Divisão de Observação da Terra e Geoinformática

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group_2_geosensors [2009/03/19 18:21] – inpeifgigroup_2_geosensors [2009/03/19 20:44] (atual) – inpeifgi
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 === Our Proposal: === === Our Proposal: ===
  
-A framework which allows to pick sensors, tools, statistical methods, standards, and models to support decisions and understanding in geospatial applications at multiple scales.+The understanding of complex environmental phenomena, such as deforestation and epidemics, requires observation at multiple scales. This scale dependency is not handled well by today's rather technical sensor definitions. For instance, to understand the impact of deforestation on the local fauna, it is necessary to track the path of individuals as well as the path of populations within a biotope. Movement patterns of individuals reveal information about change in territory and foraging, while the changed behavior of one population impacts the behavior of others. At the scale of the animal population, a sensor network should produce a single trajectory based on the tracks of the individual animals. Current definitions of sensors, sensor systems, and sensor networks are too technical to capture these abstractions of observations. For this reason, a framework which allows to pick sensors, tools, statistical methods, standards and models will be developed to support decisions and understanding in dynamic geospatial applications at multiple scales. The basis for the framework will be an algebraic specification for sensors at different scales. Based on this algebraic specification, different meaningful abstraction levels of sensor data will be identified. To enable the retrieval and composition in or between the different abstraction levels, an ontology of processes and data sources will be elaborated. Additionally, statistical methods supporting the conversion between the different abstraction levels and the assessment of uncertainty will be developed. 
 Practical outcome: cookbook. Practical outcome: cookbook.
  
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 Development of suitable tools for different sensor abstraction levels.  Development of suitable tools for different sensor abstraction levels. 
-Mobility measure: inpe --> ifgi. 
  
-This Master theses aims at the development of tools for the different levels of abstractions which can be applied in sensor network scenarios. These levels include the Sensor, Sensor System, Sensor Network, Sensor Web and Application level. The tools are supposed to facilitate the deployment of sensor networks, ease the management and administration or improve the usage of sensors and their gathered data. The work includes the analysis of an existing sensor network scenario and the utilization of the developed tools in the scenario. +This Master thesis aims at the development of tools for the different levels of abstractions which can be applied in sensor network scenarios. These levels include the Sensor, Sensor System, Sensor Network, Sensor Web and Application level. The tools are supposed to facilitate the deployment of sensor networks, ease the management and administration or improve the usage of sensors and their gathered data. The work includes the analysis of an existing sensor network scenario and the utilization of the developed tools in the scenario.
-This Master theses involves the opportunity to participate in a exchange program with the Institute for Geoinformatics (IfGI) at the University of Münster (Germany).+
  
 +The thesis involves the opportunity to participate in an exchange program with INPE. 
  
 ==3. Master== ==3. Master==
  
 At which abstraction level do we need to introduce objects? can we avoid them? At which abstraction level do we need to introduce objects? can we avoid them?
 +
 +The hypothesis of this work is that objects (like forests or water bodies) are not needed for most change models and can be substituted by observations, surfaces, substances, and media. Avoiding objects as long as possible in the “food chain of sensors” has the advantage of reducing the semantic heterogeneity of models, as object classes vary across information communities. The MSc student will study at least two change questions (e.g., deforestation, glacier melt), work out the chain of sensor abstractions leading to answers, and show at what level object notions need to be introduced, if any. 
  
 ==4. Ph.D.== ==4. Ph.D.==
  
 Ontology (SWRL) of processes and data sources for retrieval and composition in the framework (see proposal) Ontology (SWRL) of processes and data sources for retrieval and composition in the framework (see proposal)
 +
 +Selecting sensors manually to observe a complex process is a difficult task. Which sensors should be used? Which temporal resolution should be chosen? Where to place the sensors? Instead deciding manually, one could define an ontology of processes. Such ontology would describe processes in terms of how their are observed. Picking a process from this ontology is like selecting a cookbook for a specific recipe. Besides providing the necessary information on which sensors to chose and how to configure them (the composition step), the ontology would also support retrieval of sensors (the retrieval step) by aligning them to the processes observable by them.
  
