Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET

Rahem, Abd Alrazak; Ismail, Mahamod; Najm, Ihab Ahmed; Balfaqih, Mohammed

doi:10.1007/s11235-016-0242-7

Cited by 10 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each node only needs to learn the location information of its neighbor and destination nodes to determine its own next node. This protocol type adapts to network size and topology changes, and is thus mainly used in automotive network [50][51]. The main representatives of this protocol are geographical source routing (GSR) [52] and greedy perimeter state-less routing (GPSR) [53].…”

Section: Comparison Of Csma/ca and Rr-alohamentioning

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

Section: Comparison Of Csma/ca and Rr-alohamentioning

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

show abstract

“…e wireless sensor networks have played a significant part in communiqué in the last few years [1][2][3]. In the end, the revolution in wireless communication includes a wide range of standard networks.…”

Section: Introductionmentioning

confidence: 99%

Smart Congestion Control in 5G/6G Networks Using Hybrid Deep Learning Techniques

2022

View full text Add to dashboard Cite

With the mobility and ease of connection, wireless sensor networks have played a significant role in communication over the last few years, making them a significant data carrier across networks. Additional security, lower latency, and dependable standards and communication capability are required for future-generation systems such as millimeter-wave LANs, broadband wireless access schemes, and 5G/6G networks, among other things. Effectual congestion control is regarded as of the essential aspects of 5G/6G technology. It permits operators to run many network illustrations on a single organization while maintaining higher service quality. A sophisticated decision-making system for arriving network traffic is necessary to confirm load balancing, limit network slice letdown, and supply alternative slices in slice letdown or congestion. Because of the massive amount of data being generated, artificial intelligence (AI) and machine learning (ML) play a vital role in reconfiguring and improving a 5G/6G wireless network. In this research work, a hybrid deep learning method is being applied to forecast optimal congestion improvement in the wireless sensors of 5G/6G IoT networks. This proposed model is applied to a training dataset to govern the congestion in a 5G/6G network. The proposed approach provided promising results, with 0.933 accuracy, and 0.067 miss rate.

show abstract

“…Wireless sensor networks have played a crucial role in communication in the past few decades due to their unique features (e.g., mobility and ease of connection), which make them a major carrier of data across networks [1][2][3]. Consequently, the communication revolution embraces various standard wireless networks.…”

Section: Introductionmentioning

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

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

Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET

Cited by 10 publications

References 20 publications

Vehicular Ad Hoc Networks: Growth and Survey for Three Layers

Vehicular Ad Hoc Networks: Growth and Survey for Three Layers

Smart Congestion Control in 5G/6G Networks Using Hybrid Deep Learning Techniques

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

Contact Info

Product

Resources

About