AlcFier: Adaptive Self-Learning Classifier for Routing in Vehicular Ad-Hoc Network

Nahar, Ankur; Sikarwar, Himani; Das, Debasis

doi:10.1109/lcn53696.2022.9843737

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…In the case of broadcast routing [106], the data packets must be delivered from one node to all other nodes present in the network [107]. Such a protocol includes, for example, the optimized link state routing protocol [92], which utilizes the particle swarm optimization algorithm to select multipoint relay nodes responsible for broadcasting messages, thereby consequently reducing the communication overhead in the network.…”

Section: Routingmentioning

confidence: 99%

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

Herich,

Vaščák

2024

Drones

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The Internet of Vehicles (IoV) and the Internet of Flying Vehicles (IoFV) are integral components of intelligent transportation systems with the potential to revolutionize the way we move people and goods. Although both the IoV and IoFV share a common goal of improving transportation efficiency, safety, and sustainability, they possess distinct characteristics and face unique challenges. To date, the existing literature has predominantly focused on specific aspects of either the IoV or IoFV, but a comprehensive review comparing and contrasting the two domains is still lacking. This review paper aims to address this gap by providing an in-depth analysis of the key differences between the IoV and IoFV systems. The review will examine the technological components, network infrastructure, communication protocols, data management, objectives, applications, challenges, and future trends associated with both domains. Additionally, this paper will explore the potential impact of technologies such as artificial intelligence, machine learning, and blockchain. Ultimately, the paper aims to contribute to a deeper understanding of the implications and potential of these technologies, both in the context of transportation systems and beyond.

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Section: Routingmentioning

confidence: 99%

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

Herich,

Vaščák

2024

Drones

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“…The node degree of its nodes can be written as

where

can be defined as

stands for the node degree of the node

stands for the communication range of the node i , and

stands for distance between node i and j . Then, the average node degree can be expressed as

The self-adaptive node degree variance is calculated by subtracting the node measure from its average measure, which can be expressed as:

The second factor is cosine similarity between two nodes which can be defined as [ 29 , 37 ]

where

and

are the i -th and j -th nodes’ vector information, respectively. Each node

is related with a mobility vector information metric value (i.e., speed, direction, and position)

,⋯,

, where

constitutes the vector values which indicate link information between nodes.…”

Section: The Proposed Secure Multicast Routing Protocol: Dlsmrmentioning

confidence: 99%

“…The second factor is cosine similarity between two nodes which can be defined as [ 29 , 37 ]

where

and

are the i -th and j -th nodes’ vector information, respectively. Each node

is related with a mobility vector information metric value (i.e., speed, direction, and position)

,⋯,

, where

constitutes the vector values which indicate link information between nodes.…”

Section: The Proposed Secure Multicast Routing Protocol: Dlsmrmentioning

confidence: 99%

DLSMR: Deep Learning-Based Secure Multicast Routing Protocol against Wormhole Attack in Flying Ad Hoc Networks with Cell-Free Massive Multiple-Input Multiple-Output

Pramitarini,

Perdana,

Shim

et al. 2023

Sensors

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The network area is extended from ground to air. In order to efficiently manage various kinds of nodes, new network paradigms are needed such as cell-free massive multiple-input multiple-output (CF-mMIMO). Additionally, security is also considered as one of the important quality-of-services (QoS) parameters in future networks. Thus, in this paper, we propose a novel deep learning-based secure multicast routing protocol (DLSMR) in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). We consider the problem of wormhole attacks in the multicast routing process. To tackle this problem, we propose the DLSMR protocol, which utilizes a deep learning (DL) approach to predict the secure and unsecured route based on node ID, distance, destination sequence, hop count, and energy to avoid wormhole attacks. This work also addresses key concerns in FANETs such as security, scalability, and stability. The main contributions of this paper are as follows: (1) We propose a deep learning-based secure multicast routing protocol (DLSMR) to establish a high-stability multicast tree and improve security performance against wormhole attacks. In more detail, the DLSMR protocol predicts whether the route is secure based on network information such as node ID, distance, destination sequence, hop count, and remaining energy or not. (2) To improve the node connectivity and manage multicast members, we propose a top-down particle swarm optimization-based clustering (TD-PSO) protocol to maximize the cost function considering node degree, cosine similarity, cosine distance, and cluster head energy to guarantee convergence to the global optima. Thus, the TD-PSO protocol provides more strong connectivity. (3) Performance evaluations verify the proposed routing protocol establishes a secure route by avoiding wormhole attacks as well as by providing strong connectivity. The TD-PSO clustering supports connectivity to enhance network performance. In addition, we exploit the impact of the mobility model on the network metrics such as packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses.

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“…For use in applications for mobile ad hoc networks (also known as VANETs), Cardenas et al [11] developed an entirely new protocol that they named ProMRP. They should have done the following, since the ProMRP makes use of specific procedures, if they want for their neighbour to pick up their package and effectively send it on their behalf.…”

Section: Literature Reviewmentioning

confidence: 99%

“…1 shows how connected cars might form "convoys" via the use of V2V (vehicle-to-vehicle) communication. The study of VANETs has made it feasible for cars to share information and form clusters [11]. To improve the security and efficacy of transportation networks, cars may form a network using wireless communication devices to share information about hazards, travel times, and more However, there is still a safety concern.…”

Section: Introductionmentioning

confidence: 99%

Simulating MIMT Attackers' Strategies in VANET to Secure ITS in Smart Cities via Multiverse Optimization-based Hybrid Routing Approach

Sumit

2023

IJRITCC

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The connection between moving vehicles and stationary Road Side Units is made possible by VANET technology, which is an essential component of Intelligent Transportation Systems. Vanet based intelligent transportation system (ITS) security is major issue in present days. MIMT Attackers' Strategies are taken into account to test the security of VANET based ITS system in present research paper. The major objective of research paper is to evaluate the potential of security model in case of different type of message dropping and delay attack. It is observed that there is probability of message delay, message drop, and message tampering attack in VANET based ITS system. Due to such attacks there is huge impact on content delivery ratio, packet delay and dropping. Thus proposed hybrid routing approach that is considering Multi-verse optimization has been used to simulate the Quantifying MIMT attacks. In present research, Vanet security in case of intelligent transportation system in smart cities has been considered.

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AlcFier: Adaptive Self-Learning Classifier for Routing in Vehicular Ad-Hoc Network

Cited by 5 publications

References 7 publications

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

DLSMR: Deep Learning-Based Secure Multicast Routing Protocol against Wormhole Attack in Flying Ad Hoc Networks with Cell-Free Massive Multiple-Input Multiple-Output

Simulating MIMT Attackers' Strategies in VANET to Secure ITS in Smart Cities via Multiverse Optimization-based Hybrid Routing Approach

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