# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 2 Related Work
### Energy Management for EH-WSNs  
Research on energy management for EH-WSNs has been very prolific in recent years, and many solutions have been proposed [2; 23; 24; 25; 26; 27; 28; 29; 30; 31]. They can be classified based on their requirement of predicted information about the amount of energy that can be harvested in the future, i.e., prediction-based and model-free. Prediction-based schemes require that an energy predictor [32] supplies the EM with predictions of the future harvested energy. The first EM using the prediction-based approach was introduced in 2007 by [PERSON] et al. [2]. In this scheme, an exponentially-weighted moving average filter is used to predict the future amount of harvested energy, and the duty-cycle is computed according to the difference between predicted and observed energy inputs. [PERSON] et al. introduced the Closed-Loop Power Manager (CL-PM) in [24], which uses two distinct energy management strategies, one for periods during which environmental energy is available and one for periods during which the harvested energy is below a fixed threshold, referred to as zero energy interval. [PERSON] et al. proposed Wake-up Variation Reduction PM (WVR-PM) [25], a variation of CL-PM that allows a node to store more energy when environmental energy is available to achieve a similar quality of service during zero energy interval periods than when environmental energy is available. [PERSON] et al. proposed a prediction-based algorithm [33] for energy harvesting sensor nodes using a supercapacitor as the energy storage device. The algorithm consists of a prediction block, which provides the forecast of the supercapacitor energy reserve for a given consumption and a given harvest forecast. The second block implements an energy policy, which defines predicates to enforce the properties of the operation style. The algorithm was implemented on a testbed of twelve energy harvesting wireless sensor nodes organized in a multihop topology, and the field test lasted four weeks. In a previous work [26], we introduced GRAPMAN, an EM for EH-WSNs powered by pseudo-periodic energy sources that aims to achieve high average throughput while maintaining consistent quality of service, i.e., with low fluctuations with respect to time. As the amount of energy that a sensor can harvest shows large fluctuations and is hard to predict, energy predictors suffer from significant errors, incurring overuse or underuse of the harvested energy [29]. Unlike prediction-based approaches, model-free schemes do not require any prediction or model of the energy source. LQ-Tracker [27] uses Linear-Quadratic Tracking, a technique from adaptive control theory, to adapt the duty-cycle considering only the state of charge of the energy storage device. Similarly, [PERSON] et al. proposed using a Proportional Integral Derivative (PID) controller [28]. With P-FREEN [29], [PERSON] et al. designed an EM that maximizes the duty-cycle of a sensor node in the presence of battery storage inefficiencies. The authors formulated the average duty-cycle maximization problem as a non-linear programming problem and proposed a set of budget-assigning principles that maximized the duty-cycle by using the currently-observed energy harvesting rate and the residual energy. In a previous work [30], we proposed an approach that relies on fuzzy control theory to dynamically set the node's consumed energy. Fuzzy rules are used to make a decision about the allocated energy considering the current state of charge of the energy storage device and the amount of harvested energy. [PERSON] et al. proposed AutoSP-WSN [4], a framework for Solar-Powered WSNs incorporating both energy management algorithms and communication protocols and that was evaluated in the field. However, this work differs from ours in two major ways. First, the authors introduce routing and link rate control protocols, while we focus on MAC protocols leveraging emerging ULP WuRx.