## 3 Related Work
This is especially true for migratory animals, meaning that an energy-harvesting behavior monitor must adapt its energy consumption relative to its harvesting rate. The energy consumption of a wildlife behavior monitor is not consistent enough to model with traditional linear techniques due to the variation in acquisition time for sensors such as the GPS. Achieving Energy Neutral Operation (ENO) for an animal behavior monitoring device has typically first required modeling of the energy consumption and harvesting [31], then development of a sensor rate control system. An energy usage model of wireless sensors nodes was developed by [24] that was used to develop an adjustable solar data collection rate to achieve ENO. Solar energy was assumed to be periodic and proportional to a diurnal cycle. In the model presented in [24], a period of 24 h was broken into blocks and a constant value for incoming energy was assigned to each block. Data collection was then modified based on a difference in measured and predicted energy for each of these blocks. Any deviation from predicted and actual energy harvested may result in sub-optimal data collection due to periods of depleted or full battery charge. [PERSON] presented a model-free, adaptive control algorithm that modified data collection to meet the following metrics: ENO, performance maximization, and duty cycle stability [32]. Selecting a data collection rate that balanced energy consumed and harvested was developed using linear-quadratic tracking to minimize the difference between battery charge and a target value. These techniques were tested \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Class** & **1** & **2** & **3** \\ \hline
**Type** & **Burst** & **Logging** & **Transceivers** \\ \hline \multirow{4}{*}{Advantages} & Very small and lightweight. & \multirow{4}{*}{Typically small in size. } \\  & Long-term (years) battery & & \\  & operation. Radio Frequency & Ability to collect position & \\  & Identification Device (RFID) & & and behavior data. \\  & does not need stored energy. & & \\ \hline \multirow{3}{*}{Disadvantages} & Does not collect position or & \multirow{3}{*}{Device must be retrieved to acquire data. Limited} & \multirow{3}{*}{Typically larger and heavier than other classes. Requires more energy.} \\  & behavior data. Requires & & \\ \cline{1-1}  & receiving base stations. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of common behavior monitor devices. with a small set of data on a constant energy consuming sensor. Their results indicated that highly variable harvesting rates, which have been seen on wildlife tracking devices, may be an issue when using their algorithm. Computational impact of a control method for ENO should be of concern and the most functional and minimal algorithm should be selected. There are many options for feedback control algorithms that may achieve intended research goals, and several of the most popular ones were compared regarding computational impact. Fuzzy logic is a method of using linguistic expressions and activation functions to form a control value. The linear quadratic regulator (LQR) is an optimal control method that minimizes the cost function and requires calculation of a gain matrix before use. Proportional-integral-derivative (PID) control is a simple method that relies on three gain values that can be tuned automatically given a linear model or manually. PID control was used by [33] to adjust data collection such that an energy storage element was maintained at a constant level. State of charge (SOC) was used as a control signal and the node duty cycle was manipulated. \(H_{\infty}\) is a non-linear optimal method of control that normally guarantees robust performance of systems.