Stochastic Lagrangian Krill Herd Optimized Quadratic Associative Boost Classification for Load Balancing in VANET

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 5

Year of Publication : 2025

Authors : S. Sumathi, P. Tamilselvan

10.14445/23488379/IJEEE-V12I5P104

How to Cite?

S. Sumathi, P. Tamilselvan, "Stochastic Lagrangian Krill Herd Optimized Quadratic Associative Boost Classification for Load Balancing in VANET," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 5, pp. 32-44, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I5P104

Abstract:

VANETs are wireless technologies specifically designed for communication among vehicles and roadside infrastructure. VANETs face various challenges, including network connectivity issues due to high mobility and limited communication range, especially in dense urban environments. Load balancing in VANETs is crucial for ensuring efficient and reliable data communication among vehicles. The dynamic nature of VANETs, characterized by rapidly changing network topologies and varying traffic loads, poses unique challenges for achieving optimal communication performance. In this paper, a Stochastic Lagrangian Krill Herd Optimized Quadratic Associative Boost Classification (SLKHO-QABC) method is introduced for resource-efficient load balancing in VANETs. The main objective of the proposed method is designed for load-balanced data transmission in VANETs with minimum end-to-end delay. The SLKHO-QABC method includes two major processes namely resource optimization and classification in VANET. Initially, the Stochastic Universal Sampled Lagrangian Krill Herd Optimization is used to determine the resource-efficient vehicle nodes based on fitness functions. With the optimal vehicle nodes, the load capacity is identified through classification. Quadratic Associative Boost Classification is utilized to categorize the less or heavy-loaded vehicle nodes based on the likelihood ratio test. Finally, the vehicle node with a higher load broadcasts an information packet to the lesser-loaded vehicle node during the time of flight, which is used to achieve efficient load balancing in VANET. Experimental analysis is performed for various parameters. Performance comparison analyses show that the proposed SLKHO-QABC method improves the load balancing efficiency throughput and minimizes energy utilization, packet loss rate, and end-to-end delay. SLKHO-QABC method improves the load balancing efficiency by 5.5.% and throughput by 40%, reduces the Packet loss rate by 31%, energy consumption by 18%, and end-to-end delay by 18.5%.

Keywords:

VANETs, load balancing, data communication, Stochastic Universal Sampled Lagrangian Krill Herd Optimization, Quadratic Associative boost Classification, likelihood ratio test, time of flight method.

References:

