Leveraging Mobility to Enhance IoT Applications - Liu, Fangqi (Proceedings / IEEE International Conference on Mobile Data Management, 2025)
The exponential growth of the Internet of Things has introduced significant challenges in managing vast volumes of data, particularly in urban and remote environments where network and infrastructure limitations are more pronounced. This dissertation investigates innovative solutions to these challenges by integrating mobile entities, such as public transit fleets and drones, into IoT systems to enhance mobile data management. The research focuses on optimizing data collection, transmission, and network infrastructure, particularly in timesensitive community IoT applications. Three key use cases are explored: leveraging public transit fleets to optimize data collection and network infrastructure in smart cities, deploying drones to enhance fire monitoring in high-rise buildings, and utilizing autonomous drone systems for remote wildland fire monitoring. The dissertation proposes novel algorithms for mobile entity deployment, flight planning, and real-time task generation, illustrating how mobile data management solutions can overcome the limitations of traditional IoT systems and improve the efficiency of data transmission in dynamic mobile sensing environments.