Two-Tier Intelligent Optimization Framework for Trust-Aware Clustering and Energy-Efficient Routing in VANET Communication

International Journal of Electrical and Electronics Engineering |
© 2025 by SSRG - IJEEE Journal |
Volume 12 Issue 9 |
Year of Publication : 2025 |
Authors : R. Raja Kumar, T. Suresh, K. Sekar |
How to Cite?
R. Raja Kumar, T. Suresh, K. Sekar, "Two-Tier Intelligent Optimization Framework for Trust-Aware Clustering and Energy-Efficient Routing in VANET Communication," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 9, pp. 75-88, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I9P108
Abstract:
Vehicular Ad hoc Network (VANET) is a currently developing trend that inspires the delivery of several service providers in urban regions. In VANETs, the vehicles signify the nodes in the Network, which want to assure superior assistance when there is a greater node density. In this framework, the combination of probable clustering techniques has the probability of enhancing the safety of the road and easing a trustworthy choice of encouraging message route. The clustering protocols were defined as the perfect candidate for resolving network scalability issues and ensuring reliable data propagation. This study proposes a Two-Tier Intelligent Optimization Framework for Trust-Aware Clustering and Energy-Efficient Routing (TTIO-TACEER) model in VANET. The main purpose of the TTIO-TACEER model is to integrate an optimized clustering mechanism with an efficient routing strategy to enhance network performance and reliability. In the clustering phase, the TTIO-TACEER technique is applied using the Goose Algorithm (GO) to select optimal Cluster Heads (CHs). Furthermore, the multi-criteria Fitness Function (FF) considers Residual Energy (RE), Trust Level (TL), Degree Difference (DD), Total Energy Consumed (TEC), and mobility for cluster formation. TTIO-TACEER is implemented using the Ninja Optimizer Algorithm (NiOA) to optimize routing paths between CHs and the Base Station (BS) in the routing phase. Moreover, the routing FF considers the distance to the BS to improve data transmission reliability while reducing latency (LAT). An extensive simulation validation is executed to highlight the significance of the TTIO-TACEER technique. A brief comparative study described the superior results of the TTIO-TACEER technique when compared to other existing models.
Keywords:
VANET, Clustering, Goose Algorithm, Energy-efficient routing, Ninja Optimizer Algorithm, Fitness function.
References:
[1] Muddasar Ayyub et al., “A Comprehensive Survey on Clustering in Vehicular Networks: Current Solutions and Future Challenges,” Ad Hoc Networks, vol. 124, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ankita Srivastava, Arun Prakash, and Rajeev Tripathi, “Location Based Routing Protocols in VANET: Issues and Existing Solutions,” Vehicular Communications, vol. 23, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Bedelkhanuly Azat, and Tang Hong, “Destination Based Stable Clustering Algorithm and Routing for VANET,” Journal of Computer and Communications, vol. 8, no. 1, pp. 28-44, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] D. Kalaivani, and P.V.S.S.R. Chandra Mouli, “Link Survivability Rate-Based Clustering for QoS Maximisation in VANET,” International Journal of Grid and Utility Computing (IJGUC), vol. 11, no. 4, pp. 457-467, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Hamideh Fatemidokht, and Marjan Kuchaki Rafsanjani, “QMM-VANET: An Efficient Clustering Algorithm Based on QoS and Monitoring of Malicious Vehicles in Vehicular Ad Hoc Networks,” Journal of Systems and Software, vol. 165, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Forough Goudarzi, Hamid Asgari, and Hamed S. Al-Raweshidy, “Traffic-Aware VANET Routing for City Environments-A Protocol Based on Ant Colony Optimization,” IEEE Systems Journal, vol. 13, no. 1, pp. 571-581, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Chung-Ming Huang, Tzu-Hua Lin, and Kuan-Cheng Tseng, “Data Dissemination of Application Service by Using Member-Centric Routing Protocol in a Platoon of Internet of Vehicle (IoV),” IEEE Access, vol. 7, pp. 127713-127727, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Muhammad Fahad Khan et al., “An Efficient Optimization Technique for Node Clustering in Vanets Using Gray Wolf Optimization,” KSII Transactions on Internet and Information Systems, vol. 12, no. 9, pp. 4228-4247, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Yasir Ali Shah et al., “CAMONET: Moth-Flame Optimization (MFO) Based Clustering Algorithm for VANETs,” IEEE Access, vol. 6, no. 1, pp. 48611-48624, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] A. Sariga, and J. Uthayakumar, “Type 2 Fuzzy Logic based Unequal Clustering Algorithm for Multi-Hop