WSN-based wildlife localization framework in dense forests through optimization techniques

Wildlife in forests is threatened by land use changes, requiring tracking to characterize movement patterns and propose preservation policies. The positioning uses GPS-based collars (End Nodes (ENs)), which are energy-consuming and require a line of sight with the satellites, a condition rarely fulfilled in forests. It motivates using Wireless Sensor Networks, which rely on the Received Signal Strength Indicator (RSSI) and Time of Flight (ToF) to determine the distance between the EN and Anchor Nodes (ANs) with known positions and, subsequently, apply trilateration. However, existing approaches may have significant errors due to multipath and shadow fading caused by dense canopies. Thus, this paper proposes a three-step framework to address these limitations. First, it optimizes the ANs positions, increasing the redundancy of trilateration and coverage, enhancing the likelihood of accurate localization, and ensuring sufficient data to mitigate adverse channel effects. Second, it presents an optimization problem that minimizes the variance of distance estimation since the associated errors can increase exponentially. Finally, it scores the ANs with the most reliable position estimations to mitigate the effects of outliers. Numerical studies show that our optimized AN placement improves coverage by 25% compared to random or equispaced strategies. The distance estimator achieves a Mean Average Percentage Error (MAPE) below 7%, outperforming the Wiener-based estimator at 20%. Finally, our scoring method reduced MAPE to 5.53% with a standard deviation of 7.15% compared with the median strategy that achieved 9.66% and a standard deviation of 15.87% when ten ANs are placed in a region of 100 km${}^2$.