Suchergebnisse - efficient Q‐learning based computation offloading algorithm*

DDPG-Based Computation Offloading Strategy for Maritime UAV.

Autoren: Zhao, Ziyue Xu, Yanli Yu, Qianlian

Quelle: Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3376, 19p

Schlagwörter: EDGE computing, RESOURCE allocation, DRONE aircraft, INTERNET of things, REINFORCEMENT learning, DISTRIBUTED computing

Q‐learning based computation offloading for multi‐UAV‐enabled cloud‐edge computing networks

Autoren: Meng Wang Shuo Shi Shushi Gu et al.

Quelle: IET Communications. 14:2481-2490

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

Zugangs-URL: https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-com.2019.1184
https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2019.1184
https://doi.org/10.1049/iet-com.2019.1184
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-com.2019.1184
https://digital-library.theiet.org/content/journals/10.1049/iet-com.2019.1184
https://dblp.uni-trier.de/db/journals/iet-com/iet-com14.html#WangSGGQ20

Twin delayed deep deterministic policy gradient-based intelligent computation offloading for IoT

Autoren: Siguang Chen Bei Tang Kun Wang

Quelle: Digital Communications and Networks, Vol 9, Iss 4, Pp 836-845 (2023)

Schlagwörter: Deep reinforcement learning, Computation offloading, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Fog computing, Information technology, 02 engineering and technology, Resource allocation, T58.5-58.64, 7. Clean energy

Zugangs-URL: https://doaj.org/article/6b11b52c7125444ab8938bc917e5095c

ImanRHT/QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing

Autoren: Iman Rahmati

Schlagwörter: DRL, deep reinforcement learning, MEC, Mobile edge computing, quality of experience, DQN, LSTM, computation task offloading

Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms

Autoren: Weizhe Zhang Brij B. Gupta Ibrahim A. Elgendy et al.

Quelle: Wireless Networks. 27:2023-2038

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Computation Offloading for Workflow in Mobile Edge Computing Based on Deep Q-Learning

Autoren: Mingfang Ma Songtao Guo Xin Su et al.

Quelle: 2019 28th Wireless and Optical Communications Conference (WOCC). :1-5

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

Zugangs-URL: https://dblp.uni-trier.de/db/conf/wocc/wocc2019.html#ZhuGMFL0GJ19
https://ieeexplore.ieee.org/document/8770689/
https://doi.org/10.1109/WOCC.2019.8770689

Reinforcement learning based task offloading of IoT applications in fog computing: algorithms and optimization techniques.

Autoren: Allaoui, Takwa Gasmi, Kaouther Ezzedine, Tahar

Quelle: Cluster Computing; Nov2024, Vol. 27 Issue 8, p10299-10324, 26p

Schlagwörter: DEEP reinforcement learning, REINFORCEMENT learning, PROCESS capability, MATHEMATICAL optimization, INTERNET of things

Dual-Q network deep reinforcement learning-based computation offloading method for industrial internet of things.

Autoren: Du, Ruizhong Wu, Jinru Gao, Yan

Quelle: Journal of Supercomputing; Nov2024, Vol. 80 Issue 17, p25590-25615, 26p

Schlagwörter: DEEP reinforcement learning, DATA privacy, MACHINE learning, MOBILE computing, INTERNET of things

Task Offloading and Scheduling Based on Mobile Edge Computing and Software-defined Networking

Autoren: Fatimah Azeez Rawdhan

Quelle: Journal of Telecommunications and Information Technology, Vol 99, Iss 1 (2025)

Schlagwörter: energy efficiency, MEC, PSO, Q-learning, scalability, scheduling, Telecommunication, TK5101-6720, Information technology, T58.5-58.64

Dateibeschreibung: electronic resource

Relation: https://www.jtit.pl/jtit/article/view/1941; https://doaj.org/toc/1899-8852

Zugangs-URL: https://doaj.org/article/c861c02dcfc64add8297c91742baf9ad

Deep reinforcement learning based computation offloading for xURLLC services with UAV-assisted IoT-based multi-access edge computing system.

