On the use data reduction algorithms for real-time Wireless Sensor Networks.

This work presents the design of real time applications for Wireless Sensor Networks (WSNs) by using analgorithm based on data stream to process the sensor data. The proposed algorithm is based on sampling techniques applied to data histograms created from original data stream sac quired by sensor nodes. As a result, the algorithm provides a sample of log n item store present the original data stream of n elements. In this work, we show how to use the data reduction algorithmin real-time WSN design

Data Stream Based Algorithms For Wireless Sensor Network Applications

A wireless sensor network (WSN) is energy con-strained, and the extension of its lifetime is one of the most important issues in its design. Usually, a WSN collects a large amount of data from the environment. In contrast to the conventional remote sensing ? based on satellites that collect large images, sound files, or specific scientific data ? sensor networks tend to generate a large amount of sequential small and tuple-oriented data from several nodes, which constitutes data streams. In this work, we propose and evaluate two algorithms based on data stream, which use sampling and sketch techniques, to reduce data traffic in a WSN and, consequently, decrease the delay and energy consumption. Specifically, the sampling solution, provides a sample of only log n items to represent the original data of n elements. Despite of the reduction, the sampling solution keeps a good data quality. Simulation results reveal the efficiency of the proposed meth-ods by extending the network lifetime and reducing the delay without loosing data representativeness. Such a technique can be very useful to design energy-efficient and time-constrained sensor networks if the application is not so dependent on the data precision or the network operates in an exception situation (e.g., there are few resources remaining or there is an urgent situation