Examining spatiotemporal crowdsensing and caching for population-dynamic OTT content delivery

This study proposes a novel spatiotemporal crowdsensing and caching (SCAC) framework to address the surging demands of urban wireless network traffic. In the context of rampant urbanization and ubiquitous digitization in cities, effective data traffic management is crucial for maintaining a dynamic urban ecosystem. Leveraging user mobility patterns and content preferences, this study formulates an offloading policy to alleviate congestion across urban areas. Our approach uses an AI-based method at the cell level, providing a practical and scalable solution that can be readily adapted to bustling metropolitan areas. The implementation of our model demonstrated its effectiveness in reflecting real-world urban dynamics, resulting in significant reductions in peak-hour traffic and robust performance across diverse urban settings. The deployment strategy initiates from densely populated transportation hubs, gradually expanding to broader urban areas. This systematic expansion adheres to a policy framework that emphasizes data privacy and sustainable urban development, ensuring alignment with societal needs and regulatory frameworks. By addressing technological efficacy and societal impact, this study enhances the understanding of urban wireless traffic management. It offers mobile network operators, policymakers, and urban planners a comprehensive strategy to harness the potential of spatiotemporal technology, thereby ensuring that cities remain dynamic, efficient, and well-prepared for the future of digital connectivity.