RETRACTED ARTICLE: Multi Information Amount Movement Aware- Routing in FANET: Flying Ad-hoc Networks

Radley, Sheryl; Sybi, Cynthia J.; Premkumar, K.

doi:10.1007/s11036-019-01395-4

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…If AODV, GPSR, and other routing methods are adopted, VANETs will have serious packet loss. As more and more scholars pay attention to the research field of vehicle self-organizing network, some new research results begin to come into being [10][11][12][13]. Li et al propose a W-GPCR routing method for vehicular ad hoc networks, which first analyzes the relationship between vehicle node routes and other parameters, such as the Euclidean distance between node pairs, travel direction, and vehicle density.…”

Section: Related Workmentioning

confidence: 99%

A Multiscenario Intelligent QoS Routing Algorithm for Vehicle Network

Liu

Feng

2022

Computational Intelligence and Neuroscience

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Vehicular ad hoc network (VANET) is a key part of intelligent transportation system. VANET technology is very important for realizing vehicle-to-vehicle communication, remote control of unmanned vehicles, and early warning reception of road condition information ahead of time when external networks such as the Internet are limited. Aiming at the problems of uncertainty in vehicle mobility, uneven distribution of traffic density in road sections, and uncertainty in the road scene where the vehicle is located in VANET, a multiscenario intelligent QoS routing algorithm (MISR) for vehicle network is proposed. The algorithm analyzes a variety of vehicle network scenarios and discusses the routing methods used in scenarios with/without roadside auxiliary units and vehicle uniform acceleration limited/unrestricted, so that the vehicle network can ensure that the communication link is not interrupted as much as possible. At the same time, QoS performance criteria such as data transmission rate, bit error rate, and delay time are considered. For complex scenes with variable vehicle speeds, this paper introduces a deep reinforcement learning method to intelligently select routing nodes for vehicle networks.

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Section: Related Workmentioning

confidence: 99%

A Multiscenario Intelligent QoS Routing Algorithm for Vehicle Network

Liu

Feng

2022

Computational Intelligence and Neuroscience

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“…For the vehicle-mounted ad hoc network without the cooperation of the roadside auxiliary communication unit, the biggest problem is that the vehicle communication link may be interrupted at any time due to the change of the vehicle speed, which greatly affects the communication quality of the vehicle ad hoc network. Therefore, in the research of vehicle network routing technology, we must first consider the problem of link interruption in vehicle self-organizing network [26,27]. According to the sensing range of onboard sensors, the distance of end-to-end vehicle nodes, vehicle speed, vehicle speed variation range, task packet size, and network transmission rate, this paper proposes a time prediction model, which is used for the current vehicle.…”

Section: Time Prediction Model For Vehicular Ad Hoc Networkmentioning

confidence: 99%

Vehicle‐Mounted Self‐Organizing Network Routing Algorithm Based on Deep Reinforcement Learning

2021

Wireless Communications and Mobile Computing

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Through the research on the vehicle-mounted self-organizing network, in view of the current routing technical problems of the vehicle-mounted self-organizing network under the condition of no roadside auxiliary communication unit cooperation, this paper proposes a vehicle network routing algorithm based on deep reinforcement learning. For the problems of massive vehicle nodes and multiple performance evaluation indexes in vehicular ad hoc network, this paper proposes a time prediction model of vehicle communication to reduce the probability of communication interruption and proposes the routing technology of vehicle network by studying the deep reinforcement learning method. This technology can quickly select routing nodes and plan the optimal route according to the required performance evaluation indicators.

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“…The example of the modularity including both cases (high and low) is shown in Figure 33a,b, respectively. With the significant increase in the use of SNs in recent years, the modularity concept has been extensively studied in the research [126][127][128].…”

Section: Analyzing the Modularity In A Social Network Users' Graphmentioning

confidence: 99%

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Majeed

Rauf

2020

Inventions

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Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

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RETRACTED ARTICLE: Multi Information Amount Movement Aware- Routing in FANET: Flying Ad-hoc Networks

Cited by 9 publications

References 20 publications

A Multiscenario Intelligent QoS Routing Algorithm for Vehicle Network

A Multiscenario Intelligent QoS Routing Algorithm for Vehicle Network

Vehicle‐Mounted Self‐Organizing Network Routing Algorithm Based on Deep Reinforcement Learning

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

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