Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks
This paper presents an in-depth study and analysis of offloading strategies for lightweight user mobile edge computing tasks using a machine learning approach. Firstly, a scheme for multiuser frequency division multiplexing approach in mobile edge computing offloading is proposed, and a mixed-intege...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6455617 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832561471549079552 |
|---|---|
| author | Shuchen Zhou Waqas Jadoon Junaid Shuja |
| author_facet | Shuchen Zhou Waqas Jadoon Junaid Shuja |
| author_sort | Shuchen Zhou |
| collection | DOAJ |
| description | This paper presents an in-depth study and analysis of offloading strategies for lightweight user mobile edge computing tasks using a machine learning approach. Firstly, a scheme for multiuser frequency division multiplexing approach in mobile edge computing offloading is proposed, and a mixed-integer nonlinear optimization model for energy consumption minimization is developed. Then, based on the analysis of the concave-convex properties of this optimization model, this paper uses variable relaxation and nonconvex optimization theory to transform the problem into a convex optimization problem. Subsequently, two optimization algorithms are designed: for the relaxation optimization problem, an iterative optimization algorithm based on the Lagrange dual method is designed; based on the branch-and-bound integer programming method, the iterative optimization algorithm is used as the basic algorithm for each step of the operation, and a global optimization algorithm is designed for transmitting power allocation, computational offloading strategy, dynamic adjustment of local computing power, and receiving energy channel selection strategy. Finally, the simulation results verify that the scheduling strategy of the frequency division technique proposed in this paper has good energy consumption minimization performance in mobile edge computation offloading. Our model is highly efficient and has a high degree of accuracy. The anomaly detection method based on a decision tree combined with deep learning proposed in this paper, unlike traditional IoT attack detection methods, overcomes the drawbacks of rule-based security detection methods and enables them to adapt to both established and unknown hostile environments. Experimental results show that the attack detection system based on the model achieves good detection results in the detection of multiple attacks. |
| format | Article |
| id | doaj-art-067365fd37d94a679e3e323a260b8206 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-067365fd37d94a679e3e323a260b82062025-02-03T01:24:48ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/64556176455617Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing TasksShuchen Zhou0Waqas Jadoon1Junaid Shuja2Institute of International Education, Huanghuai University, Zhumadian 463000, ChinaDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, 22060 Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, 22060 Islamabad, PakistanThis paper presents an in-depth study and analysis of offloading strategies for lightweight user mobile edge computing tasks using a machine learning approach. Firstly, a scheme for multiuser frequency division multiplexing approach in mobile edge computing offloading is proposed, and a mixed-integer nonlinear optimization model for energy consumption minimization is developed. Then, based on the analysis of the concave-convex properties of this optimization model, this paper uses variable relaxation and nonconvex optimization theory to transform the problem into a convex optimization problem. Subsequently, two optimization algorithms are designed: for the relaxation optimization problem, an iterative optimization algorithm based on the Lagrange dual method is designed; based on the branch-and-bound integer programming method, the iterative optimization algorithm is used as the basic algorithm for each step of the operation, and a global optimization algorithm is designed for transmitting power allocation, computational offloading strategy, dynamic adjustment of local computing power, and receiving energy channel selection strategy. Finally, the simulation results verify that the scheduling strategy of the frequency division technique proposed in this paper has good energy consumption minimization performance in mobile edge computation offloading. Our model is highly efficient and has a high degree of accuracy. The anomaly detection method based on a decision tree combined with deep learning proposed in this paper, unlike traditional IoT attack detection methods, overcomes the drawbacks of rule-based security detection methods and enables them to adapt to both established and unknown hostile environments. Experimental results show that the attack detection system based on the model achieves good detection results in the detection of multiple attacks.http://dx.doi.org/10.1155/2021/6455617 |
| spellingShingle | Shuchen Zhou Waqas Jadoon Junaid Shuja Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks Complexity |
| title | Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks |
| title_full | Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks |
| title_fullStr | Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks |
| title_full_unstemmed | Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks |
| title_short | Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks |
| title_sort | machine learning based offloading strategy for lightweight user mobile edge computing tasks |
| url | http://dx.doi.org/10.1155/2021/6455617 |
| work_keys_str_mv | AT shuchenzhou machinelearningbasedoffloadingstrategyforlightweightusermobileedgecomputingtasks AT waqasjadoon machinelearningbasedoffloadingstrategyforlightweightusermobileedgecomputingtasks AT junaidshuja machinelearningbasedoffloadingstrategyforlightweightusermobileedgecomputingtasks |