Distributed signal processing in sensor networks [from the guest Editors]

R ecent advances in hardware technology have led to the emergence of small, low-power, and possibly mobile devices with limited onboard processing and wireless communication capabilities. Typically, these devices, called sensors, consist of a radio frequency circuit, a low-power digital signal processor, a sensing unit, and a battery. Some may be capable of actuation. Due to their low cost and low complexity design requirement, individual sensors can only perform simple local computation and communicate over a short range at low data rates. But when deployed in large numbers across a spatial domain, these primitive sensors can form an intelligent network to measure aspects or identities of the physical environment on a potentially unprecedented scale and with high precision. Sensor networks of this type are ideal for situation awareness applications such as environmental monitoring (air, water, and soil), healthcare monitoring , home applications such as " smart climate control, " smart factory instru-mentation, military surveillance, precision agriculture, space exploration, and intelligent transportation. To fully exploit the potential of sensor networks, it is essential to develop energy and bandwidth efficient signal processing algorithms that can be implemented in a fully distributed manner. Distributed signal processing in a wireless sensor network differs from the traditional signal processing framework in several important aspects. ■ Sensor measurements are collected in a distributed fashion across the network. This necessitates data sharing via intersensor communication. Given a low energy budget per sensor, it is unrealistic for sensors to communicate all their full-precision data samples with one another. Thus, local data compression becomes an integral part of the distributed signal processing design. In contrast, in a traditional signal processing framework where data is centrally collected, there is no need for distributed data compression. ■ The design of optimal distributed signal processing algorithms depends on the parametric data model used, the knowledge of sensor noise distributions , the qualities of intersensor communication channels, and the underlying application metrics. Distributed signal processing over a wireless sensor network requires judicious coordination and planning of sensor computation as well as careful exploitation of the limited communication capability per sensor. In other words, distributed signal processing in sensor networks has a communication aspect not present in the traditional signal processing framework. ■ In a wireless sensor network, sensors may enter or leave the network dynamically, resulting in unpredictable changes in network size and topology. Sensors may disappear permanently either due to damage to …