Traditional network performance simulations and lab tests often miss the complexities of the real world. Device manufacturers and network operators may omit implementing optional features like Multiple User MIMO (MU-MIMO) or massive MIMO due to practical or economic limitations. The resulting lack of clarity on deployed network performance highlights the need for independent, academic measurement reports, free from commercial operators' bias. To achieve this, a scalable and easy-to-use measurement methodology is essential to enable comprehensive assessment of as many deployed networks as possible. Our research explores the design, techniques, and challenges of leveraging consumer devices as wireless measurement tools. We extract wireless data from devices using standard device APIs and root-access capabilities: the prior is simpler to implement but provides limited access, while the latter requires specific devices but yields more detailed information. These two approaches complement each other: the non-root method enables large-scale data collection with reduced complexity, while the root-based method focuses on in-depth analysis at a smaller scale. Our combined approach reveals key performance differences between 4G and 5G deployments in low-band (<1 GHz), mid-band (1-6 GHz), and high-band/mmWave (>24 GHz). First, while we confirm the Gigabit-level downlink throughput advertised in 5G mmWave networks, it is limited by range, poor indoor penetration, and device thermal limitations. Next, we observe 5G mid-band outperforms 5G low-band and its 4G counterparts, which is primarily driven by increased bandwidth rather than advanced features. We also uncovered adjacent and co-channel interference between 4G and 5G mid-band channels (i.e., CBRS, C-Band spectrum), due to insufficient guard bands and mismatched TDD configurations. These findings underscore the importance of additional spectrum in future 6G networks, while it is also imperative to prioritize the implementation of new features, such as MU-MIMO and higher modulation, for optimal performance. Beyond cellular networks, our methodology has proven valuable in evaluating the unlicensed spectrum. First, we highlight the sensitivity threshold and hidden node problem that occurs in the coexistence between Wi-Fi and LTE in the unlicensed 5 GHz. Next we focus on the newly released unlicensed 6 GHz utilized by Wi-Fi 6E, particularly the low power indoor (LPI) regime which was created to protect incumbent fixed links. While concerns persist regarding LPI's interference, our extensive measurement campaigns across two university campuses revealed negligible interference due to significant building entry loss. However, further research is necessary to determine appropriate signal levels for the proposed client-to-client (C2C) mode in future Wi-Fi 7 specifications. Our device-based methodology effectively characterizes both previous-generation (4G and Wi-Fi 5/802.11ac) and current-generation (5G and Wi-Fi 6E) wireless networks. Our findings have directly informed spectrum policies, including energy detection thresholds in the unlicensed 5 GHz spectrum and C2C signal levels in the 6 GHz band. Importantly, our API-based approach has proven scalable and is currently utilized by various US universities within SpectrumX's Broadband Map US project. Additionally, the extensive data we've collected provides a valuable resource for machine learning applications, such as indoor-outdoor classification.