## 3 Related Work  
Wildlife behavior monitors can be classified into three basic groups categorized by their data retrieval method: (1) Same-message burst transmitters; (2) data loggers; and (3) data transceivers. These are summarized in Table 1. In the first group, same-message burst transmitters broadcast an encoded unique number that must be received by one or more base stations within their transmission range [8; 9] and include Radio Frequency Identification Device (RFID) tags [10; 11]. RFID tracking devices do not typically transmit any information other than a unique number for identification. For the second group, loggers archive sensor data, but require manual retrieval which can potentially lead to complete data loss [12]. Both data loggers and same-message transmitters are typically designed for tracking small animals and as a result may not include methods for energy harvesting because overall device size and weight may interfere with natural behavior of the study animal. With respect to the third group, data transceivers typically use GPS satellite localization and transmit acquired data through radio communication [13]. In addition to functionality, behavior monitoring devices can be classified by their energy needs. In general, burst transmitters typically use the least amount of energy or none in the case of passive RFID, with logging style devices second, and transceivers requiring the highest level of energy. This study selected a data transceiver for analysis and development due to the largest potential impact on performance and because the data collection rate can be regulated to manage energy consumption. Figure 1: Cellular Track Technologies (CTT)-1000a Internet of things (IoT) wildlife behavior monitoring device. Figure 2: Measured behavior monitor performance using fixed collection interval and programmable power supply in place of solar panel (**a**) showing data collection gap and battery state of charge (**b**). Energy supply is critical to IoT wireless sensor devices, as lifetime and utility of the sensors are based on how long a battery can sustain operation [4; 14; 15]. Harvesting energy from the environment has to supplement stored battery energy [16; 17; 18; 19; 20; 21; 22] and selection of processing modes based on available energy [23] are common methods of extending operating life of IoT devices. Photovoltaic cells (PVC) can be used for solar energy harvesting and provide a renewable energy resource that is well suited for behavior monitors [11; 24]. However, any freely available energy source many have uncertainty in its delivery. Solar capture systems may be impacted by factors such as the solar diurnal cycle, weather patterns, and shading from foliage [25; 26]. Several behavior monitors have been created that utilize energy harvesting, but they typically collect data, and consequently spend energy, at conservative fixed intervals [27; 28]. In addition to harvesting energy from the environment, responsible spending of energy for data collection must occur so as to not prematurely use all stored energy. Scheduling and prioritization of tasks or sensor data collection is known to extend device operation by selectively performing tasks based on a threshold of available energy, or task energy cost. As a result, device energy consumption can effectively be reduced while still remaining partially operational [29]. This method may be unable to adjust energy consumption rates to with a variable acquisition time sensor such as GPS. The use of a hard threshold is the major issue with commonly used energy reduction techniques. In addition, when not collecting data, a system should take careful consideration proper methods of reducing power consumption of the system as a whole and in particular the microcontroller. The exact reasons for sleep at appropriate states or scale-back operation of a microcontroller can have profound long-term impacts on power usage [30]. In practice, harvested energy is never consistent.