Semantic Sensor Web

TitleSemantic Sensor Web
Publication TypeJournal Article
Year of Publication2008
AuthorsCory Henson, Amit Sheth

In March 2008, heavy rainstorms across the Midwestern region of the US caused many rivers to breach their banks. Residents of Valley Park, a small town along the Meramec River, Missouri, had to decide whether to rely on a newly constructed levee or abandon their homes for higher ground.1 Although the levee held, many chose the latter option and fled their homes; it was a chaotic situation that might have been avoided through access to better situational knowledge regarding the current water pressure and the levee's structural integrity. Had pressure sensors been embedded in the levee, they might have provided accurate real-time information that let residents make informed decisions about the safety of the levee, their homes, and themselves. This scenario demonstrates the increasingly critical role of sensors that collect and distribute observations of our world in our everyday lives. In recent years, sensors have been increasingly adopted by a diverse array of disciplines, such as meteorology for weather forecasting and wildfire detection (, civic planning for traffic management (, satellite imaging for earth and space observation (, medical sciences for patient care using biometric sensors (, and homeland security for radiation and biochemical detection at ports ( Sensors are thus distributed across the globe, leading to an avalanche of data about our environment. The rapid development and deployment of sensor technology involves many different types of sensors, both remote and in situ, with diverse capabilities such as range, modality, and maneuverability. Today, it's possible to use sensor networks to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge. With a view to addressing this problem, we discuss a semantic sensor Web (SSW) in which sensor data is annotated with semantic metadata to increase interoperability as well as provide contextual information essential for situational knowledge. In particular, this involves annotating sensor data with spatial, temporal, and thematic semantic metadata.

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