A SENSOR network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions. The purpose of a sensor network is to provide users with the information of interest from data gathered by spatially distributed sensors. Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods Thus, it is not surprising that signal estimation has been one of the most fundamental collaborative information processing problems in sensor networks and has found wide applications in military and civilian fields, such as target tracking and localization, air traffic Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods control, guidance, and navigation. Such signal estimations in a sensor network could be done under the end-to-end information flow paradigm by communicating all the relevant data to a central collector node, e.g., a sink node. This, however, is a highly inefficient solution in sensor networks, because it may cause long packet delay, and it has the potential for a critical failure point at the central collector node, and most of all, the sensor networks are usually severely constrained in energy and bandwidth,. Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods To avoid these problems, an alternative solution is for the estimation to be performed in-network every sensor with both sensing and computation capabilities performs not only as a sensor but also as an estimator, and it collects measurements from its neighbors to generate estimates. This is known as the distributed estimation and has attracted increasing attention during the past few years . In sensor networks,measurements aresampled and Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods transmitted to estimators via unreliable communication networks. lthoughfrequent measurement sampling and transmission may improve estimation performance, it, however, consumes much energy and is thus not desirable in sensor networks with constrained energy. In other words, estimation should be performed in an energyefficient way in sensor networks, and one straightforward yet efficient way is to increase measurement sampling periods. Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods However, thismayin turn degrade estimation erformance. Thus, one has to tradeoff between estimation performance and energy consumption in sensor network based estimations and the tradeoff can be intuitively realized by adopting a nonuniform sampling strategy. Such a strategy brings much design flexibility, e.g., onemayincrease thesampling period to save energies during some periods while decrease it to improve estimation performance during some other time intervals when necessary.