A semantic sensor web framework for proactive environmental monitoring and control.
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Date
2017
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Abstract
Observing 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.
Description
Doctor of Philosophy in Computer Science, University of KwaZulu-Natal, Westville, 2017.