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Energy-neutral operation (ENO) is a major concern for Internet of things (IoT) sensor systems. Animals can be tagged with IoT sensors to monitor their movement and behavior. These sensors wirelessly upload collected data and can receive parameters to change their operation. Typically, the behavior monitors are powered by a battery where the system relies upon harvesting solar radiation for sustainable operation. Solar panels typically are used as the harvesting mechanism and can have a level of uncertainty regarding consistent energy delivery due to factors such as adverse weather, foliage, time of day, and individual animal behavior. The variability of available energy inevitably creates a trade-off in the rate at which data can be collected with respect to incoming and stored energy. The objective of this research was to investigate and simulate methods and parameters that can control the data collection rate of an IoT behavior monitor to achieve sustained operation with unknown and random energy harvesting. Analysis and development of a control system were performed by creating a software model of energy consumption and then simulating using different initial conditions and random energy harvesting rates for evaluation. The contribution of this effort was the exploration into the usage of a discrete-time gain scheduled Proportional–Integral–Derivative (PID) that was tuned to a specific device configuration, using battery state of charge as an input, and found to maintain a battery level set-point, reject small solar harvesting energy disturbances, and maintain a consistent data collection rate throughout the day.
Jay Wilhelm; Sheldon Blackshire; Michael Lanzone. Energy-Neutral Data Collection Rate Control for IoT Animal Behavior Monitors. Applied Sciences 2017, 7, 1169 .
AMA StyleJay Wilhelm, Sheldon Blackshire, Michael Lanzone. Energy-Neutral Data Collection Rate Control for IoT Animal Behavior Monitors. Applied Sciences. 2017; 7 (11):1169.
Chicago/Turabian StyleJay Wilhelm; Sheldon Blackshire; Michael Lanzone. 2017. "Energy-Neutral Data Collection Rate Control for IoT Animal Behavior Monitors." Applied Sciences 7, no. 11: 1169.
Fixed Wing Unmanned Aerial Vehicles (UAVs) performing Intelligence, Surveillance and Reconnaissance (ISR) typically fly over Areas of Interest (AOIs) to collect sensor data of the ground from the air. If needed, the traditional method of extending sensor collection time is to loiter or turn circularly around the center of an AOI. Current Autopilot systems on small UAVs can be limited in their feature set and typically follow a waypoint chain system that allows for loitering, but requires that the center of the AOI to be traversed which may produce unwanted turns outside of the AOI before entering the loiter. An investigation was performed to compare the current loitering techniques against two novel smart loitering methods. The first method investigated, Tangential Loitering Path Planner (TLPP), utilized paths tangential to the AOIs to enter and exit efficiently, eliminating unnecessary turns outside of the AOI. The second method, Least Distance Loitering Path Planner (LDLPP), utilized four unique flight maneuvers that reduce transit distances while eliminating unnecessary turns outside of the AOI present in the TLPP method. Simulation results concluded that the Smart Loitering Methods provide better AOI coverage during six mission scenarios. It was also determined that the LDLPP method spends less time in transit between AOIs. The reduction in required transit time could be used for surveying additional AOIs.
Jay P. Wilhelm; Garrett S. Clem; Gina M. Eberhart. Direct Entry Minimal Path UAV Loitering Path Planning. Aerospace 2017, 4, 23 .
AMA StyleJay P. Wilhelm, Garrett S. Clem, Gina M. Eberhart. Direct Entry Minimal Path UAV Loitering Path Planning. Aerospace. 2017; 4 (2):23.
Chicago/Turabian StyleJay P. Wilhelm; Garrett S. Clem; Gina M. Eberhart. 2017. "Direct Entry Minimal Path UAV Loitering Path Planning." Aerospace 4, no. 2: 23.