Link quality prediction for multimedia streaming based on available bandwidth and latency

This paper quantifies the impact of topological characteristics on the performance of single radio multichannel IEEE 802.11 mesh networks. Topological characteristics are the number of nodes per subnetwork, the hop count, the neighbor node density, the hidden nodes, the number of nodes in the neighborhood of the gateway, and the hidden nodes in the neighborhood of the gateway. Network performance metrics are throughput, fairness and delay. The data mining Support Vector Machine (SVM) model was used to extract the relationships between the network topology metrics and the network performance metrics based on data results obtained through ns-2 simulation of random networks. The results obtained can be used as a basis to design channel assignment algorithms or to aid the deployment and management of single radio wireless mesh networks.

Several active queue management (AQM) schemes have been proposed to address the bufferbloat issue, a long network delay and performance degradation issue caused by excess buffering of packets in network nodes. The solutions proposed up to now, however, satisfy only a subset of the key network performance metrics, i.e., end-to-end throughput, throughput fairness, and delay, in 802.11s-based multi-radio multi-channel wireless mesh networks (MRMC WMN). In this letter, we propose FD-AQM, a fairness-aware delay-controlled AQM to satisfy all three key network performance metrics simultaneously in the 802.11s-based MRMC WMN. The proposed FD-AQM achieves the goal by adopting a per-queue drop interval which is recalibrated according to the average round-trip time of flows, and a drop decision mechanism which considers average throughput as well as target delay. Through simulations, we show that FD-AQM is superior to other AQM schemes in terms of throughput and fairness.

With the rapid development of Internet of Things (IoT) technology, the number of nodes in wireless sensor networks (WSNs) is explosively increasing, and the scale of network is increased gradually. Traditional single-layer non-clustering network is no longer suitable for current WSNs, which results in high maintenance cost and fast deterioration of network performance. By analyzing the impact of existing static and dynamic clustering schemes on network performance, it is concluded that additional factors need to be considered to improve the overall performance of the network, such as residual energy of nodes, number of neighbor nodes and load balancing. Therefore, an adaptive multi-layer clustering networking strategy based on capability weights is proposed. Based on the real-time changes of each cluster density, node load and residual energy, the node capacity weights are updated dynamically according to the actual network performance, then the cluster heads are renewed adaptively. By comparing the performance metrics in the experiments, proposed strategy can effectively reduce the load of key nodes such as cluster head, and improves the network performance metrics such as average transmission delay, average transmission hops and load balancing.

Enhancement of wireless sensor network lifetime by deploying heterogeneous nodes

A mobile ad hoc network (MANET) is a self-organizing network of mobile nodes which can form a dynamic topology. All the nodes generally have a limited transmission range and move freely throughout the network. So implementing a good routing protocol to react the link state changing is a critical issue in current research. From now on, a popular method is introducing a link quality evaluation (LQE) mechanism in routing algorithm. In this paper, we study the link quality evaluation scheme based on traffic-based metrics, such as packet reception ratio (PRR), and find two flaws (one is the assessment lagging and the other is the sample bias) which result in large handoff delay in routing protocols, especially when the PRR-like scheme is used in mobile environments. Therefore, we propose a link quality prediction (LQP) model to sense the link state by analyzing the Signal to Noise ratio of the neighbor link, and using grey theory to tackle the small sample size problem. Finally we set up mobile nodes test-bed to validate the scheme. Based on comparison of previous schemes, the LQP's performance is far better in handoff delay and packet loss rate.

Efficient routing in wireless sensor networks entails the establishment of high quality links. Recent research has shown that metric-based routing, such as ETX [6]can significantly improve routing performance by tracking various link-quality metrics. Such metrics, however, may fail to capture link quality at relatively high traffic rates. This poster describes how machine learning techniques can be leveraged to help estimate link quality in those adverse scenarios. We also present MetricMap, a metric-based protocol using our learning-enabled link quality assessment method. Experimental results on MoteLab show that MetricMap can achieve up to 400% improvement on data delivery rate in a high traffic rate application, with no negative impact on other performance metrics, such as data latency.

