Intelligent Topology Management for TDOA-Based Localization in IoT Networks

Time Difference of Arrival (TDOA) localization plays a pivotal role in IoT networks, driving applications such as smart city infrastructure, industrial asset tracking, and environmental monitoring. However, traditional centralized localization approaches impose excessive computational demands and communication overhead, making them unsuitable for resource-constrained IoT deployments. This letter introduces a novel distributed TDOA localization framework, leveraging intelligent topology management to dynamically adapt network configurations for optimal performance. An Iterative Multi-Stage Adaptive Estimation (MAE) algorithm is developed, providing a robust closed-form solution for node interaction optimization, significantly improving the trade-off between computational efficiency and communication overhead. The proposed method achieves superior localization accuracy by mitigating the impact of measurement noise and addressing energy constraints inherent to IoT environments. Simulation results demonstrate substantial gains in positioning performance, energy efficiency, and scalability compared to state-of-the-art algorithms, highlighting its suitability for real-time IoT applications in complex and dynamic network scenarios.