Smart and efficient sensor networks operation for 5G and beyond ecosystems
(English) Sensor Networks (SN) will play an integral role in Beyond 5G (B5G) ecosystems, especially for highly-distributed use cases and services such as Digital Twins (DT). Thus, the underlying transport network needs to provide connectivity between the highly dense and distributed SNs and the DT m...
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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:10803/690458 |
| Acceso en línea: | http://hdl.handle.net/10803/690458 https://dx.doi.org/10.5821/dissertation-2117-405945 |
| Access Level: | acceso abierto |
| Palabra clave: | Àrees temàtiques de la UPC::Informàtica Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 004 621.3 |
| Sumario: | (English) Sensor Networks (SN) will play an integral role in Beyond 5G (B5G) ecosystems, especially for highly-distributed use cases and services such as Digital Twins (DT). Thus, the underlying transport network needs to provide connectivity between the highly dense and distributed SNs and the DT manager, that typically runs far from sensor data sources, i.e., in a centralized server. In view of this, critical requirements such as high data throughput, latency sensitivity communication, and data veracity and integrity assurance are essential to be provided by B5G networks to support DT services. In order to meet such requirements, statistical and Artificial Intelligence (AI)-based SN data collection and analysis can be implemented to provide smart and efficient data transmission. By means of those procedures, SN data can be compressed and analyzed locally in order to reduce the total data volume to be conveyed in the centralized server. In addition, the inherent nature of the compression and analysis algorithms add privacy and security to the transmitted data without affecting integrity. The use of these kind of AI-based techniques opens the opportunity to perform Knowledge Transfer (KT) between DTs operating under the same tenant infrastructures. Since sharing raw data poses a privacy breach, AI-based methods allow interchanging relevant information while obfuscating critical details, thus enabling coordinated operation across differentiated segments. This Ph.D. thesis aims at enhancing the operation of dense SNs, which are supported by an underlying transport infrastructure that includes edge/fog computing capabilities distributed among nodes. Through the application of statistical and AI-based methods and procedures, the proposed methods will target several objectives, such as reducing the volume of data transported through the network while keeping privacy and integrity, detecting anomalies or events in the collected data to provide early alarms and notifications, and facilitating the operation of services across several SN domains. In more detail, the first objective is to develop methods to reduce the volume of collected sensor data through statistical and AI-based methods for data compression and sampling rate manipulation. We proposed both statistical-based and Autoencoder (AE)-based approaches for compression, as wells as sampling rate adaptation method that works with either. Simulations of implemented algorithms on real world datasets showed a significant ability to reduce the volume of the data, reaching 1% of its original size in some cases, and leading the reduced energy consumption in the factor of one-tenth in case of sensors with limited energy availability. The second objective is to develop methods for maintaining data veracity through employing AI-based anomaly detection methods at multiple levels of the network. Anomalies may arise due to a range of factors from faulty sensors to malicious attacks and detecting them can facilitate timely actions to avoid or mitigate their effect. We proposed two AE-based methods: one operating at the sensor level, and the other on the network level. Simulations of implemented algorithms on real world datasets showed more than 90% of accuracy in detecting anomalies in single sensor data analysis. Moreover, prompt detection of subtle anomalies spanning multiple sensors that could not be detected by single sensor data analysis was achieved. Finally, the third objective is to investigate methods to improve multi-domain DT systems management and coordination through KT while preserving the privacy of each individual DT. We proposed an AE-based knowledge extraction method that extracts codified information about the state of the sharing DT and sends it to the target DT. The method showed that the target DT is able to use the codified and private information about the state of the sharing DT before the changes are apparent through their effect on its system. |
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