Smart Collar Technologies and GPS-Based Tracking Systems for Understanding Wildlife Movement in Human-Dominated Landscapes

Abstract

Familiarity with wildlife movement in a landscape dominated by humans is critical for biodiversity conservation, to reduce human–wildlife conflicts, and to inform evidence-based habitat management. The present study introduces a smart collar-based wildlife tracking framework that combines GPS positioning, wireless communication and embedded sensing technologies to track the movement and behavior of animals constantly and automatically. The proposed framework is centered on using GPS localization, accelerometer sensor, temperature sensor and activity sensor, in conjunction with low power IoT communication to gather high resolution spatiotemporal data under various environmental conditions. A systematic methodology is presented for pre-processing trajectories, estimating home ranges, analyzing movements, assessing migrations and mapping habitat use. The framework allows to identify movement corridors, resting places, seasonal migration pathways and wildlife-anthropogenic infrastructure interactions (such as crossing roads, settlements, agricultural areas, etc.). Moreover, the study addresses practical aspects of deploying smart collars, data collection methodology, energy efficient operation and animal welfare aspects. Together with the novel research opportunities that arise from multi-sensor fusion, edge intelligence, artificial intelligence, and real-time conservation decision support, challenges such as GPS signal loss, battery restrictions, communication constraints and environmental interference are analysed critically. The proposed framework proposes to be scalable and reliable to enhance ecological monitoring, wildlife conservation planning and sustainable living and coexistence between human and wildlife population in fast changing landscapes.