A New Autonomous Data Transmission Reduction Method For Wireless Sensors Networks

The inherent limitation in energy resources and computational power for sensor nodes in a Wireless Sensor Network, poses the challenge of extending the lifetime of these networks. Since radio communication is the dominant energy consuming activity, most presented approaches focused on reducing the number of data transmitted to the central workstation. This can be achieved by deploying both on the workstation and the sensor node a synchronized prediction model capable of forecasting future values. Thus, enabling the sensor node to transmit only the values that surpasses a predefined error threshold. This mechanism offers a decrease in the cost of transmission energy for a price of an increase in the cost of computational energy. Therefore, finding the right tradeoff between complexity and efficiency is very important to achieve optimal results. In this paper, we present a novel data reduction method that outperforms other state of the art data reduction approaches. We demonstrated the efficiency of our algorithm using simulation on real-world data sets collected at our laboratory. The obtained results show that our method was able to achieve a data suppression ratio ranging between 93.2% and 99.8%.