 ==5. Ph.D.== ==5. Ph.D.==
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 [[http://etherpad.com/epAysM9MZU|Link to document]] [[http://etherpad.com/epAysM9MZU|Link to document]]
  
-==paper== +=== First paper === 
- An algebraic approach to the composition of sensors+==An algebraic approach to the composition of sensors==
    
-* Abstract+**Abstract**
  
 The understanding of complex environmental phenomena, such as deforestation and epidemics, requires observation at multiple scales. This scale dependency is not handled well by today’s rather technical sensor definitions. For instance, to understand the impact of deforestation on the local fauna, it is necessary to track the path of individuals as well as the path of populations within a biotope. Movement patterns of individuals reveal information about change in territory and foraging, while the changed behavior of one population impacts the behavior of others. At the scale of the population, a sensor network should produce a single trajectory based on the tracks of the individual animals. Current definitions of sensors, sensor systems, and sensor networks are too technical to capture these abstractions of observations. For example, the definition of geosensor networks as “distributed ad-hoc wireless networks of sensor-enabled miniature computing platforms that monitors phenomena in geographic space” (Nittel et al., 2004) does not admit animals as sensors and cannot relate the phenomena to those observed at other scales. These definitions also exclude human sensors which are the key to volunteered geographic information. We propose definitions of sensors as information sources at multiple scales, relating physical stimuli to symbol systems. An algebraic formalization shows the aggregations, compositions, and generalizations. It also serves as a basis for defining consistent application programming interfaces to sense the environment at multiple scales of observations and with different devices.  The understanding of complex environmental phenomena, such as deforestation and epidemics, requires observation at multiple scales. This scale dependency is not handled well by today’s rather technical sensor definitions. For instance, to understand the impact of deforestation on the local fauna, it is necessary to track the path of individuals as well as the path of populations within a biotope. Movement patterns of individuals reveal information about change in territory and foraging, while the changed behavior of one population impacts the behavior of others. At the scale of the population, a sensor network should produce a single trajectory based on the tracks of the individual animals. Current definitions of sensors, sensor systems, and sensor networks are too technical to capture these abstractions of observations. For example, the definition of geosensor networks as “distributed ad-hoc wireless networks of sensor-enabled miniature computing platforms that monitors phenomena in geographic space” (Nittel et al., 2004) does not admit animals as sensors and cannot relate the phenomena to those observed at other scales. These definitions also exclude human sensors which are the key to volunteered geographic information. We propose definitions of sensors as information sources at multiple scales, relating physical stimuli to symbol systems. An algebraic formalization shows the aggregations, compositions, and generalizations. It also serves as a basis for defining consistent application programming interfaces to sense the environment at multiple scales of observations and with different devices. 
  
