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Distributed Semantic Sensor Networks How to Use Semantics and Knowledge Distribution to Integrate Sensor Data of Disparate Data Sources

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Distributed Semantic Sensor Networks How to Use Semantics and Knowledge Distribution to Integrate Sensor Data of Disparate Data Sources 2007 Distributed Semantic Sensor Networks How to use semantics and knowledge distribution to integrate sensor data of disparate data sources. In this research project we focused on sensor networks and how semantic web technologies and knowledge sharing can make integration of distributed sensor data possible. To achieve this we have first identified the main challenges to address to allow for data integration. A distributed semantic sensor network framework is proposed that uses semantic web techniques and knowledge sharing to address these challenges. We propose that knowledge sharing in a sensor network is a key aspect of data integration in a dynamic environment, since it allows the network to handle changes in the environment. This project is innovative in that it proposes new ways to handle semantic integration in a distributed environment, by using question federation and data conversion on semantically annotated data and questions. It contributes to current research in that our framework enables data integration of distributed sensor data. Masters Student M.J. van der Veen, Department of Artificial Intelligence, Rijksuniversiteit Groningen, Groningen Supervisors Bart Verheij, Department of Artificial Intelligence, Rijksuniversiteit Groningen Arnoud de Jong, TNO ICT & Telecommunicatie, Groningen 1 People A masters student at the Department of Artificial Intelligence at the Rijksuniversiteit Groningen in The Netherlands. This thesis is the final work in his masters studies. His main interests besides writing this thesis is doing sports, coaching, travelling and making music . Masters Student: Maarten van der Veen A tenured lecturer/researcher (in Dutch: universitair docent) at the University of Groningen, Department of Artificial Intelligence and a member of the ALICE institute. He participates in the Multi-agent systems research program. His research areas are artificial intelligence, logic and law, with emphasis on defeasible argumentation and legal reasoning. A recently added direction of research is agent-based social simulation. Supervisor: Bart Verheij An innovator at TNO ICT & Telecommunications. He specializes in location based services, semantics and data visualization. Supervisor: Arnoud de Jong 2 Table of Contents 1 Introduction ..................................................................................................................................... 4 1.1 Problem Relevance .................................................................................................................. 4 1.2 Goal ......................................................................................................................................... 7 1.3 Challenges ............................................................................................................................... 7 1.4 Related Work ........................................................................................................................... 8 1.5 Research Method ..................................................................................................................... 9 1.6 Thesis Outline.......................................................................................................................... 9 2 Research Background .................................................................................................................... 11 2.1 Sensor Networks.................................................................................................................... 11 2.2 Semantic Web Technologies ................................................................................................. 15 2.3 Multi Agent Systems ............................................................................................................. 25 3 Integration of distributed knowledge ............................................................................................. 29 3.1 Question Federation .............................................................................................................. 29 3.2 Data Conversion .................................................................................................................... 38 3.3 Knowledge sharing ................................................................................................................ 41 4 Framework Design ........................................................................................................................ 43 4.1 Requirements ......................................................................................................................... 43 4.2 Design .................................................................................................................................... 47 5 Case study ..................................................................................................................................... 60 5.1 Evaluation .............................................................................................................................. 71 6 Discussion ..................................................................................................................................... 73 6.1 Possibilities and limitations ................................................................................................... 73 6.2 Relation to related research ................................................................................................... 77 7 Conclusion ..................................................................................................................................... 81 7.1 Contributions ......................................................................................................................... 82 Appendix A Query splitting algorithm .......................................................................................... 84 Appendix B Example knowledge base and ontologies .................................................................. 86 Appendix C Full results of questions ............................................................................................. 89 Appendix D Implementation: Network endpoint ........................................................................... 91 8 Bibliography .................................................................................................................................. 92 3 1 Introduction Take a look at our world from an information system perspective and you will see a lot of information that once only existed in our heads, but has now been made explicit in digital environments. With the arrival of the world wide web [1], we started to link different information sources on a wide scale through a text interface. With the upcoming of the Semantic Web [2], the possibilities of linking data on the web will be taken to a higher level. Semantics allow machines to interpret data and to connect different data instances by adding semantic metadata to a data instance. For example, one could specify properties of objects, to enable machines to compare objects based on these properties. We can describe an apple as an instance which has a round shape, a colour and which is edible. An interesting thought is whether it is possible to link our physical world much in the same way as the information sources on the world wide web are linked. Linking sensors in a network results in a sensing web, which has many new capabilities compared to the normal web. An analogue is the addition of sensors to a normal computer. The result is a robot (without actuators), which is aware of its environment and has many more capabilities than a normal computer. To achieve this, we need to be able to measure the physical world and make these measurements accessible. Physical phenomena can be measured by using sensors. Imagine a network of these sensors. Such a network should allow us to pose questions about the real world phenomena which are measured by the individual sensors. To give a simple example of the possibilities that arise, consider an office building with in every room a temperature sensor. By creating a network of sensors, we could ask the question: “In what rooms is the temperature over 25 degrees Celsius?”. Or maybe: “alert me when the average temperature in the office building gets below 10 degrees Celsius”. More complex questions can be thought of when there are sensors of different types available, for example, pressure sensors and noise sensors. This is interesting because combining measurements of different sensors gives us insight into the interaction between the phenomena in the environment, measured by these sensors. Since sensor networks are situated in the real world, one should think about how to handle continuous operation in a dynamic environment. Environmental events that change the meaning of sensor measurements but also human intervention in the network are reasons why a network of sensors could become erroneous in the future, making it unusable. By introducing knowledge sharing in the sensor network domain new possibilities arise to handle continuous operation in a dynamic environment. Knowledge sharing is already a research topic in Peer-to-Peer networks [3, 4] and in multi-agent systems [5] (See Section 3.3 on knowledge sharing). In the sensor network domain knowledge sharing enables sensors to exchange knowledge about sensor measurements and their properties, about changes in the environment or human inflicted changes. Exchanging knowledge
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