Résumé
The Internet of Things (IoT) is transforming communication and data exchange across sectors such as healthcare and transportation, where efficient and precise localization and high-resolution sensing are critical requirements for applications ranging from asset tracking and autonomous navigation. Existing localization techniques often suffer from accuracy limitations, excessive energy consumption, and latency issues due to the dynamic and resource-constrained environments in which IoT devices operate. These limitations are exacerbated by the dense deployment of IoT devices, multipath signal propagation, and environmental interference, which can compromise the reliability of location-based services. This paper presents a novel hybrid model that integrates quantum mechanics for state representation, Markov processes for predictive modeling, and the Salp Swarm Algorithm (SSA) for optimizing nodal positions and speeds. Our Quantum-SSA-Markov model significantly enhances localization accuracy and efficiency in IoTbased wireless sensor networks (WSNs), outperforming existing techniques in energy consumption, latency, signal-to-noise ratio (SNR), and data flow rate. Experimental results confirm that this innovative approach provides a scalable and reliable solution for next-generation IoT systems, particularly in scenarios that require robust real-time localization.