Real-time and accurate link quality prediction is essential for wireless sensor networks. In order to improve the agility of link quality prediction, physical layer parameters such as signal-to-noise ratio (SNR) were used directly to evaluate the link quality so as to avoid taking a long time to compute packet reception ratio. However, existing link quality prediction mechanisms often ignore the temporal correlation of physical layer parameter sequences when predicting these parameters. In response to this problem, this paper proposes RNN-SNR, a more effective link quality prediction mechanism utilizing the short-term memory characteristics of recurrent neural network. This mechanism is able to mine the internal relationship of SNR sequences more accurately, so it could reduce the prediction error effectively. Compared with the similar mechanism, RNN-SNR has higher prediction accuracy under different link qualities. Especially under moderate links, the prediction error is reduced by 21.43%.

In 5G networks, time-series data will be omnipresent for the monitoring and management of network performance metrics. With the increase in the number of Internet of Things (IoT) devices, it is expected that the number of real-time time-series data streams will increase at a fast pace, making forecasting essential for the proactive successful management of the network. In this article, we discuss to use both linear and non-linear forecasting methods, including machine learning, deep learning, and neural networks to improve 5G networks’ management. For this purpose, we design and implement a real-time distributed forecasting framework, used to make simultaneous predictions of different network performance metrics, and with different learning algorithms. By using our framework, we compare the use of forecasting methods in two network scenarios, in a real vehicular network and in a 4G network, representing two different slices in a 5G network. We also integrate our framework in a 5G architecture. Using the best forecasting models assessed previously, we propose a dynamic threshold algorithm for multi-slice management, to ensure that the resources of each slice are updated according to the slices’ needs, while avoiding congestion and saving resources for other slices. The experimental results show that it is possible to forecast the slices’ needs and congestion probability, selecting the best forecasting approach or an ensemble of the best ones, and act accordingly in the network to optimize its management.

A GRASP-based network re-optimization strategy for improving RWA in multi-constrained optical transport infrastructures

this paper studies the improvement of Network’s performance using different quality of service (QoS) mechanisms. Classification and marking, Congestion Management, congestion avoidance, policing and shaping QoS mechanisms were applied into a simulated network through five scenarios. Network performance metrics such as delay, jitter, and packet loss were measured afterwards. The implementation done has had great impact on improving network’s performance as delay was decreased to 38msec and jitter to 25msec.

It is difficult to manage traditional networks due to network growth, an increase in user numbers, and the emergence of new technologies like big data and cloud computing. Consequently, it is required to alter the current network design. SDN is a well-liked architecture because it offers customizable control and centralized management in data centers. To achieve scalability and dependability, it was necessary to propose the geographical dispersal of a logically centralized control plane due to the huge scale of networks. Software-defined networking (SDN) load-balancing optimization has been studied for a long time. Many approaches to the load-balancing conundrum have been put forth by researchers, but very few have taken the impact of transmission delay between controllers and switches under heavy network load into account. In this article we elaborate the various techniques of load balancing in SDN those working on different areas like multi-controller, switch migration, multi-agent, strategy, time sharing, prediction, reinforcement learning etc. We discuss the methods used along with the advantages and disadvantages of various costs, service quality, and network performance metrics.

In recent years, with the rapid development of network applications and the increasing demand for high-quality network service, quality-of-service (QoS) routing has emerged as a critical network technology. The application of machine learning techniques, particularly reinforcement learning and graph neural network, has garnered significant attention in addressing this problem. However, existing reinforcement learning methods lack research on the causal impact of agent actions on the interactive environment, and graph neural network fail to effectively represent link features, which are pivotal for routing optimization. Therefore, this study quantifies the causal influence between the intelligent agent and the interactive environment based on causal inference techniques, aiming to guide the intelligent agent in improving the efficiency of exploring the action space. Simultaneously, graph neural network is employed to embed node and link features, and a reward function is designed that comprehensively considers network performance metrics and causality relevance. A centralized reinforcement learning method is proposed to effectively achieve QoS-aware routing in Software-Defined Networking (SDN). Finally, experiments are conducted in a network simulation environment, and metrics such as packet loss, delay, and throughput all outperform the baseline.

