An Asynchronous Data Transmission Policy for Task Offloading in Edge-Computing Enabled Ultra-Dense IoT
Source abstract
Abstract
In recent years, task offloading and its scheduling optimization have emerged as widely discussed and significant topics. The multi-objective optimization problems inherent in this domain, particularly those related to resour... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: IoT and Edge/Fog Computing · Cloud Computing and Resource Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
Show source-provided references
1. A survey on task offloading in multi-access edge computing10.1016/j.sysarc.2021.102225 ↗
2. Software defined wireless sensor load balancing routing for internet of things applications: Review of approaches10.1016/j.heliyon.2024.e29965 ↗
3. Task offloading in fog computing: A survey of algorithms and optimization techniques10.1016/j.comnet.2022.109137 ↗
4. Quantum particle swarm optimization for task offloading in mobile edge computing10.1109/TII.2022.3225313 ↗
5. A comprehensive review on internet of things task offloading in multi-access edge computing10.1016/j.heliyon.2024.e29916 ↗
6. An intelligent energy-efficient approach for managing IoE tasks in cloud platforms10.1007/s12652-022-04464-x ↗
7. Computation offloading in mobile edge computing networks: A survey10.1016/j.jnca.2022.103366 ↗
8. An energy-optimized embedded load balancing using DVFS computing in cloud data centers10.1016/j.comcom.2022.10.019 ↗
9. Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions10.1016/j.jnca.2022.103568 ↗
10. Joint resource allocation for latency-sensitive services over mobile edge computing networks with caching10.1109/JIOT.2018.2875917 ↗
11. Reliable internet of things: Challenges and future trends10.3390/electronics10192377 ↗
12. Resource allocation and task scheduling in fog computing and internet of everything environments: A taxonomy, review, and future directions10.1145/3513002 ↗
13. Task offloading strategies for mobile edge computing: A survey10.1016/j.comnet.2024.110791 ↗
14. QoS and energy-efficiency aware scheduling and resource allocation scheme in LTE-A uplink systems10.1007/s11235-022-00980-5 ↗
15. Isyaku · Advances in Intelligent Computing Techniques and Applications · 2024 · 6210.1007/978-3-031-59707-7_6 ↗
16. Reliable failure restoration with bayesian congestion aware for software defined networks
17. Task co-offloading for D2D-assisted mobile edge computing in industrial internet of things10.1109/TII.2022.3158974 ↗
18. SDN-assisted mobile edge computing for collaborative computation offloading in industrial internet of things10.1109/JIOT.2022.3190281 ↗
19. Task-offloading strategy of mobile edge computing for WBANs
20. Federated deep reinforcement learning for task offloading in digital twin edge networks10.1109/TNSE.2024.3350710 ↗
21. Radio resource scheduling for narrowband internet of things systems: A performance study10.1109/MNET.2018.1700386 ↗
22. A performance-aware dynamic scheduling algorithm for cloud-based IoT applications10.1016/j.comcom.2020.06.016 ↗
23. Combined communication and computing resource scheduling in sliced 5G multi-access edge computing systems10.1109/TVT.2021.3139026 ↗
24. Cooperative transmission scheduling and computation offloading with collaboration of fog and cloud for industrial IoT applications
25. Unnisa · presented at 2021 Emerging Trends in Industry 4.0 (ETI 4.0) · May 2021 · 1
26. Performance analysis and uplink scheduling for QoS-aware NB-IoT networks in mobile computing10.1109/ACCESS.2019.2908985 ↗
27. MAC for machine-type communications in industrial IoT—Part II: Scheduling and numerical results10.1109/JIOT.2020.3045831 ↗
28. Reinforcement learning for real-time optimization in NB-IoT networks10.1109/JSAC.2019.2904366 ↗
29. Deep reinforcement learning for scheduling in an edge computing-based industrial internet of things10.1155/2021/8017334 ↗
30. Joint multi-domain resource allocation and trajectory optimization in UAV-assisted maritime IoT networks10.1109/JIOT.2022.3201017 ↗
31. Edge intelligence-driven joint offloading and resource allocation for future 6G industrial internet of things
32. Joint communication, computing, and caching resource allocation in LEO satellite MEC networks10.1109/ACCESS.2023.3237701 ↗
33. Deep reinforcement learning based resource management for multi-access edge computing in vehicular networks10.1109/TNSE.2020.2978856 ↗
34. EdgeCloudSim: An environment for performance evaluation of edge computing systems
35. Management and orchestration of edge computing for IoT: A comprehensive survey10.1109/JIOT.2023.3245611 ↗
36. An intelligent hybrid method: Multi-objective optimization for MEC-enabled devices of IoE10.1016/j.jpdc.2022.09.008 ↗
37. A computation offloading strategy for multi-access edge computing based on DQUIC protocol10.1007/s11227-024-06176-9 ↗
38. An overview of queuing delay and various delay based algorithms in networks10.1007/s00607-021-00973-3 ↗
39. An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the Internet of Things
40. Advanced energy-efficient computation offloading using deep reinforcement learning in MTC edge computing10.1109/ACCESS.2020.2991057 ↗
41. Delay-aware and energy-efficient computation offloading in mobile-edge computing using deep reinforcement learning10.1109/TCCN.2021.3066619 ↗
42. Deep reinforcement learning-based task offloading and resource allocation for industrial IoT in MEC federation system10.1109/ACCESS.2023.3302518 ↗
43. A deep learning approach for task offloading in multi-UAV aided mobile edge computing10.1109/ACCESS.2022.3208584 ↗
44. Intelligent offloading and resource allocation in heterogeneous aerial access IoT networks10.1109/JIOT.2022.3161571 ↗
45. Real-time scheduling under heterogeneous routing for industrial internet of things10.1016/j.compeleceng.2020.106740 ↗
46. Deep grid scheduler for 5G NB-IoT uplink transmission