Sep 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 13446-13459· 2 citations· 57 references
Abstract
With the rapid development of 6G and Internet of Vehicles (IoV) technologies, the volume of computation-intensive tasks generated by intelligent vehicles is growing exponentially. Given limited onboard processing capabilities, vehicles increasingly rely on edge servers deployed by service providers (SPs) at roadside units to offload tasks. Vehicle clients can offload the tasks to SPs to mitigate their onboard computation load, while SPs derive economic benefits through the provision of computation resources. However, this interaction introduces a conflict of interest, as vehicles aim to minimize their offloading costs, while SPs seek to maximize revenue. To address this problem, we propose SPOR, a Stackelberg game-based service priority-aware computation offloading and resource pricing scheme in IoV. SPOR is a hierarchical game-theoretic framework in which SPs act as leaders setting prices, while vehicles act as followers determining their offloading strategies. A novel service prioritization function is introduced, incorporating booking price, system load, and reputation to ensure fair and balanced resource allocation. We provide a theoretical proof of the existence and uniqueness of a Nash equilibrium. Extensive experiments on a real-world vehicle edge computing dataset show that SPOR outperforms baseline methods in delay, energy consumption, average load, and task completion rate. Notably, SPOR maintains task completion rates above 97% even under heavy workloads, demonstrating its effectiveness in enhancing system reliability and overall performance.
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.
A. Saluja, Satyabrata Das, S. K. Nayak et al.· Turkish Journal of Electrica...· 0 citations
The results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Irshad Khan, Neetha Papanna Umalakshmi, Somshekhar Durgaiah et al.· Bulletin of Electrical Engin...· 0 citations
SOVANET+ is presented, an extended scheduling technique that jointly accounts for service criticality, network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services, supporting its viability for next-generation intelligent transportation systems.
Athanasios Kanavos, Gerasimos Papanikolaou-Ntais, A. Kaloxylos· Electronics· 0 citations
This paper proposes a hierarchical computation framework that flexibly supports task execution across local vehicles, neighboring vehicles, RSUs, and cloud resources, and designs an efficient task migration and resource scheduling strategy that improves overall system performance under dynamic network conditions.
Liqun Yang· Journal of Grid Computing· 0 citations
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
With advancements in connected vehicle technology, sophisticated computing equipment is installed to assist resource-intensive applications for better and faster processing. However, due to the high demand for computation, local resources are insufficient, and therefore, tasks are offloaded to nearby network edges to meet task deadlines. A similar approach is adopted for vehicle-to-vehicle task offloading, where underutilized vehicles are used to meet the computation demands of heavily loaded vehicles. Due to dynamic changes in topology caused by vehicle speed and direction, many tasks fail to deliver results after remote computation. In this work, we explore energy consumption in the latter approach, where tasks are executed but fail to deliver results. Furthermore, we propose a multi-layer, energy-enabled task offloading strategy that relies on degree, closeness, and betweenness centrality as the initial selection mechanism, where the second tier selection relies on features such as service time and potential path diversion time. The results show a 56% to 45.17% energy loss reduction in the proposed approach with varying vehicle arrival rates.
A. W. Malik, S. Khan· 2026 IEEE Cloud Summit· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
