Building an Energy-Efficient Ad-Hoc Network for Wildlife Observation

Abstract

This study evaluated the design of an energy-efficient ad-hoc network used for wildlife observations, particularly in order to understand the social relationships in an animal group, where the distance between individuals, i.e., proximity, can be used to measure a relationship. Our proposed network consists of a full mesh topology and contains nodes that communicate via Bluetooth Low Energy (BLE) in advertisement mode. The initial hardware configuration and software algorithm duty cycles the BLE communication to on and off states using a parameter called the BLE active triggering interval. The algorithm is improved by placing the BLE subsystem and CPU in deep sleep when there are no BLE or CPU tasks to process. This improves the power performance by up to 94.48%. To scale up power optimization and track the trade-off between power and throughput, we created a simulator that modeled our network with dynamic wireless sensor nodes. The simulator verified the base case hardware results. It also showed a median power performance increase of 97.79% in comparison to the base case, yet throughput decreased by 66.65%. The highest power performance increased by 98.89% when a wireless sensor node was configured with a BLE active triggering interval of 50 s and its CPU was set to 14 MHz; however, the simulator showed a throughput drop of 79.97%. Depending on the application, a design may tolerate the decline in throughput to achieve higher power performance.

Internet on animals: Wi‐Fi‐enabled devices provide a solution for big data transmission in biologging

“…However, LPWANs are limited in their data transmission capacity per day by small bandwidths (10–100 s of bytes per day), for example, 1 GPS fix per hour (Antoine‐Santoni et al., 2018). Bluetooth low energy (BLE) can also be used for the transmission of sensor data (Ayele et al., 2018; Kirkpatrick et al., 2021), but is more commonly used for proximity detection according to signal strength between devices (Camal & Aksanli, 2020); such as the use of ProxLogs tags to record encounters of starlings Sturnidae vulgaris (Kirkpatrick et al., 2021), or BATS tracking system to examine foraging interactions in the common noctule bat Nyctalus noctula (Duda et al., 2018; Ripperger et al., 2019). In contrast, big data can only be stored on onboard memory without wireless access (Eikelboom et al., 2020; Kirkpatrick et al., 2021; Wijers et al., 2018).…”