Multilateration-Assisted Fingerprinting-Based Localization for Dynamic IoT Environments

Accurate and reliable localization is crucial in dynamic Internet of Things (IoT) environments where real-time, location-based decision-making is required. However, this presents significant challenges, especially when anchor availability or environmental conditions change. In this paper, we present a novel hybrid Received Signal Strength (RSS)-based localization approach that integrates fingerprinting algorithms with multilateration assistance to effectively address the challenges of scenario dynamism. The primary objective of this work is to improve robustness and reliability over standard fingerprinting-based algorithms. The results show that the proposed solution significantly outperforms traditional techniques as anchor availability decreases, achieving comparable performance in unobstructed scenarios. This demonstrates the robustness and adaptability of hybrid approaches combining fingerprinting and multilateration, thereby advancing research on this promising yet underexplored combination.