The increasing demand for the coverage of high‐speed wireless local area networks (WLANs) is driving the installation of a very large number of access points (APs). Wireless mesh networks (WMNs) have emerged as a promising technology in the next generation networks to provide economical and scalable broadband access to wireless interconnection of multiple APs that manage individual WLANs in order to extend the coverage of the conventional single WLANs. In this paper, we develop a new analytical model to investigate the quality‐of‐service (QoS) performance metrics in WMNs interconnecting multiple WLANs. The model takes the phenomenon of communication locality into consideration. Extensive simulation experiments are conducted to validate the accuracy of the analytical model. The proposed model is then used to obtain the maximum achievable throughput, which is a key performance metrics in wireless networks. Moreover, we utilize the model to evaluate the impact of communication locality on network performance and investigate its effects on the optimization of integrated WLANs and multi‐hop mesh networks. Copyright © 2009 John Wiley & Sons, Ltd.

Το αντικείμενο της διατριβής αυτής είναι η μελέτη της διαστρωματικής (cross-layer) προσέγγισης ανάπτυξης ασύρματων δικτύων κατανεμημένης λειτουργίας με απαιτήσεις επικοινωνίας πραγματικού χρόνου και περιορισμένους διαθέσιμους πόρους. Επιπλέον βασικό στόχο αποτελεί και η σχεδίαση, πρόταση αντίστοιχης αρχιτεκτονικής η οποία στοχεύει στη βέλτιστη διαχείριση διαθεσίμων δικτυακών πόρων σε καταστάσεις συμφόρησης του δικτύου και κατά συνέπεια στην βελτίωση της απόδοσης αυτού. Μέσα από μελέτες στα πλαίσια της διατριβής αναδεικνύεται η σημασία του φαινομένου συμφόρησης ως κύριου παράγοντα σπατάλης δικτυακών πόρων καθώς και οδήγησης του δικτύου σε κατάσταση κορεσμού με αρνητική επίπτωση σε όλες τις παραμέτρους απόδοσης του δικτύου. Στόχος, λοιπόν, της προτεινόμενης επικοινωνιακής αρχιτεκτονικής είναι η αποφυγή του φαινομένου συμφόρησης έτσι ώστε το δίκτυο να οδηγείται δυναμικά σε ένα σταθερό σημείο απόδοσης (όσο αυτό είναι δυνατό) το οποίο θα επιτρέπει στο δίκτυο να αποδίδει όσο το δυνατόν καλύτερα αποφεύγοντας τη ίδια στιγμή άσκοπη σπατάλη πόρων. Βασική παράμετρος στη σχεδίαση αποτελεί η συμβατότητα τόσο ως προς σημαντικό εύρος διαθέσιμων πρωτοκόλλων σε διάφορα επίπεδα, όσο και ως προς τη δυνατότητα συνύπαρξης κόμβων που ενσωματώνουν την προτεινόμενη αρχιτεκτονική με κόμβους χωρίς αυτήν. Η προτεινόμενη αρχιτεκτονική υλοποιήθηκε στα πλαίσια γνωστού και αξιόπιστου δικτυακού εξομοίωση. Αξιολόγηση της υλοποίησης αυτής μέσα από μεγάλο αριθμό πειραμάτων έδειξε επίτευξη του στόχου καθώς το φαινόμενο της συμφόρησης αντιμετωπίστηκε σε όλες τις περιπτώσεις με σημαντικά οφέλη στην απόδοση του δικτύου και διαχείριση των πόρων. Επιπλέον, λόγω του μεγάλου βαθμού παραμετροποίησης αποτελεί ιδανική βάση για μελλοντικές προεκτάσεις.

We categorized VPN’s impact in to two different aspects; The affecting aspect, and the affected aspect. The affecting aspect of the impact encompasses factors such as security, algorithms, hardware, and software. Whereas the affected aspect is of the likes of network performance. While VPNs have managed to integrate security, one of the affecting aspect of the impact on one hand, on the other hand, VPNs should be regarded as a potential threat to network performance. In this study, for affordability purpose, we choose to use NS-2 simulated test-bed to shed light on the VPN’s performance impact in a network. Considering the most common network performance metrics, throughput and delay; we assessed these performance metrics by means of average and percentage changes theories. The findings emphasize quantitative impact on the TCP/IP throughput than on its counterpart UDP/IP. We finally developed an analytical equation to model this VPN’s performance impact.