Wireless Sensor Networks,” International Journal of Wireless and Ad Hoc Communication, vol. 1, no. 1, pp. 33-46, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] J.G. Rajeswari, and R. Kousalya, “An Energy, Mobility and Obstacle Aware Clustering based Intelligent Routing Protocol for FANET,” Procedia Computer Science, vol. 252, pp. 934-943, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Amit Choksi, and Mehul Shah, “Machine Learning-assisted Distance Based Residual Energy Aware Clustering Algorithm for Energy Efficient Information Dissemination in Urban VANETs,” International Journal of Next-Generation Computing, vol. 15, no. 1, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] R.K. Mahesh, and Shivkumar S. Jawaligi, “Energy-Efficient Cluster-Based Routing in VANET Assisted by Hybrid Jellyfish and Beluga Optimisation and Fault Tolerance,” International Journal of Vehicle Autonomous Systems, vol. 18, no. 1, pp. 1-30, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[14] A. Sajithabegam, and T. Menakadevi, “An Enhanced Energy and Distance Based Optimized Clustering and Dynamic Adaptive Cluster-Based Routing in Software Defined Vehicular Network,” Telecommunication Systems, vol. 87, no. 4, pp. 917-937, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Khalid A. Darabkh, Mamoun F. Al-Mistarihi, and Bayan Abdallah Odat, “Leveraging Fog Computing and Software-Defined Networking for a Novel Velocity-Aware Routing Protocol with Election and Handover Thresholds in VANETS,” The Journal of Supercomputing, vol. 81, no. 2, pp. 1-37, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Yashar Ghaemi, and Hosam El-Ocla, “Time Delay-Based Routing Protocol Using Genetic Algorithm in Vehicular Ad Hoc Networks,” Cluster Computing, vol. 28, no. 2, pp. 1-19, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[17] T. Kanimozhi, and S. Belina V.J. Sara, “Modified Glowworm Swarm Optimisation-Based Cluster Head Selection and Enhanced Energy-Efficient Clustering Protocol for IoT-WSN,” International Journal of Computational Science and Engineering, vol. 28, no. 2, pp. 204-218, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Youseef Alotaibi et al., “Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks,” Computers, Materials & Continua, vol. 78, no. 3, pp. 4243-4262, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Dhanashree Shukla, and Sudhir D. Sawarkar, “Dynamic Resources Optimisation and Interference Management-Based Green Communication Protocol for 5G,” International Journal of Wireless and Mobile Computing, vol. 28, no. 2, pp. 186-204, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Moneerah Alotaibi et al., “Integrating Two-Tier Optimization Algorithm with Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles,” IEEE Access, vol. 13, pp. 6820-6833, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Junhong Tong, and Quanjie Weng, “Application of Intelligent Routing Algorithm Based on Fuzzy Set Control in SDN Network,” 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, pp. 1610-1614, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Mahendra Dongare, Satish Jondhale, and Balasaheb Agarkar, “Energy-Efficient Approach for Node Selection in Routing Protocols of Wireless Sensor Networks,” 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), Bengaluru, India, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[23] C. UmaRani et al., “An Hybrid Machine Learning and Improved Social Spider Optimization Based Clustering and Routing Protocol for Wireless Sensor Network,” Wireless Networks, vol. 31, no. 2, pp. 1885-1910, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Md. Asif Thanedar, and Sanjaya Kumar Panda, “An Energy-Efficient Resource Allocation Algorithm for Managing On-Demand Services in Fog-Enabled Vehicular Ad Hoc Networks,” International Journal of Web and Grid Services, vol. 20, no. 2, pp. 135-158, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Ying Cao, Wei Wang, and Yan He, “Prediction of Heat-Treated Wood Adhesive Strength Using BP Neural Networks Optimized by Four Novel Metaheuristic Algorithms,” Forests, vol. 16, no. 2, pp. 1-25, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Anis Ben Ghorbal et al., “Predicting Carbon Dioxide Emissions Using Deep Learning and Ninja Metaheuristic Optimization Algorithm,” Scientific Reports, vol. 15, no. 1, pp. 1-28, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[27] A. Balamurugan, “An Energy Efficient Fitness Based Routing Protocol in Wireless Sensor Networks,” ICTACT Journal on Communication Technology, vol. 5, no. 1, pp. 894-899, 2014.
[Google Scholar] [Publisher Link]
[28] Ghassan Husnain et al., “A Bio-Inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs),” Energies, vol. 16, no. 3, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]