Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor

Abstract- In order to fuse data from a distributed sensor system, different fusion architectures can be chosen. In this work, distributed data fusion has been applied to data from a ground sensor network. Measurements have been made using five seismic and five acoustic sensor nodes, each consisting of three sensors, on vehicles of five different types. The data were analyzed off-line to achieve tracking and classification of the vehicles. The tracking was made using standard Kalman filters, and classification was made by a combination of a Bayes’ classifier and voting. The information system, which made distributed data fusion possible, was based on a combination of intelligent agents and a distributed look-up table for data storage and retrieval. The results indicate that this system can operate on-line with a high accuracy in both tracking and classification.

The localization requirements for mobile nodes in wireless (sensor) networks are increasing. However, most research works are based on range measurements between nodes which are often oversensitive to the measurement error. In this paper we propose a location estimation scheme based on moving nodes that opportunistically exchange known positions. The user couples a linear matrix inequality (LMI) method with a barycenter computation to estimate its position. Simulations have shown that the accuracy of the estimation increases when the number of known positions increases, the radio range decreases and the node speeds increase. The proposed method only depends on a maximum RSS threshold to take into account a known position, which makes it robust and easy to implement. To obtain an accuracy of 1 meter, a user may have to wait at the same position for 5 minutes, with 8 pedestrians moving within range on average. 1.