life. Environmental monitoring applications typically incorporate some sensor technology to continually observe specific features of inter- est in the physical environment and transmitting data emanating from these sensors to a computing system for analysis. Semantic Sensor Web technology supports se- mantic enrichment of sensor data and provides expressive analytic techniques for data fusion, situation detection and situation analysis. Despite the promising successes of the Semantic Sensor Web technology, current Semantic Sensor Web frameworks are typically focused at developing applications for detecting and reacting to situations detected from current or past observations. While these reactive applications provide a quick response to detected situations to minimize adverse effects, they are limited when it comes to anticipating future adverse situations and determining proactive control actions to prevent or mitigate these situations. Most current Semantic Sensor Web frameworks lack two essential mechanisms required to achieve proactive control, namely, mechanisms for antici- pating the future and coherent mechanisms for consistent decision processing and planning. Designing and developing proactive monitoring and control Semantic Sensor Web applications is challenging. It requires incorporating and integrating different tech- niques for supporting situation detection, situation prediction, decision making and planning in a coherent framework. This research proposes a coherent Semantic Sen- sor Web framework for proactive monitoring and control. It incorporates ontology to facilitate situation detection from streaming sensor observations, statistical ma- chine learning for situation prediction and Markov Decision Processes for decision making and planning. The efficacy and use of the framework is evaluated through the development of two different prototype applications. The first application is for proactive monitoring and control of indoor air quality to avoid poor air quality situations. The second is for proactive monitoring and control of electricity usage in blocks of residential houses to prevent strain on the national grid. These appli- cations show the effectiveness of the proposed framework for developing Semantic Sensor Web applications that proactively avert unwanted environmental situations before they occur.en_US
dc.language.isoen_ZAen_US
dc.subject.otherSensor web.en_US
dc.subject.otherEnvironmental monitoring.en_US
dc.subject.otherMarkov process.en_US
dc.subject.otherMachine learning.en_US
dc.titleA semantic sensor web framework for proactive environmental monitoring and control.en_US
dc.typeThesisen_US


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