Localization and Mobility Solutions in Next-Generation Smart IoT Networks

This paper describes a new way to monitor people’s positions and movements to improve next-generation smart IoT networks’ accuracy, efficiency, and real-time flexibility. The recommended solution combines machine learning-based signal processing, adaptive filtering, and motion-based enhancements for high-precision tracking in changing situations. We use RSSI, ToA, and AoA to estimate location. Kalman filtering and weighted fusion reduce localization errors and provide real-time flexibility. Mobility-aware adjustment using past movement data and predictive analytics enhances tracking by adapting for motion and environmental interference. The proposed technology outperforms GPS, Wi-Fi, Bluetooth Low Energy, and Ultra-Wideband. The recommended approach has the lowest latency (8 ms) and energy expenditure (0.3 Joule) and provides 0.2-meter location precision, better than GPS (8 meters) and Wi-Fi (3 meters). The technology makes adding devices, keeping them secure, and eliminating interference easy. This technology is ideal for mission-critical IoT applications such as smart cities, industrial automation, and healthcare monitoring. Bayesian reasoning, reinforcement learning, and neural networks provide flexible learning and long-term localization accuracy. With this architecture, next-generation smart IoT networks can better track their users and locate themselves. It uses sophisticated signal processing, real-time sensor fusion, and predictive mobility models. It is efficient, versatile, and low-power, making it ideal for smart, connected environments of the future.