  
-* Introduction+**Introduction**
  
-* Related Work+**Related Work**
  
 Current sensor models and definitions are designed from a technical perspective. In the engineering community, sensors are defined as devices that produce analog signals based on the observed phenomenon. These signals are converted to digital signals by analog-to-digital converters (ADCs). Sensor networks comprise a large number of sensor nodes "that are densely deployed either inside the phenomenon or very close to it" (Akyildiz et al., 2002). From the viewpoint of the Open Geospatial Consortium (OGC)(FOODNOTE: The Open Geospatial Consortium ) a sensor is "a transducer which converts a physical phenomenon into a digital data representation" (Havens et al 2007).  Nittel&Stefanidis (2005) introduced the term Geosensor Network by defining a Geosensor Network as a distributed ad-hoc wireless network of sensor-enabled miniature computing platforms that monitors phenomena in geographic space.  Current sensor models and definitions are designed from a technical perspective. In the engineering community, sensors are defined as devices that produce analog signals based on the observed phenomenon. These signals are converted to digital signals by analog-to-digital converters (ADCs). Sensor networks comprise a large number of sensor nodes "that are densely deployed either inside the phenomenon or very close to it" (Akyildiz et al., 2002). From the viewpoint of the Open Geospatial Consortium (OGC)(FOODNOTE: The Open Geospatial Consortium ) a sensor is "a transducer which converts a physical phenomenon into a digital data representation" (Havens et al 2007).  Nittel&Stefanidis (2005) introduced the term Geosensor Network by defining a Geosensor Network as a distributed ad-hoc wireless network of sensor-enabled miniature computing platforms that monitors phenomena in geographic space. 
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 Research on the Semantic Sensor Web (Sheth et al., 2008) investigates the role of semantic annotation, ontologies, and reasoning to improve discovery on the Sensor Web. It combines OGC's vision of a web of sensors with the reasoning capabilities of the semantic Web. Besides discovery, a semantic layer would improve interoperability between sensor networks and help to make sensors situation aware. An ontological analysis of the OGC standards on observation and measurements has been done by Probst (2006). (TODO ask anu for input) However, the integration of semantics into sensor networks and sensor applications is still a challeging research task and a thoroughly defined model for sensors from an information perspective is currently missing. Research on the Semantic Sensor Web (Sheth et al., 2008) investigates the role of semantic annotation, ontologies, and reasoning to improve discovery on the Sensor Web. It combines OGC's vision of a web of sensors with the reasoning capabilities of the semantic Web. Besides discovery, a semantic layer would improve interoperability between sensor networks and help to make sensors situation aware. An ontological analysis of the OGC standards on observation and measurements has been done by Probst (2006). (TODO ask anu for input) However, the integration of semantics into sensor networks and sensor applications is still a challeging research task and a thoroughly defined model for sensors from an information perspective is currently missing.
  
-* Use Case+**Use Case**
  
 Main Use Case: Main Use Case:
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 We assume that the example use case is similarly designed as the existing ZebraNet project (Zhang et al., 2004). Each animal of the monitored population carries a small sensor platform consisting of a global positionig system (GPS) and a wireless transceiver to establish communication between the platforms. The animals are considered as the nodes of the sensor network and propagate gathered data from platform to platform to finally forward it to a mobile base station which accompanies the population. We assume that the example use case is similarly designed as the existing ZebraNet project (Zhang et al., 2004). Each animal of the monitored population carries a small sensor platform consisting of a global positionig system (GPS) and a wireless transceiver to establish communication between the platforms. The animals are considered as the nodes of the sensor network and propagate gathered data from platform to platform to finally forward it to a mobile base station which accompanies the population.
 Besides positioning sensors also other sensor types may be attached to single animals. These additional sensors could measure variables like heart frequency or blood pressure. But also environmental data, e.g. temperature or luminosity, could be gathered sensors attached to the animals. Besides positioning sensors also other sensor types may be attached to single animals. These additional sensors could measure variables like heart frequency or blood pressure. But also environmental data, e.g. temperature or luminosity, could be gathered sensors attached to the animals.
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- 
  
 ZebraNet Link: ZebraNet Link:
 [1] http://www.princeton.edu/%7Emrm/zebranet.html [1] http://www.princeton.edu/%7Emrm/zebranet.html
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- 
  
 Example: Step counter for joggers, blood pressure sensor  Example: Step counter for joggers, blood pressure sensor 
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-* Definitions (find a better name; combine with axiomatization)+**Definitions** (find a better name; combine with axiomatization)
  
 In this section... In this section...
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 class () => GEOSENSORNETWORK class () => GEOSENSORNETWORK
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 +**Algebra**
  
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- *Algebra 
- 
- 
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-* Conclusions and Further Work 
  
  
 +**Conclusions and Further Work**
  
  
-* References+**References**
  
 Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E.: Wireless Sensor Networks: a Survey. Computer Networks, vol. 38, 393–422 (2002). Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E.: Wireless Sensor Networks: a Survey. Computer Networks, vol. 38, 393–422 (2002).
group_2_geosensors.1237486903.txt.gz · Última modificação: 2009/03/19 18:21 por inpeifgi