{"id":{"repo_id":"london-metro","oai_identifier":"oai:repository.londonmet.ac.uk:11511"},"canonical_url":"https://search.dev.ndltd.org/etd/london-metro/oai:repository.londonmet.ac.uk:11511","repository":{"repo_id":"london-metro","name":"London Metropolitan University","base_url":"https://repository.londonmet.ac.uk/cgi/oai2"},"display":{"title":"Selective prioritisation of real-time IP packets to improve quality of service in 5G wireless sensor networks","abstract":"The successful deployment of 5G and future-generation networks depends critically on the provision of Ultra-Reliable Low-Latency Communication (URLLC), which is vital to meeting the stringent requirements of emerging real-time applications. However, the static, rule-based scheduling algorithms traditionally used for Quality of Service (QoS) management are ill-equipped to handle the dynamic, stochastic nature of modern wireless environments. This thesis addresses this critical gap by designing, implementing, and empirically validating a novel, multi-stage Artificial Intelligence (AI) framework that enables a transition from passive network analysis to active, autonomous control. The central research question investigates whether a hierarchical suite of Machine Learning (ML), Deep Learning (DL), and Deep Reinforcement Learning (DRL) techniques can dynamically manage real-time traffic to significantly enhance QoS in 5G Wireless Sensor Networks (WSNs). The research methodology unfolds in three logical stages, validated within a live network testbed. First, supervised learning techniques – including regression and classification models – are used to quantitatively evaluate and “fingerprint” traditional schedulers, establishing a robust performance baseline and confirming that predictability is a critical QoS metric. Second, the investigation proceeds to predictive forecasting, where a comparative analysis reveals that a tuned RF model leveraging rich instantaneous metrics outperforms complex sequential DL architectures for predicting next-step network delays. The culmination of this research work is the development of an autonomous scheduling agent. By framing the network scheduling as a Markov Decision Process (MDP), a novel Transformer-based RL agent is trained online. The findings demonstrate that this agent can learn an optimal policy in real time, achieving 100% scheduling success with ultralow latency and closing the loop from analysis to control. Collectively, this research contributes a comprehensive, validated AI-driven framework for intelligent QoS management. It provides a practical roadmap for evolving from static network configurations to adaptive, self-learning systems. The results confirm that the proposed hierarchy of AI techniques can successfully navigate the complex trade-offs in 5G WSNs, significantly outperforming traditional methods and paving the way for truly autonomous network control.","abstract_html":"The successful deployment of 5G and future-generation networks depends critically on the provision of Ultra-Reliable Low-Latency Communication (URLLC), which is vital to meeting the stringent requirements of emerging real-time applications. However, the static, rule-based scheduling algorithms traditionally used for Quality of Service (QoS) management are ill-equipped to handle the dynamic, stochastic nature of modern wireless environments. This thesis addresses this critical gap by designing, implementing, and empirically validating a novel, multi-stage Artificial Intelligence (AI) framework that enables a transition from passive network analysis to active, autonomous control. The central research question investigates whether a hierarchical suite of Machine Learning (ML), Deep Learning (DL), and Deep Reinforcement Learning (DRL) techniques can dynamically manage real-time traffic to significantly enhance QoS in 5G Wireless Sensor Networks (WSNs). The research methodology unfolds in three logical stages, validated within a live network testbed. First, supervised learning techniques – including regression and classification models – are used to quantitatively evaluate and “fingerprint” traditional schedulers, establishing a robust performance baseline and confirming that predictability is a critical QoS metric. Second, the investigation proceeds to predictive forecasting, where a comparative analysis reveals that a tuned RF model leveraging rich instantaneous metrics outperforms complex sequential DL architectures for predicting next-step network delays. The culmination of this research work is the development of an autonomous scheduling agent. By framing the network scheduling as a Markov Decision Process (MDP), a novel Transformer-based RL agent is trained online. The findings demonstrate that this agent can learn an optimal policy in real time, achieving 100% scheduling success with ultralow latency and closing the loop from analysis to control. Collectively, this research contributes a comprehensive, validated AI-driven framework for intelligent QoS management. It provides a practical roadmap for evolving from static network configurations to adaptive, self-learning systems. The results confirm that the proposed hierarchy of AI techniques can successfully navigate the complex trade-offs in 5G WSNs, significantly outperforming traditional methods and paving the way for truly autonomous network control.","abstract_has_math":false,"creators":["Naval Dos Santos, Orlando"],"institution":"London Metropolitian University","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Virdee, Bal Singh","Zamankhani, Shahram Salek"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07","date_published":"2025-07","updated_at":"2026-07-24T02:54:49Z","subjects":["000 Computer science, information & general works"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.grantnumber","label":"Dc Identifier Grantnumber","values":["N/A"],"render_values":[{"text":"N/A","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Virdee, Bal Singh","Zamankhani, Shahram Salek"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["London Metropolitan University"]},{"key":"dc:creator","label":"Author","values":["Naval Dos Santos, Orlando"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07"]},{"key":"dc:date.issued","label":"Date","values":["2025-07"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Computing and Digital Media (SCDM)","School of Computing and Digital Media"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["London Metropolitian University"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://repository.londonmet.ac.uk/11511/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["000 Computer science, information & general works"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.grantnumber","label":"Dc Identifier Grantnumber","values":["N/A"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://repository.londonmet.ac.uk/11511/1/Orlando-Naval-Dos-Santos_09026441.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The successful deployment of 5G and future-generation networks depends critically on the provision of Ultra-Reliable Low-Latency Communication (URLLC), which is vital to meeting the stringent requirements of emerging real-time applications. 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