Sensor stream reduction for clustered wireless sensor networks
Proceedings of the 2008 ACM symposium on Applied computing
An in-network reduction algorithm for real-time wireless sensor network applications
Proceedings of the 4th ACM workshop on Wireless multimedia networking and performance modeling
Employed BPN to Multi-sensors Data Fusion for Environment Monitoring Services
ATC '09 Proceedings of the 6th International Conference on Autonomic and Trusted Computing
Expert Systems with Applications: An International Journal
A wavelet-based sampling algorithm for wireless sensor networks applications
Proceedings of the 2010 ACM Symposium on Applied Computing
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A wireless sensor network (WSN) is energy constrained, 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 tupleoriented 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 methods 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).