Data fusion and error reduction algorithms for sensor networks

Sensor networks are attracting attention in several fields. However, the feasibility of such networks faces several challenges, two of which are data fusion and error reduction. This paper presents data fusion and high level error correction algorithms for sensor networks. These algorithms are scalable and general, and thus can be applied to networks of any size using any type of sensors. The data fusion procedure developed results in significant reduction of data sent without reducing the amount of information provided. This allows for real-time remote monitoring of information across low bandwidth connections such as the Internet. The high level error reduction is accomplished using a probability matrix and results in a significant amount of error elimination. A sensor network capable of tracking object motion is constructed to evaluate the performance of the two algorithms. The experimental results obtained confirmed the theory presented.