Stabilizing Quality of Wi-Fi-Based Location Services Using High-Performance Distributed Stream Processing and Data Pipelines

Location-based Systems (LBS) are popular for delivering customized information. However, some issues, such as place credibility, the efficiency of position calculations, and communication latency, pose challenges for indoor LBS. This work proposes the High-performance Perspective Platform (H3P) to help network managers understand network users’ information. The H3P provides indoor positioning services based on Wi-Fi 6 (IEEE 802.11ax) to stabilize service quality and ensure high computation efficiency for rapid service response. It utilizes Apache Kafka and Apache Zookeeper clusters on Kubernetes to handle large amounts of data. Wi-Fi usage data is transmitted to Kafka’s distributed real-time data streaming to enhance position credibility and the immediacy of position calculations. The data structure is also optimized to improve computation efficiency. Experimental results show that H3P improves data latency by up to 69% and data insertion latency by up to 73%. Additionally, H3P offers more stability and efficiency than Chang et al., 2012 in terms of data transmission, with an improvement of approximately 16.15%. This allows administrators to manage the network with user-friendly interfaces and a smooth user experience.