Real-Time VANET Applications Using Fog Computing

Grover, Jyoti; Singhal, Sunita; Yadav, Anju

doi:10.1007/978-981-10-5828-8_65

Cited by 37 publications

(17 citation statements)

References 8 publications

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“…Moreover, it reduces traffic between users and the cloud and energy usage while saving the bandwidth [60]. The FNs provide computing power, storage, and networking services for the infrastructure's applications [61].…”

Section: E Fog Computingmentioning

confidence: 99%

IoT Provisioning QoS based on Cloud and Fog Computing

Samann

Zeebaree

Askar

2021

JASTT

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The wide-spread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.

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Section: E Fog Computingmentioning

confidence: 99%

IoT Provisioning QoS based on Cloud and Fog Computing

Samann

Zeebaree

Askar

2021

JASTT

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“…Fog computing can refine the data obtained from social networks and vehicular networks to predict the possible interdependencies. It includes both local decisions making and geo-distribution characteristics to bring less delay [28]. This subsection describes how to start a Private Communication Channel (PC 2 ) between any two nodes (source Table 7 ORT of node E. Table 6 Cluster Head Route Table. Pseudocode 2 Reliability factor Optimization Technique.…”

Section: System Operation Phase (Sop)mentioning

confidence: 99%

A New Fog-Based Routing Strategy (FBRS) for Vehicular Ad-Hoc Networks

Gayyar

Saleh

Labib

2021

Preprint

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Vehicular ad-hoc network (VANET) plays a significant role in future intelligent transportation systems. The main objective of vehicular ad hoc networks (VANETs) is to improve driver safety and traffic efficiency. Many researchers proposed different schemes to improve communication efficiency. It is quite challenging where vehicles’ speed, Direction, and density of neighbors on the move are not consistent. Although several routing protocols have been introduced to manage data exchange among vehicles in VANETS, they still suffer from many drawbacks such as lost packets or time penalties. This paper introduced a new Fog Based Routing Strategy, which constructs a reliable system of adaptive, stable, and efficient routing networks. FBRS consists of two main phases: System Setup Phase (SSP) and System Operation Phase (SOP). SSP creates a cluster network, collects its nodes’ data, mining routes between them, and ranking paths using Dijkstra’s algorithm into a simplified table. Although, SOP generates a reliable route between the request of any two nodes for a communication channel and maintains the route against any simultaneous crashes. Recent VANET routing protocols have been compared against FBRS. Experimental results have proven the outperforming of the proposed algorithm against recent routing protocols in terms of packet delivery ratio and routing overhead.

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“…Vehicular Cloud and Fog Computing (VFC) is a standard that comprehends FC and vehicular cloud (VCC) [14]. VFC is also a solution that satisfies the requirements of VANET, such as secure and efficient computing, storage, and in-network resource provision [13].…”

Section: Background Study Of This Researchmentioning

confidence: 99%

“…The authors in previous studies [14][15][16] have conducted investigations separately using Cuckoo/CSA (ABC) and Firefly Genetic Algorithm (GA), respectively. The investigations were performed to evaluate delay sensitivity for real-time detection for only DoS JSA attacks in VANET, which also utilized DSRC technology.…”

Section: Background Study Of This Researchmentioning

confidence: 99%

Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network

Erskine

Elleithy

2019

Electronics

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The vehicular ad hoc network (VANET) is a method through which Intelligent Transportation Systems (ITS) have become important for the benefit of daily life. Real-time detection of all forms of attacks, including hybrid DoS attacks in IEEE 802.11p, has become an urgent issue for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate an enormous amount of messages. This leads to overutilization of the road side unit (RSU) or the central processing unit (CPU) for computation. Therefore, efficient storage and intelligent VANET infrastructure architecture (VIA), which includes trustworthiness, are required. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computation, and communication needs for VANET. This research utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence, including Cuckoo/CSA Artificial Bee Colony (ABC) and Firefly/Genetic Algorithm (GA), to provide real-time detection of DoS attacks in IEEE 802.11p, using VFC for a secure intelligent vehicular network. Vehicles move ar a certain speed and the data is transmitted at 30 Mbps. Firefly Feed forward back propagation neural network (FFBPNN) is used as a classifier to distinguish between the attacked vehicles and the genuine vehicles. The proposed scheme is compared with Cuckoo/CSA ABC and Firefly GA by considering jitter, throughput, and prediction accuracy.

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Real-Time VANET Applications Using Fog Computing

Cited by 37 publications

References 8 publications

IoT Provisioning QoS based on Cloud and Fog Computing

IoT Provisioning QoS based on Cloud and Fog Computing

A New Fog-Based Routing Strategy (FBRS) for Vehicular Ad-Hoc Networks

Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network

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