Deep Learning-Based Optimization of Network Routing Protocols for Smart Cities

Thesis Abstract

The rapid proliferation of interconnected devices and data-intensive applications in urban environments has accentuated the critical need for efficient, adaptive, and scalable network routing protocols within smart city infrastructures. Traditional routing algorithms often struggle to cope with the dynamic and heterogeneous nature of urban networks, leading to suboptimal performance metrics such as increased latency, data loss, and energy consumption. This study aims to develop a deep learning-based optimization framework to enhance the performance of network routing protocols in smart city networks, with a focus on addressing issues of scalability, reliability, and energy efficiency. The primary objectives include designing a deep neural network model capable of predicting optimal routing paths in real-time, evaluating the model's performance against existing routing protocols, and integrating the optimized routing mechanism into a simulated urban network environment to assess its practical viability. To achieve these objectives, a mixed-methods research design was employed, combining quantitative simulation experiments with qualitative analysis of system performance metrics. The population of the study comprises network nodes within a representative urban infrastructure modeled after a mid-sized European city, comprising approximately 10,000 interconnected devices categorized into sensors, traffic management systems, and public service networks. A stratified random sampling technique was used to select a sample of 1,200 nodes representing the diversity of network types and device functions. Data collection involved generating synthetic network traffic data through simulations in a controlled environment and training the deep neural network model using TensorFlow, with datasets comprising over 500,000 routing instances to capture varied network conditions. The analytical framework centered on training and validating the deep learning model through supervised learning, employing techniques such as cross-validation and hyperparameter tuning to optimize the model’s accuracy and generalizability. Performance evaluation was conducted using metrics including average routing delay, packet delivery ratio, energy consumption, and computational overhead, analyzed via regression analysis and ANOVA to determine statistical significance of improvements over conventional protocols. Additionally, sensitivity analysis assessed the robustness of the deep learning approach under varying network loads and failure scenarios. Expected findings suggest that the deep learning-enhanced routing protocol significantly outperforms traditional algorithms, achieving reductions of approximately 25% in average delay, 15% in energy consumption, and improvements in packet delivery ratios by over 10%. The results are anticipated to demonstrate the model’s adaptability to dynamic network conditions, confirming its potential to facilitate smarter, more resilient urban networks. It is anticipated that the study will contribute novel insights into the integration of artificial intelligence with network management systems, filling a gap in the current literature regarding the practical application of deep learning for real-time routing optimization in complex urban environments. The research concludes that the application of deep learning techniques offers a potent means of addressing the limitations of conventional routing protocols within smart city networks, fostering improvements in efficiency, reliability, and sustainability. Based on these findings, it is recommended that urban network planners and policymakers incorporate AI-driven routing solutions into the infrastructural planning process, along with further research into real-world deployment strategies, including scalability assessments and integration with emerging 5G and IoT technologies. Future studies should explore the extension of this framework to multimodal and heterogeneous network architectures, with emphasis on standardization and interoperability challenges. This research advances the knowledge frontier at the intersection of artificial intelligence, network engineering, and urban informatics, providing a scalable and adaptable pathway for smart city network design and management.