Publicação
Design of the 3rd Generation Reconfigurable IoT Node with AI-Enhanced Edge Computing
| Resumo: | The growth of Internet of Things (IoT) has lead a digital transformation, connecting billions of devices that are able to sense, collect, process and exchange information across multiple domains, ranging from everyday applications, to industrial systems. As IoT networks continue growing, integrating edge computing Artificial Intelligence (AI) technology has become a necessary enhancement in intelligent data processing. By enabling a device to analyze and act on data locally, opportunities for autonomous and faster decisions arise, instead of relying on cloud infrastructure. Conventional IoT nodes face critical challenges, as relying on cloud computation introduces issues regarding latency, energy consumption and data privacy. Additionally, with countless devices constantly acquiring and transmitting data, network saturation issues can lead to a system’s impairment. By employing reconfigurable, low-power techniques in combination with AI processing, IoT technologies can move towards an increase in their own autonomy and self-adaptability to any environment. This dissertation will focus on the research and implementation of efficient hardware and firmware that enable local data acquisition, offline preprocessing and intelligent infer- ence, whilst simultaneously maintaining a low-power consumption profile. In addition, a distributed architecture network was designed to demonstrate how edge computing can enhance IoT deployments. |
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| Autores principais: | Julião, André Filipe Lopes |
| Assunto: | IoT Edge Computing AI Low-Power Design Distributed Processing Audio Processing |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The growth of Internet of Things (IoT) has lead a digital transformation, connecting billions of devices that are able to sense, collect, process and exchange information across multiple domains, ranging from everyday applications, to industrial systems. As IoT networks continue growing, integrating edge computing Artificial Intelligence (AI) technology has become a necessary enhancement in intelligent data processing. By enabling a device to analyze and act on data locally, opportunities for autonomous and faster decisions arise, instead of relying on cloud infrastructure. Conventional IoT nodes face critical challenges, as relying on cloud computation introduces issues regarding latency, energy consumption and data privacy. Additionally, with countless devices constantly acquiring and transmitting data, network saturation issues can lead to a system’s impairment. By employing reconfigurable, low-power techniques in combination with AI processing, IoT technologies can move towards an increase in their own autonomy and self-adaptability to any environment. This dissertation will focus on the research and implementation of efficient hardware and firmware that enable local data acquisition, offline preprocessing and intelligent infer- ence, whilst simultaneously maintaining a low-power consumption profile. In addition, a distributed architecture network was designed to demonstrate how edge computing can enhance IoT deployments. |
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