scholarly journals Enhanced Routing Algorithm Based on Reinforcement Machine Learning—A Case of VoIP Service

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 504
Author(s):  
Davi Ribeiro Militani ◽  
Hermes Pimenta de Moraes ◽  
Renata Lopes Rosa ◽  
Lunchakorn Wuttisittikulkij ◽  
Miguel Arjona Ramírez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Performance ◽  
Routing Algorithm ◽  
Variable Number ◽  
Performance Comparison ◽  
Routing Algorithms ◽  
Network Data ◽  
Dynamic Adjustment ◽  
Network Parameters ◽  
Control Message

The routing algorithm is one of the main factors that directly impact on network performance. However, conventional routing algorithms do not consider the network data history, for instances, overloaded paths or equipment faults. It is expected that routing algorithms based on machine learning present advantages using that network data. Nevertheless, in a routing algorithm based on reinforcement learning (RL) technique, additional control message headers could be required. In this context, this research presents an enhanced routing protocol based on RL, named e-RLRP, in which the overhead is reduced. Specifically, a dynamic adjustment in the Hello message interval is implemented to compensate the overhead generated by the use of RL. Different network scenarios with variable number of nodes, routes, traffic flows and degree of mobility are implemented, in which network parameters, such as packet loss, delay, throughput and overhead are obtained. Additionally, a Voice-over-IP (VoIP) communication scenario is implemented, in which the E-model algorithm is used to predict the communication quality. For performance comparison, the OLSR, BATMAN and RLRP protocols are used. Experimental results show that the e-RLRP reduces network overhead compared to RLRP, and overcomes in most cases all of these protocols, considering both network parameters and VoIP quality.

A Novel Routing Network Algorithm Via Neutrosphic Fuzzy Set Approach

2021 ◽  
pp. 9-22
Author(s):  
S.Krishna Prabha ◽  
◽  
Broumi said ◽  
Selçuk Topal ◽  
◽  
...  
Keyword(s):  
Public Transportation ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Routing Algorithms ◽  
Network Data ◽  
Main Function ◽  
Network Nodes ◽  
Web Interfaces ◽  
Telephone Networks ◽  
Set Up

Routers steer and bid network data, through packets that hold a variety of categories of data such as records, messages, and effortless broadcasts like web interfaces. The procedure of choosing a passageway for traffic in a network or between several networks is called routing. Starting from telephone networks to public transportation the principles of routing are applied. Routing is the higher-level decision-making that directs network packets from their source en route for their destination through intermediate network nodes by specific packet forwarding mechanisms. The main function of the router is to set up optimized paths among the different nodes in the network. An efficient novel routing algorithm is proposed with the utilization of neutrosophic fuzzy logic in this work addition to many routing algorithms for finding the optimal path in the literature. In this approach, each router makes its own routing decision in the halting time. Various concepts like routing procedures, most expected vector, most expected object, and list of estimated delays are explained.

scholarly journals Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5164
Author(s):  
Changsun Shin ◽  
Meonghun Lee
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Network Performance ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Optimal Solution ◽  
Link Quality ◽  
Wireless Sensor ◽  
Control Message

The swarm intelligence (SI)-based bio-inspired algorithm demonstrates features of heterogeneous individual agents, such as stability, scalability, and adaptability, in distributed and autonomous environments. The said algorithm will be applied to the communication network environment to overcome the limitations of wireless sensor networks (WSNs). Herein, the swarm-intelligence-centric routing algorithm (SICROA) is presented for use in WSNs that aim to leverage the advantages of the ant colony optimization (ACO) algorithm. The proposed routing protocol addresses the problems of the ad hoc on-demand distance vector (AODV) and improves routing performance via collision avoidance, link-quality prediction, and maintenance methods. The proposed method was found to improve network performance by replacing the periodic “Hello” message with an interrupt that facilitates the prediction and detection of link disconnections. Consequently, the overall network performance can be further improved by prescribing appropriate procedures for processing each control message. Therefore, it is inferred that the proposed SI-based approach provides an optimal solution to problems encountered in a complex environment, while operating in a distributed manner and adhering to simple rules of behavior.

scholarly journals AOCR: adaptive on chip router algorithm for multicore domain controller platform

2018 ◽  
Vol 7 (4) ◽  
pp. 2246
Author(s):  
T Shanmuganathan ◽  
U Ramachandraiah
Keyword(s):  
Energy Efficient ◽  
Cost Reduction ◽  
Network Performance ◽  
Rapid Development ◽  
Routing Algorithm ◽  
Routing Algorithms ◽  
Core Domain ◽  
Latency Reduction ◽  
Increasing Demand ◽  
On Chip

