# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 2 Background and Related Work
### Wireless Signal Ranging Technology
#### 2.1.2 Bluetooth-Assisted Signal Ranging  
Bluetooth technology, with its low power consumption and high deployment density, enables meter-level signal ranging in indoor environments. Its methods primarily include triangulation, scene analysis, and proximity.  
Triangulation offers the highest precision by calculating device coordinates based on distances to at least three known Bluetooth nodes, typically using RSSI or time-of-flight (TOF) measurements. Environmental interference may cause errors, requiring algorithmic corrections. Scene analysis matches real-time Bluetooth signal measurements to a pre-established fingerprint database, enabling robust localization in complex environments but demanding comprehensive and accurate radio maps. Proximity estimation, the simplestapproach, infers relative distance based on signal strength thresholds without providing precise coordinates, making it suitable for applications requiring only approximate location information. Bluetooth-assisted signal ranging has been widely explored in indoor localization research due to its accessibility and low power consumption. Recent studies have further advanced Bluetooth-based positioning by incorporating deep learning models and hybrid fingerprinting strategies, achieving sub-3-meter accuracy even in dynamic environments [14; 15]. Beyond pure learning-based approaches, several works have investigated hybrid systems to enhance BLE localization performance. [PERSON] et al. proposed a fusion of Bluetooth Low Energy (BLE) and ultra-wideband (UWB) signals, employing regression to significantly improve positioning robustness in complex indoor environments [16]. Complementing this, [PERSON] et al. introduced a lightweight BLE fingerprinting solution based on beacon calibration and smartwatch integration, targeting practical room-level tracking with minimal infrastructure [17]. In parallel, there has been a growing focus on improving the interpretability of BLE localization. [PERSON] et al. presented an explainable machine learning framework that leverages RSSI measurements to enhance model transparency and user trust in sensitive applications [18]. Performance optimization from a system design perspective has also been explored. [PERSON] et al. systematically evaluated BLE signal processing techniques, identifying strategies such as refined RSSI filtering and optimized anchor placement to maximize positioning accuracy [19]. Furthermore, adaptive fingerprinting methods have been proposed to address environmental dynamics. [PERSON] et al. developed a dynamic fingerprint window approach (DFW-WKNN), adjusting the search space in real-time based on environmental context, thereby boosting localization precision in large-scale settings [20]. Overall, the recent trajectory of Bluetooth-assisted localization research reflects an integrated evolution--from traditional signal-based methods toward hybrid, learning-augmented, interpretable, and adaptive systems, aiming to meet the growing demands for accuracy, scalability, and robustness in dynamic indoor environments. Despite these improvements, Bluetooth-assisted localization still faces significant challenges, including sensitivity to multipath and non-line-of-sight conditions, limited scalability in large deployments, increased positioning latency in dense networks, and vulnerability to signal spoofing attacks, all of which hinder its broader adoption in mission-critical scenarios.