Monitoring Wireless Sensor Networks (WSNs) are composed of sensor nodes that report temperature, relative humidity, and other environmental parameters. The time between two successive measurements is a critical parameter to set during the WSN configuration because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data. As trends in monitored parameters can significantly vary between scenarios and within time, identifying a sampling interval suitable for several cases is also challenging. In this work, we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors' sampling interval on-the-fly, according to environmental conditions and application requirements. The primary goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application. In simulations, our mechanism could reduce up to 73 the total number of transmissions compared to a fixed strategy and, simultaneously, keep the average quality of information provided by the WSN. The inherent flexibility of the reinforcement learning algorithm facilitates its use in several scenarios, so as to exploit the broad scope of the Internet of Things.
In many practical applications of wireless sensor networks, the sensor nodes are required to report approximations of their readings at regular time intervals. For these applications, it has been shown that time series prediction techniques provide an effective way to reduce the communication effort while guaranteeing user-specified accuracy requirements on collected data. Achievable communication savings offered by time series prediction, however, strongly depend on the type of signal sensed, and in practice an inadequate a priori choice of a prediction model can lead to poor prediction performances. We propose in this paper the adaptive model selection algorithm, a lightweight, online algorithm that allows sensor nodes to autonomously determine a statistically good performing model among a set of candidate models. Experimental results obtained on the basis of 14 real-world sensor time series demonstrate the efficiency and versatility of the proposed framework in improving the communication savings.