Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Z Liao, J Peng, B Xiong, J Huang - Journal of Cloud Computing, 2021 - Springer
With the combination of Mobile Edge Computing (MEC) and the next generation cellular
networks, computation requests from end devices can be offloaded promptly and accurately …

Data quality-aware task offloading in mobile edge computing: An optimal stopping theory approach

I Alghamdi, C Anagnostopoulos, DP Pezaros - Future Generation …, 2021 - Elsevier
An important use case of the Mobile Edge Computing (MEC) paradigm is task and data
offloading. Computational offloading is beneficial for a wide variety of mobile applications on …

Multi-task offloading based on optimal stopping theory in edge computing empowered internet of vehicles

L Mu, B Ge, C Xia, C Wu - Entropy, 2022 - mdpi.com
Vehicular edge computing is a new computing paradigm. By introducing edge computing
into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency …

Deep reinforcement learning based adaptive threshold multi-tasks offloading approach in mec

L Mu, B Ge, C Xia, C Wu - Computer Networks, 2023 - Elsevier
Offloading tasks and data is an important use case for Mobile Edge Computing (MEC) that
benefits a wide variety of mobile applications on different platforms, including autonomous …

A federated multi-agent deep reinforcement learning for vehicular fog computing

B Shabir, AU Rahman, AW Malik, R Buyya… - The Journal of …, 2023 - Springer
Vehicular fog computing is an emerging paradigm for delay-sensitive computations. In this
highly dynamic resource-sharing environment, optimal offloading decision for effective …

Proactive & time-optimized data synopsis management at the edge

K Kolomvatsos, C Anagnostopoulos… - … on Knowledge and …, 2020 - ieeexplore.ieee.org
Internet of Things offers the infrastructure for smooth functioning of autonomous context-
aware devices being connected towards the Cloud. Edge Computing (EC) relies between …

Latency and energy aware rate maximization in MC-NOMA-based multi-access edge computing: A two-stage deep reinforcement learning approach

M Nduwayezu, JH Yun - Computer Networks, 2022 - Elsevier
Future network services are emerging with an inevitable need for high wireless capacity
along with strong computational capabilities, stringent latency and reduced energy …

[HTML][HTML] Edge-centric inferential modeling & analytics

C Anagnostopoulos - Journal of Network and Computer Applications, 2020 - Elsevier
This work contributes to a real-time, edge-centric inferential modeling and analytics
methodology introducing the fundamental mechanisms for (i) predictive models update and …

An efficient scheduling scheme for intelligent driving tasks in a novel vehicle-edge architecture considering mobility and load balancing

N Wang, S Pang, X Ji, H Gui, X He - Future Generation Computer Systems, 2024 - Elsevier
With the continuous popularization and evolution of 5G and 6G, mobile edge computing has
achieved rapid development. This study explores the New Generation Mobile Edge …

Data-driven type-2 fuzzy sets for tasks management at the edge

K Kolomvatsos - IEEE Transactions on Emerging Topics in …, 2021 - ieeexplore.ieee.org
Tasks allocation at the edge of the network is a significant research topic for the upcoming
new era of the intelligent edge mesh. One can easily detect interesting attempts to define …