Distributed compression in a dense microsensor network - IEEE Signal Processing Magazine

The distributed nature of the sensor network architecture introduces unique challenges and opportunities for collaborative networked signal processing techniques that can potentially lead to significant performance gains. Many evolving low-power sensor network scenarios need to have high spatial density to enable reliable operation in the face of component node failures as well as to facilitate high spatial localization of events of interest. This induces a high level of network data redundancy, where spatially proximal sensor readings are highly correlated. In this article, we propose a new way of removing this redundancy in a completely distributed manner, i.e., without the sensors needing to talk to one another. Our constructive framework for this problem is dubbed DISCUS (distributed source coding using syndromes) and is inspired by fundamental concepts from information theory. In this article, we review the main ideas, provide illustrations, and give the intuition behind the theory that enables this framework.