Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings. We show that the state-of-the-art policy-gradient approaches to RL are appropriate for the IoT domain and that they outperform previous approaches. Due to the ability to model continuous observation and action spaces, as well as improved function approximation capability, the new approaches are able to solve harder problems, permitting reward functions that are better aligned with the actual application goals. We show such a reward function and use policy-gradient approaches to learn capable policies, leading to behavior more appropriate for IoT nodes with less manual design effort, increasing the level of autonomy in IoT.
@cite_10 focused on the configuration of the sampling rate of indoor energy harvesting sensors. The objective of their agent is to maximize the number of samples while avoiding power failure, by designing a reward function that depends on the sampling rate and a penalty for power failure. They used Q-learning with a state space that included light intensity, the voltage level of the energy storage, and the current sampling rate. Another example of using RL for adaptive sampling can be found in @cite_22 . Here proposed using Q-learning for adaptive sampling rate adaption to avoid oversampling, while not missing environmental changes. They introduced a variable that describes the quality of measurement as the difference between two consecutive measurements and, depending on a specific application, this difference should be less than a threshold value. The action space is a range of possible sampling intervals, while the reward function is based on the transmission avoided and the quality of measurements.
@cite_10 @cite_22
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A reinforcement learning (RL) method for Quality of Service (QoS)-aware power management (PM) of an energy harvesting wireless sensor network, named QoS-aware RLPM, is proposed in this paper. The RL environment for each sensor node is represented by the observable measurements of harvesting energy and residual energy stored in the battery. To achieve QoS-awareness, the proposed QoS-aware RLPM attempts to satisfy various QoS demands by adjusting the duty-cycle rate of sensor node according to the observable measurements. The outcomes of these interactions are evaluated by rewards that express how well the duty-cycle rate adjustments satisfy QoS requests under energy neutrality criteria [1]. The QoS-aware RLPM learns the adjustment strategy of the duty-cycle rate by interacting with the environment and at the same time the QoS of the sensor network is maintained. Experiment results show that the proposed method satisfies the QoS requests under the energy neutrality criteria and performs better than the adaptive duty-cycling method of [1]. With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions. In this paper, we present an adaptive power manager for solar energy harvesting sensor nodes. We use a simplified model consisting of a solar panel, an ideal battery and a general sensor node with variable duty cycle. Our power manager uses Reinforcement Learning (RL), specifically SARSA(λ) learning, to train itself from historical data. Once trained, we show that our power manager is capable of adapting to changes in weather, climate, device parameters and battery degradation while ensuring near-optimal performance without depleting or overcharging its battery. Our approach uses a simple but novel general reward function and leverages the use of weather forecast data to enhance performance. We show that our method achieves near perfect energy neutral operation (ENO) with less than 6 root mean square deviation from ENO as compared to more than 23 deviation that occur when using other approaches. Distributed sampling and reconstruction of a physical field using an array of sensors is a problem of considerable interest in environmental monitoring applications of sensor networks. Our recent work has focused on the sampling of bandlimited sensor fields. However, sensor fields are not perfectly bandlimited but typically have rapidly decaying spectra. In a classical sampling set-up it is possible to precede the A D sampling operation with an appropriate analog anti-aliasing filter. However, in the case of sensor networks, this is infeasible since sampling must precede filtering. We show that even though the effects of aliasing on the reconstruction cannot be prevented due to the "filter-less" sampling constraint, they can be suitably controlled by oversampling and carefully reconstructing the field from the samples. We show using a dither-based scheme that it is possible to estimate non-bandlimited fields with a precision that depends on how fast the spectral content of the field decays. We develop a framework for analyzing non-bandlimited fields that lead to upper bounds on the maximum pointwise error for a spatial bit rate of R bits meter. We present results for fields with exponentially decaying spectra as an illustration. In particular, we show that for fields f(t) with exponential tails; i.e., F( spl omega ) < spl pi spl alpha sup - spl alpha | spl omega | , the maximum pointwise error decays as c2e sup -a sub 1 spl radic R+ c3 1 spl radic R (e sup -2a sub 1 spl radic R) with spatial bit rate R bits meter. Finally, we show that for fields with spectra that have a finite second moment, the distortion decreases as O((1 N) sup 2 3 ) as the density of sensors, N, scales up to infinity . We show that if D is the targeted non-zero distortion, then the required (finite) rate R scales as O (1 spl radic D log 1 D).