Promoted by the advancements in the various wireless access technologies, modern mobile devices equipped with multiple network interfaces are rapidly becoming the norm, and this provides a driving force for the large-scale deployment of the Multipath Transmission Control Protocol (MPTCP) in the current and future Internet. However, the simultaneous use of multiple network paths for concurrent multipath data transmission can make MPTCP have a larger attack surface than the traditional single-path transport protocols, and this may be likely to pose a risk of MPTCP being much more susceptible to cyber attacks. In this paper, we present a measurement method to investigate the vulnerability and robustness of MPTCP under cyber attacks with incomplete network information, by considering the fact that most cyber attacks normally lack of real-time information with respect to various MPTCP attributes. We mathematically characterize cyber attacks with incomplete network information from the viewpoints of both the cyber attacker and the MPTCP communication system, and then we introduce a mixed attack strategy, by jointly considering the features of both the random attacks and the selective attacks, to evaluate the robustness of MPTCP.

With the rapid development of wireless networks, multiple network interfaces are gradually being designed into more and more mobile devices. When it comes to data delivery, Stream Control Transmission Protocol (SCTP)-based Concurrent Multipath Transfer (CMT) has proven to be quite useful solution for multiple home networks, and it could become the key transport protocol for the next generation of wireless communications. The CMT delay caused by data rearrangement has been noticed by researchers, but they have seldom considered the frequent occurrence of packet loss that occurs in the high-loss networks. In this paper, we proposed an original loss-aware solution for multipath concurrent transmission (CMT-LA) that achieves the following goals: (1) identifying packet loss on all paths, (2) distributing packets adaptively across multiple available paths according to their packet loss and loss variation, and (3) maintaining the features of bandwidth aggregation and parallel transmission of CMT while improving the throughput performance. The results of our simulations showed that the proposed CMT-LA reduces reordering delay and unnecessary fast retransmissions, thereby demonstrating that CMT-LA is a more efficient data delivery scheme than classic CMT.

With the advancement of multimedia technologies and driven by the ever-increasing user interests in the variety of multimedia applications, the content-rich multimedia streaming services tend to be the most attractive service in future Internet. The multipath TCP (MPTCP), which uses multiple paths for parallel transmission and bandwidth aggregation, is considered to be the most potential transfer mechanism to satisfy the specific requirements of multimedia transmission in a multi-homed wireless network environment. However, the fully-reliable transmission nature of MPTCP can cause an unnecessary retransmission of expired multimedia data, block message from handing over to upper layer, and thus degrade the user-perceived multimedia service quality. In this paper, we propose a receiver-centric partial-reliable multipath transport solution (referred to as $rec$ PR-MPTCP) to support real-time Internet multimedia applications. In the $rec$ PR-MPTCP, a partial reliability extension is presented which runs at receiver side in order to offer a partial reliability multipathing service for multimedia applications. In addition, a new one-way delay-based bandwidth aggregation method is included into the $rec$ PR-MPTCP in order to reduce the receive buffer blocking problem in MPTCP and increase multimedia services performance. Simulation results show that $rec$ PR-MPTCP outperforms the current MPTCP solutions in terms of throughput performance and user-perceived quality of service.

Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive‐weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low‐dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

Using Weather Research and Forecasting (WRF) to simulate water vapor is one of the efficient and reliable methods of current InSAR atmospheric correction. However, its preprocessing is complicated and requires a higher computing environment, and its running time is longer. The above-mentioned characteristics limit the use of WRF. Different initializations have a non-negligible impact on the running time and simulation results. We use ERA5 and FNL to drive WRF to simulate water vapor and the results are used for tropospheric correction in the Beijing region. The comparative evaluation with MERIS and GACOS proved the reliability of WRF with ERA5 as the initialization for InSAR atmospheric correction. The high time resolution of ERA5 effectively shortens the simulation time of WRF and provides the possibility for its atmospheric correction application in time series InSAR.