a may be This is an example of date-centric networking. characterized by its relevance to an estimation task at In-network: A style of processing in which the data hand and computed by a mutual information is processed and combined near where the data is function. generated. Resource: Resources include sensors, communication Collaborative processing: Sensors cooperatively pro- links, processors, on-board memory, and node energy cessing data from multiple sources in order to serve reserves. Resource allocation assigns resources to a high-level task. This typically requires communi- tasks, typically optimizing some performance cation among a set of nodes. objective. State: A snapshot about a physical environment (e.g., Sensor tasking: The assignment of sensors to a partic- the number of signal sources, their locations or spa- ular task and the control of sensor state (e.g., on/off, tial extent, speed of movement), or a snapshot of the pan/tilt) for accomplishing the task. system itself (e.g., the network state). Node services: Services such as time synchronization Uncertainty: A condition of the information caused and node localization that enable applications to by noise in sensor measurements, or lack of knowl- discover properties of a node and the nodes to orga- edge in models. The uncertainty affects the system's nize themselves into a useful network. ability to estimate the state accurately and must Data storage: Sensor information is stored, indexed, be carefully modeled. Because of the ubiquity of and accessed by applications. Storage may be local to uncertainty in the data, many sensor network the node where the data is generated, load-balanced estimation problems are cast in a statistical frame- across a network, or anchored at a few points work. For example, one may use a covariance matrix (warehouses). to characterize the uncertainty in a Gaussian-like Embedded operating system (OS): The run-time process or more general probability distributions for system support for sensor network applications. An non-