Energy-Efficient and Trustworthy Location-Based Services for Next-Generation IoT

Over the past decade, the rapid expansion of the Internet of Things (IoT) has profoundly reshaped the digital landscape, connecting billions of devices capable of sensing, processing, and interacting with their environments. Within this ecosystem, Location-Based Services (LBS) have emerged as a cornerstone technology, enabling context-aware applications in domains as diverse as industrial automation, healthcare, smart cities, and precision agriculture. Yet, delivering LBS that are at once energy-efficient, accurate, and trustworthy remains an open challenge, particularly in dynamic and resource-constrained IoT environments. Conventional positioning systems, such as Global Positioning System (GPS), provide high accuracy under open-sky conditions but fall short in IoT deployments due to their high energy demands, unreliable indoor performance, and vulnerability to interference and malicious threats. These limitations have stimulated extensive research on GPS-free localization techniques that exploit existing wireless infrastructures, including WiFi, Bluetooth Low Energy (BLE), and Long-Range (LoRa). Among these, Received Signal Strength (RSS)-based approaches stand out for their simplicity, cost-effectiveness, and compatibility with commodity devices. However, their performance is strongly affected by environmental variability, multipath propagation, and adversarial behaviors. This doctoral thesis addresses these challenges through the design and validation of a comprehensive framework for energy-efficient and trustworthy GPS-free RSS-based localization in IoT scenarios. The proposed contributions advance the state of the art by developing hybrid algorithms that combine complementary techniques to improve accuracy and resilience, introducing a novel reliability index to dynamically assess the trustworthiness of each position estimate, and devising security mechanisms to counteract disruptive attacks such as jamming. Validation is carried out through both simulations and experimental testbeds, ensuring robustness under realistic conditions. Finally, the thesis culminates in a real-world case study in smart agriculture, illustrating how advanced IoT-based LBS can translate scientific research into tangible value for a sector where sustainability, tradition, and technological innovation converge.