🎓PhD Studies@BIT (2016-2023)
During my PhD tenure in BIT, I worked on several innovative algorithms and solutions. Specially we achieve significant progress in optimizing Software-Defined Information-Centric Networking (SD-ICN). Our research results have made important contributions to the fields of the Internet of Things (IoT), Vehicular Ad-hoc Networks (VANETs), and Flying Ad-hoc Networks (FANETs), focusing on enhancing Quality of Service (QoS), optimizing resource utilization efficiency, and improving network reliability and security.
We innovate a novel SD-ICN solution for FANETs, which performs data dissemination and delivery through an analytical optimization model, emphasizing traffic classification and coordination between UAVs, sensors, and control units. The solution, incorporating advanced optimization algorithms, effectively addresses data transmission issues in high-mobility environments. Experimental analysis shows that this solution outperforms baseline SDN-based UAV implementations in terms of throughput, computational load, handover latency, packet loss, and end-to-end delay. This research not only enhances the overall performance of FANETs but also provides valuable reference points for future optimization of UAV networks.
We develop an innovative pure SD-ICN framework for IoT, optimizing real-time data delivery, effective caching, reducing overhead, and communication costs, significantly improving the overall performance of IoT networks. The proposed framework enhances the efficiency and reliability of data transmission among IoT devices by introducing new mechanisms for data discovery and dissemination. These improvements not only increase the overall performance of the network but also reduce overhead and costs during data transmission. We demonstrate significant performance improvements in throughput, flow processing rate, caching efficiency, packet loss, and communication overhead through ndnSIM (a variant of NS-3) evaluations.
We design a dynamic and reliable SD-ICN solution for IoV/VANETs. The optimized SDN data supports the ICN message structure, communication, and slicing model. This allows vehicles and other nodes to perform service-oriented tasks through OpenFlow-enabled SDN controllers. The solution leverages the hierarchical distributed architecture of 5G, deploying multiple controllers at the roadside, edge, and cloud to support V2V and V2I communication. Experimental results show that the model improves data delivery ratio, dissemination, retrieval, and caching efficiency, demonstrating its scalability and reliability. This research provides important technical support for the future optimization of intelligent transportation systems.
