Reducing the data transmission in sensor networks through Kruskal-Wallis model
| Autor: | Ali Jaber, Oussama Zahwe, Mohamad Abou Taam, Hassan Harb, Abdallah Makhoul, Chady Abou Jaoude |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
Testbed Real-time computing 020206 networking & telecommunications 02 engineering and technology Data modeling Key distribution in wireless sensor networks Sensor node 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Wireless sensor network Data reduction Data transmission Efficient energy use |
| Zdroj: | WiMob |
| DOI: | 10.1109/wimob.2017.8115780 |
| Popis: | Data reduction is one of the most attractive way to conserve the limited energy resources of wireless sensor networks (WSNs). It aims to remove unnecessary data transmission. Therefore, data prediction and reduction mechanisms must be deployed at the source node in order to eliminate the redundant sensed data before sending them to the sink. In this paper, an energy efficient periodic distributed data reduction technique is proposed. Our technique allows each sensor node to search the variation between readings collected at each period based on the Kruskal-Wallis model. Then, the sensor selects a set of representative readings instead of sending the whole readings collected during a period to the sink. To evaluate the performance of our technique, simulations on a publicly available real sensor data followed by experiments in a real-world telosB sensor network testbed have been performed. Compared to other existing approaches, we are able to achieve up to 80% communication reduction while maintaining a high level of data accuracy. |
| Databáze: | OpenAIRE |
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