scholarly journals Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?

2020 ◽  
Vol 10 (18) ◽  
pp. 6164
Author(s):  
Luis Diez ◽  
Alfonso Fernández ◽  
Muhammad Khan ◽  
Yasir Zaki ◽  
Ramón Agüero
Keyword(s):  
Machine Learning ◽  
Congestion Control ◽  
Arrival Time ◽  
Wireless Channel ◽  
Transport Layer ◽  
Transport Protocol ◽  
Length Variation ◽  
State Delay ◽  
Extensive Analysis ◽  
Buffer Length

It is well known that transport protocol performance is severely hindered by wireless channel impairments. We study the applicability of Machine Learning (ML) techniques to predict congestion status of 5G access networks, in particular mmWave links. We use realistic traces, using the 3GPP channel models, without being affected using legacy congestion-control solutions. We start by identifying the metrics that might be exploited from the transport layer to learn the congestion state: delay and inter-arrival time. We formally study their correlation with the perceived congestion, which we ascertain based on buffer length variation. Then, we conduct an extensive analysis of various unsupervised and supervised solutions, which are used as a benchmark. The results yield that unsupervised ML solutions can detect a large percentage of congestion situations and they could thus bring interesting possibilities when designing congestion-control solutions for next-generation transport protocols.

scholarly journals Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4821
Author(s):  
Rami Ahmad ◽  
Raniyah Wazirali ◽  
Qusay Bsoul ◽  
Tarik Abu-Ain ◽  
Waleed Abu-Ain
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Open Field ◽  
Network Lifetime ◽  
Detection Efficiency ◽  
Denial Of Service ◽  
Harmony Search ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Feature Selection Techniques

Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

Congestion Control: A Renaissance with Machine Learning

IEEE Network ◽  
2021 ◽  
pp. 1-8
Author(s):  
Wenting Wei ◽  
Huaxi Gu ◽  
Baochun Li
Keyword(s):  
Machine Learning ◽  
Congestion Control

scholarly journals A Model-based Scalable Reliable Multicast Transport Protocol for Satellite Networks

2017 ◽  
Vol 1 (1) ◽  
pp. 24
Author(s):  
Prawit Chumchu ◽  
Roksana Boreli ◽  
Aruna Seneviratne
Keyword(s):  
Congestion Control ◽  
Error Control ◽  
Forward Error Correction ◽  
Transmission Control Protocol ◽  
Window Size ◽  
Satellite Networks ◽  
Transport Protocol ◽  
Reliable Multicast ◽  
Auto Repeat Request ◽  
Control Part

In this paper, we design a new scalable reliable multicast transport protocol for satellite networks (RMT). This paper is the extensions of paper in [18]. The proposed protocoldoes not require inspection and/or interception of packets at intermediate nodes. The protocol would not require anymodification of satellites, which could be bent-pipe satellites or onboard processing satellites. The proposed protocol is divided in 2 parts: error control part and congestion control part. In error control part, we intend to solve feedback implosion and improve scalability by using a new hybrid of ARQ (Auto Repeat Request) and adaptive forward error correction (AFEC). The AFEC algorithm adapts proactive redundancy levels following the number of receivers and average packet loss rate. This leads to a number of transmissions and the number of feedback signals are virtually independent of the number of receivers. Therefore, wireless link utilization used by the proposed protocol is virtually independent of the number of multicast receivers. In congestion control part, the proposed protocol employs a new window-based congestion control scheme, which is optimized for satellite networks. To be fair to the other traffics, the congestion control mimics congestion control in the wellknown Transmission Control Protocol (TCP) which relies on “packet conservation” principle. To reduce feedback implosion, only a few receivers, ACKers, are selected to report the receiving status. In addition, in order to avoid “drop-to-zero” problem, we use a new simple wireless loss filter algorithm. This loss filter algorithm significantly reduces the probability of the congestion window size to be unnecessarily reduced because of common wireless losses. Furthermore, to improve achievable throughput, we employ slow start threshold adaptation based on estimated bandwidth. The congestion control also deals with variations in network conditions by dynamically electing ACKers.

scholarly journals Analisis Perbandingan Kinerja Protokol Transport Layer Pada Lalu Lintas Data VIOP dan IPTV

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Yuza Reswan ◽  
Dedy Agung Prabowo
Keyword(s):  
Transport Layer ◽  
Network Protocol ◽  
Test Time ◽  
Research Approach ◽  
Transport Protocol ◽  
Data Traffic ◽  
Advantages And Disadvantages ◽  
Sharing Time ◽  
Protocol Analyzer ◽  
Transport Layer Protocols

AbstractOne of the use of VOIP and IPTV technology that is increasingly used as a communication application on the internet are video call and video streaming, so when the data required a reliable and fast protocol performance in the process of delivery. The problem of the research is analyzing the performance of layer transport protocol in VOIP and IPTV data traffic. The objective of the research is to know the advantages and disadvantages of VOIP and IPTV data traffic and it is useful to contribute to the development of transport layer protocols in VOIP and IPTV data traffic in the future. This research is conducted independently without related to place and time. This reasearch uses SoftPerfect network Protocol Analyzer software. SoftPerfect network Protocol Analyzer is used to compare the performance of transport layer protocols of TCP, UDP, SCTV based on six parameters using different LAN and Wireless cable, test time, and sharing time. The method used in this research is based on the research approach. The results of this study indicate that SCTV protocol is a protocol that has good performance in VOIP and IPTV data traffic with TCP and UDP protocol.Keywords : Analysis, Performance, TCP, UDP, SCTP, VOIP, IPTV

scholarly journals Hybrid power control and contention window adaptation for channel congestion problem in internet of vehicles network

2022 ◽  
Vol 12 (1) ◽  
pp. 497
Author(s):  
Hayder M. Amer ◽  
Ethar Abduljabbar Hadi ◽  
Lamyaa Ghaleb Shihab ◽  
Hawraa H. Al Mohammed ◽  
Mohammed J. Khami
Keyword(s):  
Ad Hoc Networks ◽  
Power Control ◽  
Congestion Control ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Wireless Channel ◽  
Contention Window ◽  
Internet Of Vehicles ◽  
Hybrid Power ◽  
Hoc Networks

Technology such as vehicular ad hoc networks can be used to enhance the convenience and safety of passenger and drivers. The vehicular ad hoc networks safety applications suffer from performance degradation due to channel congestion in high-density situations. In order to improve vehicular ad hoc networks reliability, performance, and safety, wireless channel congestion should be examined. Features of vehicular networks such as high transmission frequency, fast topology change, high mobility, high disconnection make the congestion control is a challenging task. In this paper, a new congestion control approach is proposed based on the concept of hybrid power control and contention window to ensure a reliable and safe communications architecture within the internet of vehicles network. The proposed approach performance is investigated using an urban scenario. Simulation results show that the network performance has been enhanced by using the hybrid developed strategy in terms of received messages, delay time, messages loss, data collision and congestion ratio.

Hybrid machine learning detection for orbital angular momentum over turbulent MISO wireless channel

2020 ◽  
Vol 14 (22) ◽  
pp. 4116-4126
Author(s):  
Alaa ElHelaly ◽  
Mai Kafafy ◽  
Ahmed H. Mehanna ◽  
Mohamed M. Khairy
Keyword(s):  
Machine Learning ◽  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Wireless Channel ◽  
Hybrid Machine
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