The widespread availability of sensor devices and the rapid increase in their deployment, everywhere from industrial plants to private homes has put sensor network research in the spotlight for the past several years. Moreover, the requirement for rich highly configurable sensor network applications has led to the emergence of Service Oriented Sensor Networks (SOSNs), which imports the concept of Service Oriented Architecture (SOA) into the sensor network domain. It represents each of its sensors as a service object in a service framework that allows their dynamic discovery and composition into applications. Representing sensors as composable services and utilizing their associated knowledge can lead to significant enhancements in information processing capabilities of a sensor network, allowing it to operate on sophisticated data types and events beyond the primitive data types typically originating from individual hardware sensors. This dissertation describes the research and development of Sensable, a scalable query processing middleware which extends the capabilities of service-oriented sensor networks as follows: (1) Provides adaptive sensor-aware query processing to minimize overall power consumption in sensor networks by utilizing knowledge associated with sensor services to identify and minimize sensing operations which cause significant power drain; (2) Enhances Smart Space capabilities to sense virtual types of data which cannot be directly sourced from physical sensors due to their inherent sophistication or higher Quality of Service (QoS) requirements and; (3) Enables Smart Spaces to monitor and track phenomena event clouds whose shape, size and motion cannot be modeled precisely.;Sensable attempts to bring query processing using service-oriented sensor networks into the realm of immediate utility by providing query scalability based on actual network infrastructure using current and emerging technologies and advanced querying capabilities encompassing abstract data types fused from multiple sensors, the concept of data quality in face of failure and detection and tracking of events susceptible to uncertainty in face of noise.
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