With the increasing demand for high-precision, low-latency indoor localization in IoT and smart environments, the efficient online processing of CSI data has become a pressing challenge. While Chap. 4 focused on offline fingerprint updates, this chapter turns to online applications, aiming to ensure accurate and rapid localization in dynamic environments. This chapter begins by exploring the fundamental requirements for high-speed localization, emphasizing the trade-offs between speed and accuracy, and the impact of environmental dynamics on real-time performance. We then discuss advanced techniques for accelerating fingerprint matching, including optimized search algorithms and strategies for handling dynamic obstacles. A key highlight is the introduction of the BLS as a lightweight, efficient model for real-time localization, enhanced by ensemble learning to improve robustness and accuracy. Then, we present the BLS-Location, a lightweight online localization algorithm based on Broad Learning System, which accelerates fingerprint matching and maintains reliable performance with low computational cost. To further enhance adaptability, we propose the ILCL algorithm, which transforms CSI phase data into images and uses a CNN for offline training. During the online stage, it incrementally adapts to new data using a probabilistic method based on BLS, without the need for retraining. Experimental evaluations show that both BLS-Location and ILCL offer significant improvements in accuracy and efficiency, especially in large-scale and dynamic indoor environments.