TL;DR
Simulation results show that MOMUS outperforms state-of-the-art VEC scheduling approaches, particularly under high-demand scenarios, achieving higher task completion rates while reducing monetary cost and maintaining acceptable latency.
This work proposes a meta-heuristic approach that integrates the honey bee Food Foraging process with Genetics Algorithm (FFGA) for vehicular task offloading, and demonstrates that the proposed FFGA system outperforms other existing schemes, including the hybrid vehicular edge cloud (HVC), particle swarm optimization (...
This paper presents a novel approach for task offloading in Software Defined Networking (SDN)-based vehicular networks based on a multi-objective optimization algorithm which uses Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Long Short-Term Memory (LSTM)-based vehicle trajectory prediction. The proposed sol...
The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide co...
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.
With the rapid growth of Vehicular Edge Computing (VEC) and Mobile Edge Computing, efficient task offloading is essential for enhancing the computing and communication capabilities in vehicular networks. However, many existing methods suffer from slow convergence, load imbalance, and instability in dynamic, latency-sen...
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