Autoren: Fatima, Nida Saxena, Paresh Giambene, Giovanni

Quelle: Wireless Networks (10220038); Dec2024, Vol. 30 Issue 9, p7275-7291, 17p

Schlagwörter: DEEP reinforcement learning, REINFORCEMENT learning, COMPUTER systems, TELECOMMUNICATION systems, EDGE computing

Task Partition-Based Computation Offloading and Content Caching for Cloud–Edge Cooperation Networks.

Autoren: Huang, Jingjing Yang, Xiaoping Chen, Jinyi et al.

Quelle: Symmetry (20738994); Jul2024, Vol. 16 Issue 7, p906, 25p

Schlagwörter: DEEP reinforcement learning, REINFORCEMENT learning, RESOURCE allocation, ALGORITHMS, COOPERATION

Q‐learning based computation offloading for multi‐UAV‐enabled cloud‐edge computing networks.

Autoren: Wang, Meng Shi, Shuo Gu, Shushi et al.

Quelle: IET Communications (Wiley-Blackwell); Sep2020, Vol. 14 Issue 15, p2481-2490, 10p

Schlagwörter: ARTIFICIAL intelligence, MOBILE computing, DRONE aircraft, NONLINEAR programming, CELL phones, EDGE computing

An Efficient Online Computation Offloading Approach for Large-Scale Mobile Edge Computing via Deep Reinforcement Learning.

Autoren: Hu, Zheyuan Niu, Jianwei Ren, Tao et al.

Quelle: IEEE Transactions on Services Computing; Mar/Apr2022, Vol. 15 Issue 2, p669-683, 15p

Deep reinforcement learning‐based joint optimization of computation offloading and resource allocation in F‐RAN.

Autoren: Jo, Sonnam Kim, Ung Kim, Jaehyon et al.

Quelle: IET Communications (Wiley-Blackwell); Mar2023, Vol. 17 Issue 4, p549-564, 16p

Schlagwörter: REINFORCEMENT learning, EDGE computing, RESOURCE allocation, RADIO access networks, POWER transmission, NONLINEAR equations

Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing.

Autoren: Yifei Wei Zhaoying Wang Da Guo et al.

Quelle: Computers, Materials & Continua; 2019, Vol. 59 Issue 1, p89-104, 16p

Schlagwörter: MOBILE computing, REINFORCEMENT learning, DEEP learning, MACHINE learning, OPTIMAL control theory, ARTIFICIAL neural networks, ELECTRON work function

Self-Learning Based Computation Offloading for Internet of Vehicles: Model and Algorithm.

Autoren: Luo, Quyuan Li, Changle Luan, Tom H. et al.

Quelle: IEEE Transactions on Wireless Communications; Sep2021, Vol. 20 Issue 9, p5913-5925, 13p

Joint resource allocation and computation offloading in mobile edge computing for SDN based wireless networks

Autoren: Changchuan Yin Sihua Wang Nahida Kiran et al.

Quelle: Journal of Communications and Networks. 22:1-11

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 12. Responsible consumption

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/5449605/9042893/08854270.pdf
https://dblp.uni-trier.de/db/journals/jcn/jcn22.html#NahidaPWY20
https://ieeexplore.ieee.org/document/8854270
https://doi.org/10.1109/JCN.2019.000046

An efficient computational offloading method using deep reinforcement learning in edge-end-cloud.

Autoren: Liu, Xinrui Feng, Libo Zhang, Peiying et al.

Quelle: Ad Hoc Networks. Nov2025, Vol. 178, pN.PAG-N.PAG. 1p.

Schlagwörter: Reinforcement learning, Edge computing, Energy consumption, Cloud computing, Blockchains

Task Offloading and Scheduling Based on Mobile Edge Computing and Software-defined Networking.

Autoren: Rawdhan, Fatimah Azeez¹ fatimah.azeez@uomustansiriyah.edu.iq

Quelle: Journal of Telecommunications & Information Technology. 2025, Issue 1, p30-37. 8p.

Schlagwörter: Multi-objective optimization, Particle swarm optimization, Mobile computing, Software-defined networking, Resource allocation

Adaptive and Energy-Efficient IoT Routing via DOS-RL with Bayesian Hyperparameter Tuning in SDN-Driven Architectures.

Autoren: Selvi, M. Senthamil Sudha, L.

Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 3, p173-192, 20p

Schlagwörter: WIRELESS sensor networks, SOFTWARE-defined networking, REINFORCEMENT learning, SENSOR networks, NETWORK performance