Hybrid Genetic Firefly Algorithm-Based Routing Protocol for VANETs

Singh, Gagan Deep; Prateek, Manish; Kumar, Sunil; Verma, Madhushi; Singh, Dilbag; Lee, Heung-No

doi:10.1109/access.2022.3142811

Cited by 57 publications

(29 citation statements)

References 32 publications

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“…Singh [16] presented Hybrid Genetic Firefly Algorithm (HGFA) based routing to obtain faster data delivery in VANET. The fitness function considered in HFGA was retransmission time and propagation time.…”

Section: Related Workmentioning

confidence: 99%

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Traffic Aware Routing using Multi-Objective Delay Centric Enhanced Artificial Ecosystem-Based Optimization for VANET

2023

IJIES

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A vehicular Ad hoc Network (VANET) is generally a heterogeneous wireless network generated between vehicles. The vehicles of the VANET have wireless transceivers and computerized control for allowing the vehicles to operate as network nodes, hence the vehicles can communicate in a VANET environment. However, the communication of VANET is affected because of the higher network congestion and energy usage caused by the dynamic topology of VANET. Therefore, an effective Traffic-Aware Routing (TAR) approach is required to be developed to enhance communication. In this paper, the Multi-Objective Delay Centric Enhanced Artificial Ecosystem-based Optimization (MDCEAEO) is proposed to develop a TAR in VANET. The End to End Delay (EED) is considered as primary cost in the MDCEAEO to develop the TAR where a vehicle's average predicted speed is utilized for identifying the traffic in VANET. The developed MDCEAEO-TAR method is used to improve data transmission by avoiding collisions. The performances of MDCEAEO-TAR are evaluated using EED, energy consumption, Packet Delivery Ratio (PDR), and the routing overhead. The existing research such as LARgeoOPT, DREAMgeoOPT, ZRPgeoOPT, Improved Harmony Search (IHS) and Enhanced Distance, Residual energy based Congestion Aware Ant Colony Optimization (EDR-CAACO), Artificial Ecosystem-based Optimization (AEO), FixedStep Average and Subtraction Based Optimizer (FS-ASBO) and Three Influential Members Based Optimizer (TIMBO) are used to evaluate the MDCEAEO-TAR. The PDR of the MDCEAEO-TAR is 0.9836 at 1000s, which is high when compared to the IHS.

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Section: Related Workmentioning

confidence: 99%

“…Moreover, essential services developed by VANETs are hazard International Journal of Intelligent Engineering and Systems, Vol. 16 control systems and emergency handling services [8,9]. Generally, the VANET is a decentralized wireless ad hoc network, so it doesn't have a predefined architecture in the network.…”

Section: Introductionmentioning

confidence: 99%

Traffic Aware Routing using Multi-Objective Delay Centric Enhanced Artificial Ecosystem-Based Optimization for VANET

2023

IJIES

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“…But, PANFIS [10] , [11] suffers from the over-fitting problem. Also, sometimes PSO may be stuck in local optima and suffers from premature convergence problems [12] , [13] .…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease

Singh

Kaur

Kumar

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Background: Artificial intelligence techniques are widely used in solving medical problems. Recently, researchers have used various deep learning techniques for the severity classification of Chikungunya disease. But these techniques suffer from overfitting and hyper-parameters tuning problems. Methods: In this paper, an artificial intelligence-based cyber-physical system (CPS) is proposed for the severity classification of Chikungunya disease. In CPS system, the physical components are integrated with computational algorithms to provide better results. Random forest (RF) is used to design the severity classification model for Chikungunya disease. However, RF suffers from overfitting and poor computational speed problems due to complex architectures and large amounts of connection weights. Therefore, an evolving RF model is proposed using the adaptive crossover-based genetic algorithm (ACGA). Results: ACGA can efficiently optimize the architecture of RF to achieve better results with better computational speed. Extensive experiments are performed by utilizing the Chikungunya disease dataset. Conclusion: Performance analysis demonstrates that ACGA-RF achieves higher performance as compared to the competitive models in terms of F-measure, accuracy, sensitivity, and specificity. The proposed CPS system can prevent users from visiting hospitals and can render services to patients living far away from hospitals.

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“…This type of cloud infrastructure has numerous advantages and applicability that resolve various existing issues in cloud computing infrastructure. These cloud infrastructures can be used by several application areas [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

Aggarwal

Kumar

Bhatt³

et al. 2022

Computational Intelligence and Neuroscience

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Cloud computing is a procedure of stockpiling as well as retrieval of data or computer services over the Internet that allows all its users to remotely access the data centers. Cloud computing provides all required services to the users, but every platform has its share of pros and cons, and another major problem in the cloud is task scheduling or workflow scheduling. Multiple factors are becoming a challenge for scheduling in cloud computing namely the heterogeneity of resources, tasks, and user priority. User priority has been encountered as the most challenging problem during the last decade as the number of users is increasing worldwide. This issue has been resolved by an advanced encryption standard (AES) algorithm, which decreases the response time and execution delay of the user-request. There are multifarious tasks, for instance, deploying the data on the cloud, that will be executed according to first come first serve (FCFS) and not on the payment basis, which provides an ease to the users. These investigated techniques are 30.21%, 25.20%, 25.30%, 30.25%, 24.26%, and 36.9 8% improved in comparison with the traditional FFOA, DE, ABC, PSO, GA, and ETC, respectively. Moreover, during iteration number 5, this approach is 15.20%, 20.22%, 30.56%, 26.30%, and 36.23% improved than that of the traditional techniques FFOA, DE, ABC, PSO, GA, and ETC, respectively. This investigated method is more efficient and applicable in certain arenas where user priority is the primary concern and can offer all the required services to the users without any interruption.

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Hybrid Genetic Firefly Algorithm-Based Routing Protocol for VANETs

Cited by 57 publications

References 32 publications

Traffic Aware Routing using Multi-Objective Delay Centric Enhanced Artificial Ecosystem-Based Optimization for VANET

Traffic Aware Routing using Multi-Objective Delay Centric Enhanced Artificial Ecosystem-Based Optimization for VANET

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

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