In the recent years, with the rapid development of semiconductor technologies and increasing demand for more effective multi-Core Domain Controller platforms, there is a clear demand for effective routing algorithms that can be used to route the packets between these platforms, while enhancing an on chip network performance, achieving a better latency and throughput. This paper proposes an adaptive on Chip Router algorithm with a simple adaptive routing algorithm based on runtime weighted arbitration and resource allocation methodology, where the routing decisions are minimized for applications-specific MDCU platforms. The proposed scheme is evaluated by simulations and its performance in terms of latency, area, power consumption and cost reduction per vehicle are presented. The results show that, 24.5% of latency reduction, 62.25% area utilization optimization and 63.76% of energy efficient compare with existing methods.  

scholarly journals DCAR: DTN Congestion Avoidance Routing Algorithm Based on Tokens in an Urban Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Hezhe Wang ◽  
Hongwu Lv ◽  
Huiqiang Wang ◽  
Guangsheng Feng
Keyword(s):  
Network Performance ◽  
Routing Algorithm ◽  
Relay Node ◽  
Routing Algorithms ◽  
Buffer Size ◽  
Destination Node ◽  
Delivery Ratio ◽  
Movement Trajectory ◽  
Average Delay ◽  
Urban Scenario

When a delay/disruption tolerant network (DTN) is applied in an urban scenario, the network is mainly composed of mobile devices carried by pedestrians, cars, and other vehicles, and the node’s movement trajectory is closely related to its social relationships and regular life; thus, most existing DTN routing algorithms cannot show efficient network performance in urban scenarios. In this paper, we propose a routing algorithm, called DCRA, which divides the urban map into grids; fixed sink stations are established in specific grids such that the communication range of each fixed sink station can cover a specific number of grids; these grids are defined as a cluster and allocated a number of tokens in each cluster; the tokens in the cluster are controlled by the fixed sink station. A node will transmit messages to a relay node that has a larger remaining buffer size and encounters fixed sink stations or the destination node more frequently after it obtains a message transmit token. Simulation experiments are carried out to verify the performance of the DCAR under an urban scenario, and results show that the DCAR algorithm is superior to existing routing algorithms in terms of delivery ratio, average delay, and network overhead.

A PERFORMANCE MODEL OF DISHA ROUTING IN K-ARY N-CUBE NETWORKS

2007 ◽  
Vol 17 (02) ◽  
pp. 213-228 ◽  
Author(s):  
A. KHONSARI ◽  
A. SHAHRABI ◽  
M. OULD-KHAOUA
Keyword(s):  
Network Performance ◽  
Routing Algorithm ◽  
Adaptive Routing ◽  
Routing Algorithms ◽  
Performance Model ◽  
Analytical Models ◽  
Traffic Conditions ◽  
Proposed Model ◽  
Fully Adaptive ◽  
Wormhole Routed

A number of analytical models for predicting message latency in k-ary n-cubes have recently been reported in the literature. Most of these models, however, have been discussed for adaptive routing algorithms based on deadlock avoidance, e.g. Duato's routing. Several research studies have empirically demonstrated that routing algorithms based on deadlock recovery offer maximal adaptivity that can result in considerable improvement in network performance. Disha is an example of a true fully adaptive routing algorithm that uses minimal hardware to implement a simple and efficient progressive method to recover from potential deadlocks. This paper proposes a new analytical model of Disha in wormhole-routed k-ary n-cubes. Simulation experiments confirm that the proposed model exhibits a good degree of accuracy for various networks sizes and under different traffic conditions.

scholarly journals Load Balancing Routing Algorithm of Low-Orbit Communication Satellite Network Traffic Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Tie Liu ◽  
Chenhua Sun ◽  
Yasheng Zhang
Keyword(s):  
Machine Learning ◽  
Loss Rate ◽  
Satellite Communication ◽  
Routing Algorithm ◽  
Routing Algorithms ◽  
Research Trend ◽  
Average Delay ◽  
Average Value ◽  
Distribution Index ◽  
Better Than

Satellite communication has become an important research trend in the field of communication technology. Low-orbit satellites have always been the focus of extensive attention by scholars due to their wide coverage, strong flexibility, and freedom from geographical constraints. This article introduces some technologies about low-orbit satellites and introduces a routing algorithm DDPG based on machine learning for simulation experiments. The performance of this algorithm is compared with the performance of three commonly used low-orbit satellite routing algorithms, and a conclusion is drawn. The routing algorithm based on machine learning has the smallest average delay, and the average value is 126 ms under different weights. Its packet loss rate is the smallest, with an average of 2.9%. Its throughput is the largest, with an average of 201.7 Mbps; its load distribution index is the smallest, with an average of 0.54. In summary, the performance of routing algorithms based on machine learning is better than general algorithms.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

scholarly journals Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 63
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Network Performance ◽  
Service Providers ◽  
Video Conferencing ◽  
Machine Learning Algorithms ◽  
Performance Parameters ◽  
High Definition ◽  
Internet Service ◽  
Novel Approach ◽  
5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

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