Multi-Objective Harris Hawks Optimization Algorithm Based 2-Hop Routing Algorithm for CR-VANET

Hossain, Awlad; Noor, Rafidah Md; Yau, Kok‐Lim Alvin; Azzuhri, Saaidal Razalli; Z’aba, Muhammad Reza; Ahmedy, Ismail; Jabbarpour, Mohammad Reza

doi:10.1109/access.2021.3072922

Cited by 28 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we carry out a comparison of the proposed 6GCRN-IoCV method with various existing methods, such as the NPH-CRN [ 39 ], HBF-MIMO [ 40 ], MEC-ISS [ 42 ], and CR-VANET [ 43 ] methods in order to evaluate the method’s performance. Numerous performance metrics are considered for analyzing performance such as the throughput, packet delivery ratio, delay, routing overhead, route acquisition delay, the probability of detection, spectral efficiency, total transmit power, and sensing delay, which are divided into three major categories: QoS analysis, spectrum efficiency analysis, and spectrum-sensing analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The authors of [ 43 ] proposed a method of a two-hop routing algorithm for CR-VANET based on a multi-objective Harris Hawks optimization algorithm. The main problems of this research are defined as follows: Here, spectrum sensing and segment management are performed at the same time in SUs, and channel allocation is carried out by the RSU in parallel.…”

Section: Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments

Pari

Jaisankar

2022

Sensors

View full text Add to dashboard Cite

A cognitive radio network (CRN) is integrated with the Internet of Connected Vehicles (IoCV) in order to address spectrum scarcity and communication reliability issues. However, it is limited, possessing less throughput, a low packet delivery ratio, high latency, and high mobility in the spectrum. In this research study, the existing issues are addressed by proposing a 6G cognitive radio network–Internet of connected vehicles (6GCRN–IoCV) approach. Initially, all the entities such as secondary users (SUs), primary users (PUs), and pedestrians are authenticated in blockchain to ensure security. The edge-assisted roadside units (ERSU) initiate clustering only for authenticated SUs using the improved DBSCAN algorithm in consideration of several metrics. The ERSU then generates an intersection-aware map using the spatial and temporal-based logistic regression algorithm (STLR) to reduce collisions in the intersection. The spectrum utilization is improved by performing spectrum sensing in which all the SUs involved in spectrum sensing use lightweight convolutional neural networks (Lite-CNN) in consideration of several metrics and provide the sensing report to the fusion center (FC) in an encrypted manner to reduce the spectrum scarcity and security issues. The communications between the SUs are necessary to avoid risks in the IoCV environment. Hence, optimal routing is performed using the Dingo Optimization Algorithm (DOA), which increases throughput and packet delivery ratio. Finally, communication reliability is enhanced by performing hybrid beamforming, and this exploits the multi-agent-based categorical Deep-Q Network (categorical DQN), which increases spectral efficiency based on its adaptive intelligent behavior. The proposed study is simulated using the SUMO and OMNeT++ simulation tools and the performances are validated with existing works using several performance metrics. The result of the simulation shows that the proposed work performs better than the existing approaches.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments

Pari

Jaisankar

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In [17], proposed a hybrid optimization approach with the combination of Genetic algorithm and firefly algorithm for VANETs. This method achieves faster communication in VANETs and produced better results in terms of packet delivery ratio and delay but to work with high speed VANETs some more improved is essential to perform the communication in effective manner.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Bio-Inspired Optimization Scheme for RSU Distribution in Vehicular Ad-Hoc Network

Hassan¹,

Al-obaidi²,

Alani³

et al. 2023

IJETAE

View full text Add to dashboard Cite

—Vehicular Ad-Hoc Network (VANETs) is a trending modelthat plays maximum role inall the application of Intelligent Transport Systems (ITS). In general, VANETs are based on two communication types such as among vehicles and vehicles to infrastructure Roadside Units(RSU) based communication. In this paper, HACOGO approach is developedbased on ahybrid bio inspired optimization with the combination of Ant Colony Optimization (ACO) with Grasshopper Optimization Algorithm (GOA). The HACOGO is used to perform stable RSU distribution in VANETs. The ACO algorithm is used to help the vehicle to select the optimal path toward the destination and GOA algorithm highly magnificent vehicle is chosen as RSU. The Performance analysis of HACOGO is done by calculating the common parameters such as packet delivery ratio, end-to-end delay, packet loss and routing overhead. To analysis the effectiveness of the proposed model its results are compared with the earlier works. From the outcome it is proven using the HACOGO approach the end-to-end delay, packet loss and routing overhead is reduced as well as the packet delivery ratio of the network is increased than others. Keywords—Vehicular Ad-Hoc Network,Roadside Units, Ant Colony Optimization.

show abstract

“…Hossain et al [200] introduced a multiobjective version of HHO based on the 2-hop routing mechanism. The novel algorithm is applied for the cognitive Radio-Vehicular Ad Hoc Network.…”

Section: Multiobject Hhomentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

View full text Add to dashboard Cite

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

show abstract

Multi-Objective Harris Hawks Optimization Algorithm Based 2-Hop Routing Algorithm for CR-VANET

Cited by 28 publications

References 24 publications

Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments

Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments

A Hybrid Bio-Inspired Optimization Scheme for RSU Distribution in Vehicular Ad-Hoc Network

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Contact Info

Product

Resources

About