t sensor networks. Such networks are constrained by energy and band- width limitations, which require solutions to take resource conservation into account. Because sensors may be exposed to the wear and tear of motion and changing weather, there are also issues of robustness. Decentralized systems require the identification of structures within col- lections of spatio-temporally distributed signal streams. A critical technical challenge is to aggregate, represent, and maintain the structure-level infor- mation from point-wise sensor data in an irregular, dynamic network. This requires approaches beyond traditional signal processing. PARC researchers are developing novel data interpretation approaches that blend statistical and model-based techniques for explicitly reasoning about uncertainty and resources. They are employing artificial intelligence to enable individual sensors to "know" what information to sense and share. PARC's collaborative processing approach is inherently scalable. One aim of the research is to develop the capability to aggregate, store, and process information from hundreds of thousands of sensors. PARC researchers are engaged in talks with customers about real-world implementations of their collaborative sensing technologies. One potential application would embed sensors in roads and vehicles to manage traffic flow. Another would create smart security systems that could detect intrusions in buildings or within the boundaries of airports, power plants, or other sites. Smart sensors could also be used to track the movement of supplies and equipment. 3.10 OPTICAL SENSORS As more sensors are built into various types of networks, developers and users are beginning look for devices that can collect the most data while consuming NAVIGATING THE REAL WORLD the least power and occupying the smallest possible space. Fiber-optic sensors fulfill all of these criteria and more. Fiber-optic sensors offer a wide spectrum of advantages over traditional sensing systems, including smaller sizes and longer li