[Retracted] Machine Learning‐Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks

Abstract

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.

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

“…is mechanism requires more security protections due to the dynamic environments and heterogeneous nature of end devices [129,130]. Offloading security tasks on the MEC server opens up many privacy issues, and protecting end-node privacy is more challenging compared to MCC [30].…”

Task Offloading and Resource Allocation Mechanism of Moving Edge Computing in Mining Environment

“…They use this framework to solve the problems of information asymmetry in fog computing services. By applying deep learning to the allocation of fog computing resources, Nguyen Cong Luong et al can achieve optimal auction design and maximize the revenue of resource suppliers [13].Shuchen Zhou et al proposed a machine learning -based offloading strategy [14]. This strategy includes transmission power allocation, computational offload strategy, local computational power dynamic adjustment and global optimization strategy of receiving energy channel selection.…”

“…Mine application background Reference [9] X X X Reference [10] O X X Reference [11] O O X Reference [12] O X X Reference [13] X X X Reference [14] O O X…”

Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution

“…However, the user access patterns, mobility, and network conditions at the network edge need to be predicted [ 15 , 16 ]. Moreover, ML techniques can also provide intelligent input to the VNF-AS to optimize the decision of what, when, and where to multicast video streams based on user access patterns and content features [ 13 , 17 ]. Caching is essential for users of distance learning with limited network connectivity.…”