Adaptive Sampling for Energy Conservation in Wireless Sensor Networks for Snow Monitoring Applications

Energy conservation techniques for sensor networks typically rely on the assumption that data sensing and processing consume considerable less energy than communication. This assumption does not hold in some practical application scenarios, where ad hoc developed sensor units require power consumption comparable with, or even larger than, that of the radio. In this paper we focus on an embedded sensor for monitoring snow composition in mountain slopes for avalanche forecasting. To lower the sensor energy consumption we propose an adaptive sampling algorithm able to dynamically estimate the optimal sampling frequency of the signal to be monitored. In turn, this minimizes the activity of both the sensor and the radio (hence saving energy) while maintaining an acceptable accuracy on the acquired data. Simulation experiments show that the suggested solution can save up to 97% of the energy consumed for sensing when the sensor is always on, while maintaining the error at acceptable levels.