A two-phase fuzzy based access network selection scheme for vehicular ad hoc networks

Evangeline, C. Suganthi; Kumaravelu, Vinoth Babu

doi:10.1007/s12083-021-01228-w

Cited by 11 publications

(4 citation statements)

References 55 publications

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“…Determining the optimal no. of clusters is a critical task in VANETs to achieve load optimization by ensuring that the available resources are used effectively or allocated which is represented by Eq (13). Balancing the workload among the vehicles in the clusters to prevent congestion is another way to achieve load optimization which leads to enhanced network stability.…”

Section: 21mentioning

confidence: 99%

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Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system

Yerrathi,

Pakala

2024

PLoS ONE

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The Internet of Vehicles (IoV) is one of the developing paradigms that integrates the automotive industry with the Internet of Things (IoT). The evolution of traditional Vehicular Ad-hoc Networks (VANETs), which are a layered framework for Intelligent Transportation Systems (ITS), is employed to provide Quality of Service (QoS) to end users in hazardous situations. VANETs can set up ad-hoc networks and share information among themselves using Peer-to-Peer (P2P) communication. Dynamic properties in VANETs such as dynamic topology, node mobility, sparse vehicle distribution, and bandwidth constraints can have an impact on scalability, routing, and security. This can result in frequent link failures, instability, reliability, and QOS concerns, as well as the inherent complexity of NP-hard problems. Researchers have proposed several techniques to achieve stability; the most prominent one is clustering, which relies on mobility metrics. However, existing clustering techniques generate overwhelming clusters, resulting in greater resource consumption, communication overhead, and hop count, which may lead to increased latency. Therefore, the primary objective is to achieve stability by increasing cluster lifetime, which is accomplished by generating optimal clusters. A nature-inspired meta-heuristic algorithm titled African Vulture Optimization Based Clustering Algorithm (AVOCA) is implemented to achieve it. The proposed algorithm can achieve load optimization with efficient resource utilization by mitigating hidden node challenges and ensuring communication proficiency. By maintaining an equilibrium state between the exploration and exploitation phases, AVOCA avoids local optima. The paper explores a taxonomy of the techniques used in Cluster Head (CH) selection, coordination, and maintenance to achieve stability with lower communication costs. We evaluated the effectiveness of AVOCA using various network grid sizes, transmission ranges, and network nodes. The results show that AVOCA generates 40% less clusters when compared to the Clustering Algorithm Based on Moth-Flame Optimization for VANETs (CAMONET). AVOCA generates 45% less clusters when compared to Self-Adaptive Multi-Kernel Clustering for Urban VANETs (SAMNET), AVOCA generates 43% less clusters when compared to Intelligent Whale Optimization Algorithm (i-WOA) and AVOCA generates 38% less clusters when compared to Harris Hawks Optimization (HHO). The results show that AVOCA outperforms state-of-the-art algorithms in generating optimal clusters.

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Section: 21mentioning

confidence: 99%

“…Several techniques are present to avoid handoff one such technique is fuzzy logic. One such method is implemented in [ 13 ] to reduce handoff and to increase QoS. Third, the Central Control (CC) system is responsible for supervising and monitoring the RSUs already in position.…”

Section: Introductionmentioning

confidence: 99%

Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system

Yerrathi,

Pakala

2024

PLoS ONE

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“…Given the key role of resource allocation in V2X, efforts have been made to enhance communication efficiency through the application of deep learning and machine learning, as outlined in [ 29 ] and [ 30 ], respectively. A two-phase fuzzy logic-based handoff scheme was proposed in [ 31 ], demonstrating its ability to reduce unnecessary handoffs and decision delays compared with conventional schemes. Also, the combination of deep reinforcement learning (RL) with resource allocation has been explored in [ 32 , 33 , 34 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

Reuse Distance-Aided Resource Selection Mechanisms for NR-V2X Sidelink Communication

Yin,

Hwang

2023

Sensors

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Cellular vehicle-to-everything (C-V2X) facilitates direct communication between vehicles and other user equipment (UE) to improve the efficiency of the Internet of vehicles communication through sidelink. In addition, in the new radio vehicle-to-everything (NR-V2X) Mode 2, users can automatically select resources using the conventional sensing-based semi-persistent scheduling (SB-SPS) resource selection algorithm. This mechanism allows users to generate a list of available resources after a sensing window, after which the users can randomly select resources, and the resource can be used continuously over multiple periods before reselection. However, during the sensing window, neighbors may generate a similar list of available resources, and random selection may lead to resource conflicts. This phenomenon may lead to deteriorated communication performance and increased latency due to incorrect reception. Therefore, this paper proposes a reuse distance-aided resource selection (RD-RS) method which integrates resource reuse distance judgement with SB-SPS to mitigate resource conflicts and interference caused by random selection. Moreover, the reuse distance judgement is performed before the final resource selection, and whether the user will select the current resource depends on the reuse distance between that user and other occupiers. Furthermore, the performance of the proposed scheme is compared with other algorithms. Simulation results show that the proposed RD-RS not only achieves a higher packet reception ratio (PRR) but also effectively reduces the inter-packet gap (IPG). Moreover, in specific scenarios, the proposed method outperforms conventional schemes by 9% in terms of PRR and 70% in terms of Range.

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“…V2X communication can be traced back to Vehicular Ad hoc Networks (VANETs). The standard presented by Institute of Electrical and Electronics Engineers for vehicular networks (IEEE 802.11p) and the Dedicated Short-Range Communication (DSRC)/Wireless Access in Vehicular Environment (WAVE) standard are the original communication technologies for VANETs that offer wireless connectivity between vehicles and between vehicles and roadside infrastructure [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Resource-aware DBSCAN-based re-clustering in hybrid C-V2X/DSRC vehicular networks

Alrubaye,

Shahgholi Ghahfarokhi

2023

PLoS ONE

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5G wireless networks are paying increasing attention to Vehicle to Everything (V2X) communications as the number of autonomous vehicles rises. In V2X applications, a number of demanding criteria such as latency, stability, and resource availability have emerged. Due to limited licensed radio resources in 5G cellular networks, Cellular V2X (C-V2X) faces challenges in serving a large number of cars and managing their network access. A reason is the unbalanced load of serving Base Stations (BSs) that makes it difficult to manage the resources of the BSs optimally regarding the frequency reuse in cells and its subsequent co-channel interference. It is while the routing protocols could help redirect the load of loaded BSs to neighboring ones. In this article, we propose a resource-aware routing protocol to mitigate this challenge. In this regard, a hybrid C-V2X/ Dedicated Short Range Communication (DSRC) vehicular network is considered. We employ cluster-based routing that enables many cars to interface with the network via some Cluster Heads (CH) using DSRC resources while the CHs send their traffic across C-V2X links to the BSs. Traditional cluster-based routings do not attend the resource availability in BSs that are supporting the clusters. Thus, our study describes an enhanced clustering method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that re-clusters the vehicles based on the resource availability of BSs. Simulation results show that the proposed re-clustering method improves the spectrum efficiency by at least 79%, packet delivery ratio by at least 5%, and load balance of BSs by at least 90% compared to the baseline.

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A two-phase fuzzy based access network selection scheme for vehicular ad hoc networks

Cited by 11 publications

References 55 publications

Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system

Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system

Reuse Distance-Aided Resource Selection Mechanisms for NR-V2X Sidelink Communication

Resource-aware DBSCAN-based re-clustering in hybrid C-V2X/DSRC vehicular networks

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