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Descripción
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| Despite the substantial amount of studies on sensor networks (Akyildiz et al., 2002; Nittel, 2009; Yick et al., 2008), they traditionally focused on the development of software, hardware, and configurations without paying enough attention to the environmental phenomenon of interest (Zerger et al., 2010). There is a similar situation concerning research on mobile WSNs, where most of the studies were carried out to improve the main WSN limitations such as network topology, connectivity and energy conservation (Wang et al., 2010; Younis and Akkaya, 2008). Some efforts were done in relation to improve spatial coverage of a study area through sensor mobility (Wang et al., 2009). They, however, addressed geometric issues of coverage without accounting for the environmental phenomenon itself. Therefore, research on mobile sensors as a mean to improve monitoring of environmental phenomena still remains largely unexplored. Addressing this requires the consideration of two main mobility aspects. First, WSNs are highly constrained, thus so are their sensor movements. Sensor movements may be constrained by the current state of a sensor network and the environment itself. For instance, because of low remaining energy level or because sensors are located in areas of difficult transit such as a highly dense forest. There are some attempts to address mobility constraints (Krause et al., 2009; Verma et al., 2006; Walkowski, 2008; Zou and Chakrabarty, 2007). They, however, addressed specific mobility constraints without providing the possibility to extend the approaches for further constraints. Therefore, what is missing is a general model for representing mobility constraints from both, the current status of WSNs and the geographical space where sensors are deployed. Second, another fundamental issue to consider is how sampling should be adapted to gain the maximum phenomenon knowledge with each single sensor movement. In other words: where mobile sensors should be moved. Previous studies addressed this issue by uniformly spreading sensors in a study area (Howard et al., 2002; Walvoort et al., 2010); increasing sensor density where events occur frequently (Butler and Rus, 2003) or where a higher monitoring accuracy is needed (Hefeeda and Bagheri, 2008); or moving sensors to minimize the mean kriging error variance (Brus and Heuvelink, 2007; Walkowski, 2008). They, however, adapted spatial sampling to geometric criteria while they were not affected by characteristics of the monitored phenomenon itself. As a result, methods to be used in mobile WSNs to adapt spatial sampling to characteristics of the monitored phenomenon are still needed. 3. OBJECTIVES This dissertation explores approaches of sensor mobility within a wireless sensor network to be used in environmental monitoring. To achieve this goal, four sub-objectives have been defined: 1- Explore the use of metadata to represent dynamic status of wireless sensor networks; 2- Develop a mobility constraint model to infer mobile sensor behaviour; 3- Develop a method to adapt spatial sampling using mobile, constrained sensors; 4- Extend the developed adaptive sampling method to monitor highly dynamic environmental phenomena. | |
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Internacional
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Si |
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ISBN
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9789461732279 |
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Tipo de Tesis
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Doctoral |
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Calificación
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Sobresaliente |
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Fecha
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11/05/2012 |