TL;DR
This survey aims to provide a unified perspective for understanding DTO and provide methodological guidance for designing next-generation VEC systems and proposes a synthesized five-dimensional dependency taxonomy specifically designed for VEC.
Task offloading in vehicular networks (VNets) is complicated by fluctuating channels, dynamic topologies, and bursty task arrivals. No single algorithmic paradigm performs reliably across all dynamic conditions, making hybridization a compelling approach. Based on patterns observed in the literature, this survey classifies hybrid algorithms into six basic categories and two extended categories. We characterize environmental dynamism along three dimensions—channel dynamics, topology dynamics, and task dynamics—and define four operating regimes from static to highly dynamic. We review studies across these categories and regimes and compare their hybridization mechanisms, validation conditions, and reported limitations. Based on this analysis, we identify key research gaps in online dynamism detection, cross-category benchmarking, and runtime meta-control. Unlike prior surveys that primarily catalog algorithm combinations, this survey provides a structured taxonomy, an environmental characterization framework, and a diagnostic perspective to guide future research.
Integrated Terrestrial–Non-Terrestrial Networks (ITNTN), which combine terrestrial base stations (BSs), High-Altitude Platform Stations (HAPS), and Low-Earth Orbit (LEO) satellites, are key enablers of 6G communication and edge computing (EC) services. However, energy-limited BSs, particularly HAPS and satellites, pose significant sustainability challenges under continuous operation. To address this issue, we propose an on-demand EC server activation framework integrated with intelligent task offloading across ITNTN. A joint optimization problem is formulated to maximize task offloading success while satisfying energy and quality-of-service requirements. To solve it, we propose an online Q-learning policy that adaptively manages task offloading and EC server activation without prior knowledge of traffic dynamics. Simulation results show that the proposed method achieves superior task offloading success and energy efficiency compared to online heuristic and offline metaheuristic baselines. These findings highlight the importance of energyaware On-Off EC control for sustainable ITNTN systems.
The advancement of vehicle technology enables it to handle complex tasks such as augmented reality, automatic parking, and driving, which demand significant computational power and are sensitive to delays. However, the computational capabilities of these vehicles are limited. As a result, Vehicle Edge Computing (VEC) has been introduced to offload some computational burdens to neighbouring vehicles and edge servers. The main challenge of this system lies in managing offloading in scenarios with frequent disconnections and high-speed driving. To address this challenge, we have formulated the task offloading problem with the goal of minimizing overall time, considering its NP-hard nature and multiple constraints. Our proposed solution is a meta-heuristic approach that integrates the honey bee Food Foraging process with Genetics Algorithm (FFGA) for vehicular task offloading. In the food foraging process, scouts are sent to locate nearby area, which in our case are edge servers and gather contextual details, and upon their return, they perform a dance resembling the infinity symbol, where the central angle indicates the direction of the food field and the speed of the dance signifies the quality of the found food source. The vehicle client uses this information to select one edge server to be in charge of the offloading process. Subsequently, we apply the genetic algorithm to generate an optimized allocation scheme of tasks by considering contextual details of nearby nodes. These details include computational capacity, availability, location, and driving speed. The optimization operators of the genetic algorithm are used to determine the assignment of tasks to nodes. Extensive simulations have demonstrated that the proposed FFGA system outperforms other existing schemes, including the hybrid vehicular edge cloud (HVC), particle swarm optimization (PSO), and the multi-decision based offloading (MDO).
A framework based on GTGO to jointly offload, schedule and allocate resources to different tasks and augment it with an integrated explainable AI (XAI) module is presented, indicating that the suggested framework is an effective, efficient, and transparent resource management solution in intelligent vehicular edge computing systems.
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.
A task-driven offloading algorithm based on Balanced Multi-Agent Deep Deterministic Policy Gradient (BMADDPG) that reduces average task processing latency by approximately 22.67% and decreases total system cost by at least 18.32% under high-load scenarios.