Quality of service aware routing in software defined video streaming: a survey

The quality of service (QoS) parameters of IoT networks plays an important role in knowing the efficiency of an application. As the number of IoT users and devices is increasing and the number is envisaged to grow fast in the future, it has become extremely important to pay attention toward the QoS parameters for increasing the acceptability of the technology among the people. The IoT networks should be capable of handling devices of diverse nature and at the same time should provide wireless access to all of them. Artificial intelligence (AI) is one of the techniques to improve the QoS and has been used in this paper to know the change in the QoS parameters for networks with a varying number of nodes. The parameters studied in this paper are end-to-end delay, throughput, packet delivery ratio, and jitter and energy consumption. All the values have been calculated for a network with 30, 40, 50, 60, 70, 80, and 90 nodes. A comparison of QoS parameters by using AI and without AI has been explained. The results indicate that most of the parameters showed improvement in the values for all the sizes of network with the application of AI.

Improvement of QoS Parameters of IoT Networks Using Artificial Intelligence

AbstrakPerkembangan teknologi komunikasi Wireless untuk saat ini mengarah kepada teknologi berbasis jaringan IP (Internet Protocol). Sedangkan Access Point merupakan suatu perangkat dari Wireless Router.Â  Access point itu sendiri membentuk hostspot, sedangkan Wireless Router berfungsi untuk mengatur lalu lintas data. Parameter yang diukur pada penelitian ini yaitu mengukur parameter-parameter jaringan atau sering disebut dengan Quality of Service (QoS), antara lain pengukuran jitter, delay, throughput dan packet loss yang dihasilkan dalam layanan paket data pada wifi, penelitian yang dilakukan menggunakan wifi rumah yaitu internet indihome menggunakan modem huawei dengan paket layanan 10Mbps. Throughput dari hasil pengujian memiliki presentase sebesar 87,05%. Paket loss dari hasil pengujian memiliki presentase sebesar 0.0012770%, Nilai rata-rata delay yang diperoleh dari pengujian yaitu sebesar 12.7570 ms. Nilai rata-rata jitter yang diperoleh dari pengujian yaitu sebesar 0.0000000000047275 ms. Pengambilan dan pehitungan parameter QoS menggunakan software wireshark. Untuk mempermudah pengujian suatu jaringan dibutuhkan suatu simulator, salah satu simulator jaringan yang dapat digunakan adalah Graphical Network Simulator 3 (GNS3).Kata kunci: IP, QoS, WiFi, Wireshark, dan GNS3

pdf/analisis-trafik-jaringan-wifi-dan-simulasi-gns3-4h9k19u1wx.pdf

Analisis Trafik Jaringan Wifi dan Simulasi GNS3

Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error metrics such as the Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). However, these metrics present challenges including sensitivity to outliers (notably MSE and RMSE) and scale dependency, which complicates comparisons across different models. Additionally, traditional metrics sometimes yield values that are difficult to interpret across various problems. Consequently, there is a need for a metric that consistently reflects regression model performance, independent of the problem domain, data scale, and outlier presence. To overcome these shortcomings, this paper introduces a new regression accuracy measure based on the Hassanat distance, a non-convex distance metric. This measure is not only invariant to outliers but also easy to interpret as it provides an accuracy-like value that ranges from 0 to 1 (or 0–100%). We validate the proposed metric against traditional measures across multiple benchmarks, demonstrating its robustness under various model scenarios and data types. Hence, we suggest it as a new standard for assessing regression models’ accuracy. 

A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric

Internet of Things (IoT) becomes an emerging network technology that expedites billions of devices to be connected via the Internet to provide real-time intelligent application services. The benefits of Software-Defined Networking (SDN) can be used to fulfill IoT requirements. Quality of Service provisioning is an on-going demand in software-defined IoT (SD-IoT), particularly for large scale environments. In this paper, we address this issue by proposing a seamless model of AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in distributed fragmentation optimized SDN-IoT. Firstly, we propose a Multi-Criterion based Deep Packet Inspection method for classifying the network traffic, which is held in Edge Routers (access points). Secondly, we construct a Partially Connected Network Topology using the ISOMAP algorithm for an effective rule placement and routing. We propose a Traffic Differentiated QoS Routing for forwarding data packets via the most suitable switches. We select the optimum route by Deep Alternative Neural Network (DANN). Based on the relationships among switches, the path is selected and flow rules are deployed. The poor QoS is often caused by load imbalance in controllers and switches. To overwhelm this issue, we propose a Dynamic Offloading scheme in SD-IoT. We offload the data packets from the overloaded controller to the underloaded controller using Hassanat Distance-based Knearest neighbors (HDK-NN) algorithm. Similarly, we propose a Ranking-based Entropy function (R-Ef) to allow dynamic offloading among switches. Simulation is performed using the NS3.26 simulator and the results proved that our proposed AIaided SD-IoT model provides superior QoS performance compared to previous approaches.

/pdf/ai-aided-traffic-differentiated-qos-routing-and-dynamic-561881jc3w.pdf

AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

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AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT