Data‐centric approaches in the internet of vehicles: a systematic review on techniques, open issues, and future directions

Z Partovi, M Zarei, AM Rahmani - International Journal of …, 2023 - Wiley Online Library
Summary The Internet of Vehicles (IoV) is an emerging network of connected vehicles as a
branch of dynamic objects in the Internet of Things (IoT) ecosystem. With the rapid …

Edge server placement problem in multi-access edge computing environment: models, techniques, and applications

B Bahrami, MR Khayyambashi, S Mirjalili - Cluster Computing, 2023 - Springer
Abstract Multi-Access Edge Computing (MEC) is known as a promising communication
paradigm that enables IoT and 5G scenarios by using edge servers located in the proximity …

Multi-objective deep reinforcement learning for computation offloading in UAV-assisted multi-access edge computing

X Liu, ZY Chai, YL Li, YY Cheng, Y Zeng - Information Sciences, 2023 - Elsevier
Unmanned aerial vehicle-assisted multi-access edge computing (UAV-MEC) plays an
important role in some complex environments such as mountainous and disaster areas …

Cost-effective edge server network design in mobile edge computing environment

R Luo, H Jin, Q He, S Wu, X Xia - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Mobile edge computing (MEC) deploys edge servers at the base station in the proximity of
users to provide cloud computing-like computing and storage functionalities, which can …

Mobile edge computing task offloading strategy based on parking cooperation in the internet of vehicles

X Shen, Z Chang, S Niu - Sensors, 2022 - mdpi.com
Due to the limited computing capacity of onboard devices, they can no longer meet a large
number of computing requirements. Therefore, mobile edge computing (MEC) provides …

Federated multi-objective reinforcement learning

F Zhao, X Ren, S Yang, P Zhao, R Zhang, X Xu - Information Sciences, 2023 - Elsevier
Multi-objective reinforcement Learning (MORL) has significant potential for solving complex
decision problems with conflicting objectives. Desiring sufficient training samples, it is …

Energy-aware edge server placement using the improved butterfly optimization algorithm

A Asghari, M Sayadi, H Azgomi - The Journal of Supercomputing, 2023 - Springer
To improve the quality of services provided to their customers, cloud service providers move
some of their resources closer to the users. Proper server placement, considering the …

Dynamic multi-objective service composition based on improved social learning optimization algorithm

Y Hai, X Xu, Z Liu - Applied Soft Computing, 2024 - Elsevier
The technique of multi-objective service composition (MOSC) aims to create powerful and
large grained services through aggregating some simple services while optimizing some …

Workload-based adaptive decision-making for edge server layout with deep reinforcement learning

S Li, Y Zhou, B Zhou, Z Wang - Engineering Applications of Artificial …, 2025 - Elsevier
Mobile edge computing (MEC) is crucial in applications such as intelligent transportation,
innovative healthcare, and smart cities. By deploying servers with computing and storage …

Research on offloading strategy for mobile edge computing based on improved grey wolf optimization algorithm

W Zhang, K Tuo - Electronics, 2023 - mdpi.com
With the development of intelligent transportation and the rapid growth of application data,
the tasks of offloading vehicles in vehicle-to-vehicle communication technology are …