Secure data forwarding is critical for users to meet their requirements. In this paper, we propose D3 (Demon Detector in Data Plane), a source-driven, secure fault localization mechanism, which empowers the source to localize faulty link in Path Aware Networking, thus circumventing faulty link to guarantee secure data forwarding. D3 utilizes the source to instruct the on-path routers, thus empowering it to detect whether the on-path routers forward the packet as expected. Compared with existing schemes that are difficult to be deployed in practice due to the heavy storage, computation, and communication overhead, D3 offloads most of the on-path router's storage and computation overhead, thus dramatically improving the deployment efficiency. Particularly, the length of the additional packet header in D3 is 2-5 times less than the state-of-the-art mechanisms, thus having a low communication overhead. Besides that, the destination in D3 could keep stateless processing, thus having backward compatibility and eliminating the opportunity for DoS attacks toward a stateful destination. The BMv2 and Barefoot Tofino hardware evaluations show that D3 could achieve high fault localization accuracy and process the packet at line rate.

In pursuit of high-performance applications, the cloud is moving out of the data center and towards the edge. Secure data forwarding is critical for the users between the edge and the remote cloud. In this paper, we propose D3 (Demon Detector in Data Plane), a lightweight, secure fault localization mechanism, which can enable the users in the edge cloud to localize faulty links and thus avoid the faulty links to guarantee secure data forwarding along the path to the remote cloud. D3 utilizes the user to instruct the transit routers, thus empowering the user to detect whether the transit routers forward the packet as expected. Compared with existing schemes that are difficult to be deployed in practice due to the incurred heavy storage, computation, and communication overhead, D3 offloads most of the transit router’s storage and computation overhead, thus dramatically improving the deployment efficiency. Particularly, the length of the additional packet header in D3 is 2-5 times less than the state-of-the-art mechanisms, and the extra control packet overhead is ten times less while keeping a little constant storage overhead in the data plane. The evaluations in BMv2 and Barefoot Tofino hardware show that D3 could achieve high fault localization accuracy and efficiency.

Recent advances in high-speed rails (HSRs), coupled with user demands for communication on the move, are propelling the need for acceptable quality of experience (QoE) in high-speed mobility environments. However, with throughput declining significantly the QoE on existing HSRs is still far from satisfactory. In order to improve QoE on HSRs, this paper seeks to answer the question regarding which is better of two options: the selection of the best cellular carrier applying single-path TCP or the conjunction of multiple carriers applying Multi-path TCP (MPTCP). To this end, we carefully design comparison experiments using the two approaches on HSRs with a peak speed of 310 km/h. Measurement study on MPTCP performance shows that generally carrier conjunction gives similar performance as carrier selection. We take an in-depth analysis of the details of the instances, and for the first time expose the phenomenon called subflow degradation. We further confirm that subflow degradation of MPTCP occurs due to its poor adaptability to frequent handoffs. We believe these insights can provide valuable guidance for the design, implementation, and deployment of transmission protocols in high-speed mobility environments.

Deep learning-based models have achieved unprecedented performance in single image super-resolution (SISR). However, existing deep learning-based models usually require high computational complexity to generate high-quality images, which limits their applications in edge devices, e.g., mobile phones. To address this issue, we propose a dynamic, channel-agnostic filtering method in this paper. The proposed method not only adaptively generates convolutional kernels based on the local information of each position, but also can significantly reduce the cost of computing the inter-channel redundancy. Based on this, we further propose a simple, yet effective, deep lightweight model for SISR. Experiment results show that our proposed model outperforms other state-of-the-art deep lightweight SISR models, leading to the best trade-off between the performance and the number of model parameters.