Contribution to the enhancement of IoT-based application development and optimization of underwater communications, by artificial intelligence, edge computing, and 5G networks and beyond, in smart cities/seas
(English) 6G networks have emerged as a revolutionary breakthrough, promising ultra-fast and reliable connectivity that redefines the way we interact with the digital world. This new generation of networks not only drives communication between devices but is also the backbone of the Internet of Thin...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/693321 |
| Acceso en línea: | http://hdl.handle.net/10803/693321 https://dx.doi.org/10.5821/dissertation-2117-422056 |
| Access Level: | acceso abierto |
| Palabra clave: | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Àrees temàtiques de la UPC::Informàtica 004 621.3 |
| Sumario: | (English) 6G networks have emerged as a revolutionary breakthrough, promising ultra-fast and reliable connectivity that redefines the way we interact with the digital world. This new generation of networks not only drives communication between devices but is also the backbone of the Internet of Things. In addition, the learning and adaptive capabilities of Artificial Intelligence systems are driving process automation and efficiency. Similarly, Edge Computing complements this landscape by decentralizing data processing, bringing computing capacity closer to the sources of information. This allows for reducing latency and improving efficiency by processing data in real-time, driving critical applications that require instantaneous responses. This thesis focuses on two important points: 1) Improving the efficiency of applications in smart cities, and 2) Enhancing the efficiency of underwater communications in smart coastal cities by applying artificial intelligence, edge computing, and 5G and beyond. To achieve these objectives, an exhaustive study of the existing literature on 5G and beyond networks, smart cities, and artificial intelligence has been carried out. In addition, technical documentation to obtain an updated view of the different technologies that enable the development of applications based on 5G and beyond has been analyzed. Aiming to generate new and innovative alternatives in the field of tourism, security, improved underwater communications, and marine discovery that drive promote development to meet the needs of citizens in smart cities and ocean/sea. As a result of this study, the first contribution has emerged. It involves the analysis, design, and implementation of a tourist attraction recommendation system employing a deep learning algorithm tailored for smart cities. The primary objective is to improve how tourist attraction recommendations are made so that they are tailored to the requirements of each visitor in a given city and thereby reduce the time it may take a visitor to search for possible places to visit. The second contribution arises in surveillance and security, which consists of a distraction detection system for the prevention of drowning in aquatic places, developed in a 5G and beyond network environment. For this goal, an approach of surveillance cameras capturing images of people in charge of minors in swimming pools or beaches was proposed; and employing an ML algorithm (convolutional neural networks) to classify the type of distraction that a person in charge of a minor may have. Finally, the third contribution is presented, called reinforcement learning and mobile edge computing for 6G-based underwater wireless networks. In this approach, a submerged edge mobile computing architecture is presented in which an AUV is used as a mobile platform (MEC), in addition, several local AUVs equipped with computational resources that collect tasks from sensor nodes and can make the decision to process them locally or partially or fully offload them to the mobile edge computing AUV device. To this end, an algorithm based on deep reinforcement learning (DDPG) is proposed for trajectory control, task offloading strategy, and computational resource allocation, combined with mobile edge computing and AUVs to improve underwater communication; aiming to minimize the sum of maximum processing delays and energy consumption during the whole process of executing a task. The contributions presented in this doctoral thesis are of singular importance, since to date they continue to be innovative. The contributions presented not only represent significant advances in their respective areas but also lay the groundwork for future research and developments in smart city construction and underwater communications optimization, thereby reinforcing the transformative potential of artificial intelligence, edge computing, and advanced wireless networks in these domains. |
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