Implementation of a secure environment for Ambient Intelligence IoT solutions
The rapid growth in the number of Internet of Things devices has expanded the attack surface of connected environments, raising urgent security concerns. At the same time, this proliferation enables the development of Ambient Intelligence systems which are IoT systems that transform raw physical mag...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/452683 |
| Acceso en línea: | https://hdl.handle.net/2117/452683 |
| Access Level: | acceso embargado |
| Palabra clave: | Internet of things Computer security Microcontrollers Seguridad IoT AI IA Internet de les coses Seguretat informàtica Microcontroladors Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica |
| Sumario: | The rapid growth in the number of Internet of Things devices has expanded the attack surface of connected environments, raising urgent security concerns. At the same time, this proliferation enables the development of Ambient Intelligence systems which are IoT systems that transform raw physical magnitudes collected by sensors into valuable contextual information, thanks to the increasing capabilities of sensor and edge nodes to process data locally and even execute lightweight AI tasks. This master thesis investigates hardware and software strategies to design secure IoT sensor nodes that are compliant with upcoming European regulations, in particular the Cyber Resilience Act, which becomes mandatory in 2027. To meet these requirements, the project focused on recently introduced microcontrollers based on the ARM Cortex-M33, a processor architecture specifically designed with hardware primitives for embedded security. A comparative analysis of available Cortex-M33 platforms led to the selection of two candidates: the Raspberry Pi Pico 2W and the NXP FRDM-RW612, which combine modern connectivity with advanced security features. Using these platforms, a secure Ambient Intelligence sensor node prototype was developed and validated to demonstrate key mechanisms such as secure boot, encrypted storage of credentials, secure communications, and local AI inference for context-aware applications. The final objective is to evaluate alignment with upcoming cybersecurity legislation, strengthen the robustness of IoT sensor devices, and contribute practical insights for the secure deployment of Ambient Intelligence systems. |
|---|