Unveiling NearLink's Localization Potential: First Public RSSI Dataset and Cross-Environment Evaluation with Wi-Fi and BLE

Wireless localization is a cornerstone of mobile and ubiquitous computing, enabling applications ranging from smart homes to robotic navigation. Popular Wi-Fi and Bluetooth Low Energy (BLE) technologies face limitations in positioning accuracy, coverage range, power consumption, and interference resilience. NearLink, an emerging short-range wireless technology, combines the high throughput and extended range of Wi-Fi with the low-power characteristics of BLE, offering potential advantages for localization. This paper presents a comprehensive evaluation of NearLink, including individually and in combination with Wi-Fi and BLE, across diverse indoor and outdoor environments - classroom, parking lot, and helipad - using high-fidelity RSSI data collected via an autonomous robotic platform. We release the first publicly available NearLink RSSI fingerprint dataset and benchmark six localization methods, including MLT, KNN, MLP, LSTM-RNN, HADNN, and GConvLoc. Results show that NearLink (SLE mode) achieves sub-meter level accuracy (0.81 m in classrooms), robust anti-interference performance, extended communication range (up to 725 m), and low power consumption (2.39 mW in SLE mode). Fusion with BLE further improves localization stability and robustness. These findings demonstrate NearLink's promise for precise, energy-efficient localization in dynamic real-world environments.