Improvement of SCTP congestion control in the LTE-A network

Najm, Ihab Ahmed; Ismail, Mahamod; Lloret, Jaime; Ghafoor, Kayhan Zrar; Zaidan, B. B.; Rahem, Abd Alrazak

doi:10.1016/j.jnca.2015.09.003

Cited by 14 publications

(13 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies have mentioned that conventional SCTP congestion control algorithms over Long Term Evolution-Advanced (LTE-A) present poor performance in terms of various factors, such as congestion window size (cwnd), throughput (throu), and send/receive/lose packets. Thus, Najm et al [30] proposed an improvement mechanism called IMP-SCTP for 4G networks based on multi-criteria decision making (MCDM). Meanwhile, Najm et al [31] described the conventional SCTP congestion control mechanism over a high-bandwidth network such as 4G as having weak performance through a hands-on and systemic review related to SCTP congestion control.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

Self Cite

View full text Add to dashboard Cite

The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

show abstract

Section: Related Workmentioning

confidence: 99%

“…It was assumed that when cwnd ≤ ssthresh, cwnd would subsequently grow apiece ACKed in accordance with a specific interval. The details of the derived model and the visualization of the mathematical construction are mentioned in [30].…”

Section: Modeling and Mathematical Formulationmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The frequency band as described in Figure 2 CH172-5.860 GHz and CH184-5.920 GHz both are safety dedicated channels [32]. The first one provides a serious security solutions while the second plays a protective role against congestion on other channels [33]. Channel CH178-5.890GHz is a control channel responsible for controlling the transmission broadcast and link establishment.…”

Section: Ieee 80211pmentioning

confidence: 99%

Vehicular Ad Hoc Networks: Growth and Survey for Three Layers

Saad¹,

Rahem²,

Hussein³

et al. 2017

IJECE

View full text Add to dashboard Cite

A vehicular ad hoc network (VANET) is a mobile ad hoc network that allows wireless communication between vehicles, as well as between vehicles and roadside equipment. Communication between vehicles promotes safety and reliability, and can be a source of entertainment. We investigated the historical development, characteristics, and application fields of VANET and briefly introduced them in this study. Advantages and disadvantages were discussed based on our analysis and comparison of various classes of MAC and routing protocols applied to VANET. Ideas and breakthrough directions for inter-vehicle communication designs were proposed based on the characteristics of VANET. This article also illustrates physical, MAC, and network layer in details which represent the three layers of VANET. The main works of the active research institute on VANET were introduced to help researchers track related advanced research achievements on the subject. Keyword: MAC INTRODUCTIONRoad accidents have become a global public safety issue. Accidents caused by strong mass destruction have been became the number one killer in the world. Vehicle networking in an intelligent transportation system provides an important foundation to resolve this issue. Vehicular ad hoc networks (VANETs) are self-organizing networks dedicated to inter-vehicle communication and design. VANET utilizes mobile ad hoc network (MANET) technology to instigate inter-vehicle communication [1] which allows the driver to obtain the status information of other vehicles (e.g., BVR speed range, direction, position, and brake pedal pressure) and real-time traffic dispatch.The design goal of VANET is to establish an inter-vehicle communication platform that improves traffic efficiency, safety, and reliability, as well as provides convenience and multiple access to drivers and improves passenger comfort [2], [3] During the 2003 ITU-T Telecommunication Standardization Conference on cars, national car experts proposed to reduce traffic accident by 50% [4] Over 90% of the deaths occur in low-income and middle-income countries, which have only 48% of the world's vehicles, according to the Global status report on road safety more than 1.2 million people die in road accidents every year. A wireless ad hoc network is a distributed architecture with emphasis on hopping (routing), self-organization, and decentralization. VANET is defined as a fast-moving outdoor communication network [5]. It is also referred to as a self-organized traffic information system. The basic idea behind VANETs is the exchange of data and

show abstract

“…The MaxLW-DG Network Protocols and Algorithms ISSN 1943-3581 2015 algorithm discussed in this paper can serve as a benchmarking algorithm to evaluate the relative performance of the distributed protocol. The MaxBNW-DG and MaxLW-DG algorithms can also be used in conjunction with some of the recently proposed congestion control algorithms (e.g., [21]).…”

Section: Related Workmentioning

confidence: 99%

A Generic Algorithm to Determine Maximum Bottleneck Node Weight-based Data Gathering Trees for Wireless Sensor Networks

Meghanathan¹

2015

NPA

View full text Add to dashboard Cite

We propose a generic algorithm to determine maximum bottleneck node weight-based data gathering (MaxBNW-DG) trees for wireless sensor networks (WSNs) and compare the performance of the MaxBNW-DG trees with those of maximum and minimum link weight-based data gathering trees (MaxLW-DG and MinLW-DG trees). Assuming each node in a WSN graph has a weight, the bottleneck weight for the path from a node u to the root node of the DG tree is the minimum of the node weights on the path (inclusive of the weights of the end nodes). The MaxBNW-DG tree algorithm determines a DG tree such that each node has a path of the largest bottleneck weight to the root node. We observe the MaxBNW-DG trees to incur lower height, larger percentage of nodes as leaf nodes and a larger weight per intermediate node compared to the leaf node; the tradeoff being a larger a network-wide data aggregation delay due to larger number of child nodes per intermediate node. The MaxBNW-DG algorithm could be used to determine DG trees with larger trust score, larger energy (and other such criterion for node weight) per intermediate node compared to the leaf node.

show abstract

Improvement of SCTP congestion control in the LTE-A network

Cited by 14 publications

References 38 publications

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Vehicular Ad Hoc Networks: Growth and Survey for Three Layers

A Generic Algorithm to Determine Maximum Bottleneck Node Weight-based Data Gathering Trees for Wireless Sensor Networks

Contact Info

Product

Resources

About