With multiple network interfaces are being widely equipped in modern mobile devices, the Multipath TCP (MPTCP) is increasingly becoming the preferred transport technique since it can uses multiple network interfaces simultaneously to spread the data across multiple network paths for throughput improvement. However, the MPTCP performance can be seriously affected by the use of a poor-performing path in multipath transmission, especially in the presence of network attacks, in which an MPTCP path would abrupt and frequent become underperforming caused by attacks. In this paper, we propose a multi-expert Learning-based MPTCP variant, called MPTCP-meLearning, to enhance MPTCP performance robustness against network attacks. MPTCP-meLearning introduces a new kind of predictor to possibly achieve better quality prediction accuracy for each of multiple paths, by leveraging a group of representative formula-based predictors. MPTCP-meLearning includes a novel mechanism to intelligently manage multiple paths in order to possibly mitigate the out-of-order reception and receive buffer blocking problems. Experimental results demonstrate that MPTCP-meLearning can achieve better transmission performance and quality of service than the baseline MPTCP scheme.

Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.

Wireless Local Area Networks (WLANs) based on the IEEE 802.11 standard have been increasingly used. Access Points (APs) are being established in various public places, such as railway stations and airports, as well as private residences. Moreover, the rate of public WLAN services continues to increase. Throughput prediction of an AP in a multi-rate environment, i.e., predicting the amount of receipt data (including retransmission packets at an AP), is an important issue for wireless network design. Moreover, it is important to solve AP placement and selection problems. To realize the throughput prediction, we have proposed an AP throughput prediction method that considers terminal distribution. We compared the predicted throughput of the proposed method with a method that uses linear order computation and confirmed the performance of the proposed method, not by a network simulator but by the numerical computation. However, it is necessary to consider the impact of CSMA/CA in the MAC layer, because throughput is greatly influenced by frame collision. In this paper, we derive an effective transmission rate considering CSMA/CA and frame collision. We then compare the throughput obtained using the network simulator NS2 with a prediction value calculated by the proposed method. Simulation results show that the maximum relative error of the proposed method is approximately 6% and 15% for UDP and TCP, respectively, while that is approximately 17% and 21% in existing method.

We have proposed a quality of experience (QoE)-oriented wireless local area network (WLAN) to provide sufficient QoE to important application flows. Unlike ordinary IEEE 802.11 WLAN, the proposed QoE-oriented WLAN dynamically performs admission control with the aid of the prediction of a “loadable capacity” criterion. This paper proposes an algorithm for dynamic network reconfiguration by centralized control among multiple basic service sets (BSSs) of the QoE-oriented WLAN, in order to maximize the number of traffic flows whose QoE requirements can be satisfied. With the proposed dynamic reconfiguration mechanism, stations (STAs) can change access point (AP) to connect. The operating frequency channel of a BSS also can be changed. These controls are performed according to the current channel occupancy rate of each BSS and the required radio resources to satisfy the QoE requirement of the traffic flow that is not allowed to transmit its data by the admission control. The effectiveness of the proposed dynamic network reconfiguration is evaluated through indoor experiments with assuming two cases. One is a 14-node experiment with QoE-oriented WLAN only, and the other is a 50-node experiment where the ordinary IEEE 802.11 WLAN and the QoE-oriented WLAN coexist. The experiment confirms that the QoE-oriented WLAN can significantly increase the number of traffic flows that satisfy their QoE requirements, total utility of network, and QoE-satisfied throughput, which is the system throughput contributing to satisfy the QoE requirement of traffic flows. It is also revealed that the QoE-oriented WLAN can protect the traffic flows in the ordinary WLAN if the border of the loadable capacity is properly set even in the environment where the hidden terminal problem occurs.