IoT-Enhanced Localization: Smart Technologies and Systems for Intelligent Environments
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 December 2025 | Viewed by 439
Special Issue Editors
Interests: last-mile delivery optimization; machine learning; Internet of Things; indoor localization systems; smart environments
Interests: cyber-physical systems; network-on-chip based architectures, hw accelerators; user-centric systems
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Special Issue Information
Dear Colleagues,
The rapid diffusion of the Internet of Things (IoT) has transformed commercial buildings into complex, interconnected ecosystems. Indoor localization—the process of determining the position of people or objects within indoor environments—has emerged as a key enabler of a wide range of applications and services, from guiding pedestrians within large buildings such as shopping malls, airports, and hospitals, to improving emergency response times and enhancing asset tracking efficiency. IoT also supports a broad array of location-based services tailored to users’ positions.
While outdoor localization typically relies on the Global Positioning System (GPS)—which is considered the de facto standard—indoor localization presents several challenges such as signal attenuation, multipath effects, and complex architectural layouts. These challenges affect the accuracy of indoor localization systems and have stimulated the development of innovative solutions leveraging heterogeneous IoT devices and infrastructures, machine learning technologies, edge and cloud computing, and other state-of-the-art techniques to optimize performance.
This Special Issue, titled “IoT-Enhanced Localization: Smart Technologies and Systems for Intelligent Environments”, aims to synthesize high-quality research that explores how IoT paradigms are reshaping the landscape of indoor localization systems.
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Scope
We invite submissions addressing a broad spectrum of topics related to indoor localization in the context of IoT, including, but not limited to, the following:
- IoT Architectures for Indoor Localization: the design and deployment of IoT infrastructures supporting accurate and scalable indoor localization systems.
- IoT Sensor Fusion and Data Integration: combining multi-modal IoT data streams to enhance the precision, reliability, and robustness of indoor positioning systems.
- AI for IoT-based Localization: the development of AI-driven approaches—such as deep learning, reinforcement learning, and hybrid models—that leverage IoT devices and infrastructures for localization.
- Temporal and Dynamic Indoor Localization: addressing challenges posed by changing indoor layouts, crowd movements, and environmental changes, and developing strategies to account for these dynamics.
- Benchmarking, Standards, and Datasets: the evaluation of publicly available datasets and the creation of new, high-quality benchmarks for comparative research.
- Energy-Aware and Low-Latency Systems: the optimization of energy consumption and latency for battery-powered and real-time IoT-based localization systems.
- Privacy and Security in IoT-Enabled Localization: ensuring secure localization, user anonymity, and privacy-preserving data processing in smart environments.
- Use Cases and Real-World Deployments: case studies and pilot projects in hospitals, industrial sites, shopping centers, museums, public buildings, and other locations.
We look forward to receiving your contributions and insights.
Dr. Gaetano La Delfa
Dr. Salvatore Monteleone
Guest Editors
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Keywords
- indoor localization
- real-time positioning
- Internet of Things (IoT)
- IoT infrastructure
- sensor fusion
- smart environments
- machine learning
- artificial intelligence (AI)
- edge computing
- location-based services
- privacy and security
- indoor localization benchmark datasets
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