Adaptive Sampling for Effective Energy Management in Wireless Sensor Networks with Energy-hungry Sensors

Energy conservation techniques for wireless sensor networks generally assume that data acquisition and processing have energy consumption that is significantly lower than that of communication. Unfortunately, this assumption does not hold in a number of practical applications, where sensors may consume even more energy than the radio. In this context, effective energy management should include policies for an efficient utilization of the sensors, which become one of the main components that affect the network lifetime. In this paper, we propose an adaptive sampling algorithm that estimates online the optimal sampling frequencies for sensors. This approach, which requires the design of adaptive measurement systems, minimizes the energy consumption of the sensors and, incidentally, that of the radio while maintaining a very high accuracy of collected data. As a case study, we considered a sensor for snow-monitoring applications. Simulation experiments have shown that the suggested adaptive algorithm can reduce the number of acquired samples up to 79% with respect to a traditional fixed-rate approach. We have also found that it can perform similar to a fixed-rate scheme where the sampling frequency is known in advance