With various industrial wireless networks greeting booming development, modern industrial devices configured with several network interfaces increasingly become the norm. Such multihomed industrial devices can increase application throughput by making use of multiple network paths, enabled by the multipath transmission control protocol (MTCP) (MPTCP). However, MPTCP might be challenged in the heterogeneous industrial networks because concurrent transmitting industrial application data over asymmetric network paths with different delays is almost bound to the receive buffer blocking problem, which is caused by out-of-order packet arrival and is harmful to the performance of the multipath transmission. The existing MPTCP solutions generally use static mathematical models to evaluate path quality and prohibit transmission on paths with poor quality, which are unable to perform efficiently under highly dynamic and complex network environments. Therefore, in this article, we propose a learning-driven latency-aware MPTCP variant, called ${l}\,^2$ -MPTCP, which seeks to possibly mitigate the out-of-order packet arrival and receive buffer blocking problems associated with the network heterogeneity in the industrial Internet. ${l}\,^2$ -MPTCP accurately computes each MPTCP path’s forward delay and assigns application data to multiple paths according to their calculated forward delay differences by using a novel multiexpert learning-enabled forward delay estimator. ${l}\,^2$ -MPTCP dynamically manages path usage and chooses the optimal path collection for bandwidth aggregation and multipath transmission by using a promising reinforcement learning-empowered multipath manager. Experimental results demonstrate that ${l}\,^2$ -MPTCP outperforms the current MPTCP solutions in terms of multipathing service quality.

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 5G millimeter wave (mmWave) application, ultradense cellular networks are gradually becoming one of the core characteristics of 5G cellular networks. In the edge computing environment, considering load balancing among edge nodes is beneficial to slow down the process of distributed denial of service (DDoS) attack. However, most existing studies have given less consideration to congestion in the multiuser and multiedge server models. Someone who uses the M/M/1 model also seems to ignore the effect of scheduling algorithms on the Markov property of the task arrival process. In this manuscript, based on ensuring the quality of experience (QoE) for users, the G/M/1 model is introduced to the task scheduling of edge servers for the first time to improve load balancing between edge servers. For the multi armed bandit (MAB) algorithm framework, specific metrics are established to quantify the degree of its equilibrium. The number of users assigned to the edge nodes and each edge nodes processing of specific tasks is taken into account. We experimentally evaluated its performance against two baseline approaches and three state-of-the-art approaches on a real-world dataset. And the experimental results validate the effectiveness of this method.

With the development of 5G technology, ultra-dense cellular networks are becoming a trend, while the deployment of large-area and high-density base stations (BSs) will bring new energy consumption problems. In this paper, we explore the energy consumption of the edge demand response (EDR) from the two perspectives of edge users and edge facility providers. While guaranteeing the essential quality of experience (QoE), we try to improve the load balancing of edge servers and reduce system energy consumption. On the edge facility provider side, we mainly consider the physical machine turn-on problem and resource allocation of the two-phase EDR. On the user side, we start with user cost reduction and sub-channel power allocation to emphasize the QoE. Firstly, considering the density impacts of users and edge servers, we take inspiration from the classical PageRank algorithm and propose a method to calculate the edge nodes’ weights and thus determine the infrastructure’s state. Subsequently, combining distance factors, we design a sub-channel power allocation method based on dynamic planning for 5G power domain multiplexing non-orthogonal multiple access (PDM-NOMA). More importantly, based on the above work, we optimize the two-phase EDR process based on the upper confidence bound (UCB) algorithm of the multi-armed bandit (MAB) algorithm framework and dynamic planning. We compare the proposed QEL-UCB algorithm with two classical and five state-of-the-art algorithms on a real-world dataset. The experimental results demonstrate that the proposed method improves by 18.52% in load balancing and reduces by 18.59% in energy consumption, which validates the method’s effectiveness.

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.