Wind energy holds potential for in-situ powering large-scale distributed wireless sensor nodes (WSNs) in the Internet of Things (IoT) era. To achieve high performance in wind energy harvesting, a coaxial counter-rotating triboelectric nanogenerator with lift–drag hybrid blades, termed CCR-TENG, has been proposed. The CCR-TENG, which can work in non-contact and soft-contact modes, realizes low-speed wind energy harvesting through a combination of counter-clockwise rotating lift-type blades and clockwise rotating drag-type blades. Non-contact CCR-TENG realizes low-speed wind energy harvesting at wind speeds as low as 1 m/s. The output of a CCR-TENG, working in soft-contact mode, achieves 41% promotion with a maximum short-circuit current of 0.11 mA and a peak surface power density of 6.2 W/m² with two TENGs connected in parallel. Furthermore, the power density per unit of wind speed achieves 746 mW/m³·s/m. Consequently, two fluorescent lamps were successfully illuminated and six temperature sensors were continuously lit by the CCR-TENG. The reported CCR-TENG significantly improves low-speed environmental wind energy utilization and demonstrates broad application prospects for in-situ power supply of distributed wireless transmission devices and sensors in the era of the IoT.

Efficient pilotage planning is essential, particularly due to the increasing demand for skilled pilots amid frequent vessel traffic. Addressing pilot shortages and ensuring navigational safety, this study presents an innovative pilot-ASV scheduling strategy. This approach utilizes autonomous surface vehicles (ASVs) to assist or replace junior pilots in specific tasks, thereby alleviating pilot resource constraints and upholding safety standards. We develop a comprehensive mathematical model that accommodates pilot work time windows, various pilot levels, and ASV battery limitations. An improved artificial bee colony algorithm is proposed to solve this model effectively, integrating breadth-first and depth-first search strategies to enhance solution quality and efficiency uniquely. Extensive numerical experiments corroborate the model’s effectiveness, showing that our integrated optimization approach decreases vessel waiting times by an average of 9.18% compared to traditional methods without ASV integration. The findings underscore the potential of pilot-ASV scheduling to significantly improve both the efficiency and safety of vessel pilotages.