## 5 An Integrated Inductive-Deductive Framework for Data Mapping  
The real worth of the enormous amount of data generated by the WSN lies not only in easy data access, but also in the additional possibility of extracting as well as presenting the implicit information contained in the data. Recently, such networks are being deployed for an increasingly diverse set of applications each with different characteristics and environmental constraints. As a consequence, scientists from different research fields have begun to realise the importance of identifying and understanding the characteristics and special deployment needs of different application domains. In many WSN deployments, the network owners have some knowledge about the monitored environment characteristics in which the target system operates. For example, in forest fire applications, information about the forest topography can be obtained from the Geographic Information System (GIS) or satellites maps.  
WSNs have an intimate interaction, via sensors, with the physical environment they operate within because they are usually deployed to perform application specific tasks. The part of the world with which an application is concerned is defined as that application's domain. This work advocates that an application domain of a WSN can serve as a supplement to data analysis, interpretation, and visualisation methods and tools. Therefore, we elevate the distributed mapping service capabilities to make use of the special characteristics of applications. This section extends the distributed mapping service to an Adaptive, Multi-modal Application domain driven (AMA) mapping framework, which is suitable for mapping an arbitrary number of sense modalities and is capable of utilising the relations between different modalities as well as other parameters of the application domain to improve the mapping  
Figure 2: An illustrative diagram showing a generic WSN and how the distributed mapping service operates on it.  
performance. AMA is capable of dealing with dynamically changing application domain conditions. It starts with an initial user defined model that is autonomously maintained and updated throughout the network lifetime.  
A domain model carries knowledge of an application domain. It is a conceptual model of a system, which describes the various real world entities involved in that system and relationships between them. The domain model provides a structural view of the system, which we suggest using to complement the information gained from analysing data gathered by a WSN. In this section, we argue that by using additional knowledge available about the monitored environment, the distributed mapping service has a greater potential for giving more meaning to the collected data. The logical integration of a domain model and sensory data from multiple heterogeneous sensory sources can be effectively used to explain past observations as well as to predict future observations. This approach can take advantage of human guidance and information from other available sources, e.g., satellites. Furthermore, it maintains the overall coherence of reasoning about the gathered data and helps to estimate the degree of confidence using probabilistic domain models. The use of knowledge made available by the domain model can also be key to meeting the energy and channel capacity constraints of a WSN system. The energy efficiency of the system can be improved by utilising a domain model in the process of converting data into increasingly distilled and high-level representations.