rly not scalable, smart sensor networks will have to (CSIP) to refer to signal and information processing rely on in-network intelligence to focus on interesting problems dominated by this issue of selecting embed- events, filter out detractors, extract meaning from raw ded sensors to participate in an information processing sensor readings or video streams, and transmit only rel- task. The topic of active control and tasking of sensors is evant scene features to human users. quite unique to sensor networks. 3.7.4 Collaborative processing 3.7.5 Key definitions of sensor networks As the above applications have illustrated, many sensing tasks require a sensor network system to process data cooperatively and to combine information from multiple Sensor networks is an interdisciplinary research area that sources. In traditional centralized sensing and signal draws on contributions from signal processing, network- processing systems, raw data collected by sensors are ing and protocols, databases and information manage- relayed to the edges of a network where the data is ment, distributed algorithms, and embedded systems processed. From the scalability point of view, the and architecture. In the following, we define a number of nonlocal processing at the edges depletes precious key terms and concepts that will be used throughout the bandwidth. If every sensor has some data that it needs to book as we develop techniques and examples for sensor send to another node in a network, then a well-known networks. wireless capacity result by Gupta and Kumar states that Sensor: A transducer that converts a physical phe- the per node throughput scales as other words, it nomenon such as heat, light, sound, or motion into goes to zero as the number of nodes N in a wireless ad hoc electrical or other signals that may be further ma- network increases. This result holds regardless of opti- nipulated by other apparatus. mality in routing, power control, or transmission. In- Sensor node: A basic unit in a sensor network, with tuitively, t