Turkish Journal of Electrical Engineering and Computer Sciences
Author ORCID Identifier
ASHISH SALUJA: 0009-0000-4690-5508
SATYABRATA DAS: 0000-0002-0519-3863
SANJIB NAYAK: 0000-0001-9675-2853
SOHAN PANDE: 0000-0001-9971-6575
Abstract
Vehicular networks support intelligent transportation through vehicle-to-roadside Units (V2R) and vehicle-to-vehicle (V2V) communication but face challenges from dynamic topologies, limited RSU coverage, and bandwidth scarcity, which impact service delivery and revenue. RDA-ITU addresses these challenges by integrating V2R and V2V paradigms to maximize RSU revenue, enhance service availability, and improve system efficiency. It dynamically allocates services based on real-time network conditions and vehicle mobility, leveraging V2V relays to optimize both RSU-direct and cooperative communication. Through extensive simulations, RDA-ITU significantly outperforms four baselines: RBSM, VVMM-U, VVMM-LW, and VVMM-MA. It achieves 81.1% higher total revenue, 154.8% more completed requests, and 103.6% higher average data delivery. Specifically, versus RBSM, gains reach 77.6% in revenue, 228.0% in TCR, and 242.4% in TDD; against VVMM-U: 32.6%, 43.9%, and 47.9%; versus VVMM-LW: 153.7%, 74.5%, and 284.1%; and versus VVMM-MA: 25.7%, 30.2%, and 53.7%, respectively. These improvements stem from RDA-ITU’s core mechanisms: revenue-optimized candidate sorting, dynamic V2V relay pairing, and adaptive bandwidth allocation. Prioritizing high-revenue services and facilitating efficient cooperative offloading, RDA-ITU ensures strong performance in dense mobile environments, thus promoting revenue-aware vehicular edge computing.
DOI
10.55730/1300-0632.4192
Keywords
Vehicular networks, V2R communication, V2V communication, service distribution, revenue optimization, data dissemination
First Page
584
Last Page
603
Publisher
The Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
SALUJA, A. S, DAS, S, NAYAK, S. K, & PANDE, S. K (2026). A revenue-driven approach for enhanced task utilization in vehicular cloud computing. Turkish Journal of Electrical Engineering and Computer Sciences 34 (4): 584-603. https://doi.org/10.55730/1300-0632.4192
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