[1] Achyut Shankar et al., “A Modified Social Spider Algorithm for an Efficient Data Dissemination in VANET,” Environment, Development and Sustainability, pp. 1-44, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] S. Harihara Gopalan et al., “Data Dissemination Protocol for VANETs to Optimize the Routing Path using Hybrid Particle Swarm Optimization with Sequential Variable Neighbourhood Search,” Telecommunication Systems, vol. 84, no. 2, pp. 153-165, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Sahar Ebadinezhad, “Design and Performance Evaluation of Improved DFACO Protocol based on Dynamic Clustering in VANETs,” SN Applied Sciences, vol. 3, no. 486, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Madhuri Husan Badole, and Anuradha D. Thakare, “An Optimized Framework for VANET Routing: A Multi-Objective Hybrid Model for Data Synchronization with Digital Twin,” International Journal of Intelligent Networks, vol. 4, pp. 272-282, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Gagan Preet Kour Marwah, and Anuj Jain, “A Hybrid Optimization with Ensemble Learning to Ensure VANET Network Stability Based on Performance Analysis,” Scientific Reports, vol. 12, no. 10287, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Syed Ehsan Haider, Muhammad Faizan Khan, and Yousaf Saeed, “Adaptive Load Balancing Approach to Mitigate Network Congestion in VANETS,” Computers, vol. 13, no. 8, pp. 1-17, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Koppisetti Giridhar, C. Anbuananth, and N. Krishnaraj, “Energy Efficient Clustering with Heuristic Optimization based Ro/uting Protocol for VANETs,” Measurement: Sensors, vol. 27, pp. 1-8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Sham Rizal Bin Ramlee, Sazlinah Hasan, and Shamala K. Subramaniam, “Cluster Optimization in VANET Using MFO Algorithm and K-Means Clustering: A Bibliometric Review,” International Journal for Multidisciplinary Research, vol. 6, no. 2, pp. 1-65, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Ren Qun, and  S.M. Arefzadeh, “A New Energy-Aware Method for Load Balance Managing in the Fog-Based Vehicular Ad Hoc Networks (VANET) using a Hybrid Optimization Algorithm,” IET Communication, vol 15, no. 13, pp. 1665-1676, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ghassan Husnain, and Seyedeh Maryam Arefzadeh, “An Intelligent Cluster Optimization Algorithm based on Whale Optimization Algorithm for VANETs (WOACNET),” Plos One, vol. 16, no. 4, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Tony Santhosh Gnanasekar, and Dhandapani Samiappan, “Impact of Hybridized Rider Optimization with Cuckoo Search Algorithm on Optimal VANET Routing,” International Journal of Communication System, vol. 34, no. 16, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Masood Ahmad et al., “Optimized Clustering in Vehicular Ad Hoc Networks Based on Honey Bee and Genetic Algorithm for Internet of Things,” Peer-to-Peer Networking and Applications, vol. 13, no. 2, pp. 532-547, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Amir Javadpour et al., “Enhancement in Quality of Routing Service Using Metaheuristic PSO Algorithm in VANET Networks,” Soft Computing, vol. 27, no. 5, pp. 2739-2750, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Kiran Afzal et al., “An Optimized and Efficient Routing Protocol Application for IoV,” Mathematical Problems in Engineering, vol. 2021, no. 1, pp. 1-32, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Chenguang He et al., “A Two-Level Communication Routing Algorithm Based on Vehicle Attribute Information for Vehicular Ad Hoc Network,” Wireless Communications and Mobile Computing, vol. 2021, no. 1, pp. 1-14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ravie Chandren Muniyandi et al., “An Improved Harmony Search Algorithm for Proactive Routing Protocol in VANET,” Journal of Advanced Transportation, vol. 2021, no. 1, pp. 1-17, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] V.M. Niaz Ahamed, and K. Sivaraman, “Congestion Control System Optimization with the Use of Vehicle Edge Computing in VANET Powered by Machine Learning,” International Journal of Computer Networks and Applications, vol. 11, no. 4, pp. 1-13, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ali Seyfollahi, and Ali Ghaffari,  “A Lightweight Load Balancing and Route Minimizing Solution for Routing Protocol for Low-Power and Lossy Networks,” Computer Networks, vol. 179, pp. 1-21, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Shitong Ye et al., “A Deep Reinforcement Learning-Based Intelligent QoS Optimization Algorithm for Efficient Routing in Vehicular Networks,” Alexandria Engineering Journal, vol. 107, no. 6, pp. 317-331, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Aradhana Behura, M. Srinivas, amd Manas Ranjan Kabat, “Giraffe Kicking Optimization Algorithm Provides Efficient Routing Mechanism in the Field of Vehicular Ad Hoc Networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 13, no. 8, pp. 3989-4008, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Long Luo et al., “Intersection-Based V2X Routing via Reinforcement Learning in Vehicular Ad Hoc Networks,” IEEE Transactions on Intelligent Transportation Systems,  vol. 23, no. 6, pp. 5446-5459, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Dhwani Desai, Hosam El-Ocla, and Surbhi Purohit, “Data Dissemination in VANETs Using Particle Swarm Optimization,” Sensors, vol. 23, no. 4, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Nitin Singh Rajput et al., “Swarm Intelligence Inspired Meta-Heuristics for Solving Multi-Constraint QoS Path Problem in Vehicular Ad Hoc Networks,” Ad Hoc Networks, vol. 123, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Yujie Song et al., “STALB: A Spatio-Temporal Domain Autonomous Load Balancing Routing Protocol,” IEEE Transactions on Network and Service Management, vol. 20, no. 1, pp. 73-87, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Ahmad Raza Hameed et al., “Energy- and Performance-Aware Load-Balancing in Vehicular Fog Computing,” Informatics and Systems, vol. 30, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] V. Manoj Kumar et al., “Chaotic Harris Hawks Optimization Algorithm for Electric Vehicles Charge Scheduling,” Energy Reports, vol. 11, pp. 4379-4396, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Arbelo Lolai et al., “Reinforcement Learning based on Routing with Infrastructure Nodes for Data Dissemination in Vehicular Networks (RRIN),” Wireless Networks, vol. 28, pp. 2169-2184, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Yixin He et al., “Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs”, Remote Sensors, vol. 13, no. 8, pp. 1-26, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Khalid Kandali et al., “An Intelligent Machine Learning Based Routing Scheme for VANET,” IEEE Access,  vol. 10, pp. 74318-74333, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Arindam Debnath et al., “A Routing Technique for Enhancing the Quality of Service in Vanet,” IETE Journal of Research,  vol. 69, no. 4, pp. 2193-2206, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Khaled Hejja, Sara Berri, and Houda Labiod, “Network Slicing with Load-Balancing for Task Offloading in Vehicular Edge Computing,” Vehicular Communications, vol. 34, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[32] C. Fancy, and M. Pushpalatha, “Traffic-Aware Adaptive Server Load Balancing for Software Defined Networks” International Journal of Electrical and Computer Engineering, vol. 11, no. 3, pp. 2211-2218, 2021.
[CrossRef] [Google Scholar] [Publisher Link]