Enhancing Remote Conservation in Borneo: NVIS for Wildlife Monitoring and Protection using BearWave

Biodiversity conservation in fragmented and remote ecosystems often requires labour-intensive, time-consuming fieldwork, placing staff at risk and limiting the scope of long-term monitoring. To address these challenges in a sustainable, locally adaptable manner, we present BearWave, a novel, place-based HF communication framework attuned to local infrastructure constraints and designed to support low-power sensing networks under severe radio-frequency (RF) conditions. By leveraging Near Vertical Incidence Skywave (NVIS) propagation and the FT8 digital modulation technique, BearWave achieves reliable, bidirectional data transfer—even in dense tropical rainforest conditions—while keeping costs under £200 per node and enabling extended battery-powered operation. A case study in UK woodlands, chosen as an environmental analogue to Borneo’s rainforest, demonstrated BearWave’s robustness and adaptability: despite dense vegetation and non-line-of-sight paths, the system maintained over 90% message reliability at distances of up to 25 km using only 1 W of RF power. Notably, the strongest signal propagation and best reception rates occurred during nighttime, reflecting diurnal ionospheric variations. These empirical results confirm that BearWave outperforms conventional technologies such as LoRaWAN or satellite systems in harsh, attenuating environments, offering a scalable, energy-efficient approach that lowers both ecological footprints and staff labor risks. This research advances conservation-focused communication by providing a universal, scientifically validated framework capable of supporting long-term ecological monitoring, poacher detection, and improved animal welfare. Crucially, BearWave’s low-cost, low-impact design broadens access for under-resourced organisations and community-driven conservation programs, where local knowledge and stakeholder insights help shape technology decisions on the ground. By embracing a socio-technical innovation model, BearWave exemplifies how computing can be sustainably embedded in remote ecosystems worldwide.