Intelligent Localization and Navigation Systems: AI, Sensing and IoT Integration

Dear Colleagues,

This Special Issue will focus on the convergence of Artificial Intelligence (AI), advanced sensing, and the Internet of Things (IoT) to overcome the fundamental challenges of indoor and complex-environment localization and navigation. Unlike mature outdoor systems, these environments are plagued by signal degradation, multipath effects, and dynamic obstacles. The integration of AI/ML with multimodal sensing data (from Wi-Fi, BLE, UWB, LiDAR, inertial sensors, and vision) presents a paradigm shift, enabling systems that are not only accurate and robust but also adaptive, scalable, and secure. Emerging large language and foundation models also offer new opportunities for semantic reasoning, system orchestration, and human-centered interaction within intelligent localization and navigation systems.

We invite researchers and practitioners to contribute original works that bridge theoretical models with practical applications. This Special Issue aims to foster interdisciplinary dialogue and showcase solutions that address scalability, real-time processing, and seamless integration into the broader IoT ecosystem. By compiling cutting-edge research on algorithmic innovation, system architecture, and empirical validation, this collection will serve as a key reference for the development of the next generation of intelligent, autonomous, and context-aware spatial computing systems.

Contributions are welcome on topics including, but not limited to:

• AI/ML models for localization and tracking (e.g., deep learning, federated learning, reinforcement learning).
• Sensor fusion techniques for robust positioning in dynamic environments.
• IoT-enabled and edge-computing architectures for scalable navigation systems.
• Energy-efficient and low-latency algorithms for real-time operation.
• Security, privacy, and trust frameworks for AI-driven navigation.
• Benchmarking, performance evaluation, and novel datasets.
• Experimental testbeds and real-world case studies in smart buildings, healthcare, or industrial settings.
• Foundation models and LLM-enabled semantic reasoning with natural language interfaces for human-centered indoor localization and navigation systems.
• Digital Twins for real-time synchronization between IoT navigation systems and physical environments in Industry 4.0.
• Physics-informed machine learning and hybrid modelling for signal propagation and kinematics.
• Green AI and energy-harvesting-aided localization for resource-constrained IoT devices.
• Collaborative navigation and swarm intelligence for multi-agent systems and robotics.
• Advanced semantic mapping, scene understanding, and V-SLAM for autonomous systems.
• Explainable AI (XAI) and trustworthy models for spatial reasoning and decision-making.
• Integration of foundation models and LLMs in robotics for intelligent navigation and human–robot interaction.

Dr. Cristian González García
Dr. Santosh Subedi
Prof. Dr. Jae-Young Pyun
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• indoor localization
• sensor fusion
• edge AI
• IoT navigation
• machine learning
• smart environments
• positioning systems
• wireless sensing
• multimodal sensing
• semantic mapping
• context-aware navigation
• V-SLAM
• explainable AI