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.
Highlights
Mobile edge computing and storage (MECC) technology, as a new computing and storage paradigm, deploys centralized cloud data centers in a distributed form on the side of the access network close to the data source, attempting to deeply integrate Internet Service Providers (ISPs), mobile operators, and IoT devices to perform many operations such as service awareness, data transmission, information processing, and control optimization near the data source
For lightweight IoT devices, frequent storage and computation operations will consume a lot of energy, which is a great challenge for battery-powered devices only, and the development of battery technology still cannot meet the energy demand of IoT applications [2]. erefore, without a new way to supply energy or improve energy efficiency, it will greatly limit the application of MECC in such scenarios
With the extension of mobile edge computing in composition to multiaccess edge computing, the addition of various heterogeneous networks makes the stability of the system a great challenge, and it is impossible to establish a completely reliable computation offloading model without transmission errors and failures occurring, so this paper introduces the failures during computation offloading into the research to make the computation offloading more robust
Summary
Mobile edge computing and storage (MECC) technology, as a new computing and storage paradigm, deploys centralized cloud data centers in a distributed form on the side of the access network close to the data source, attempting to deeply integrate Internet Service Providers (ISPs), mobile operators, and IoT devices to perform many operations such as service awareness, data transmission, information processing, and control optimization near the data source. With the demand of energy consumption of mobile devices, this paper discusses the control strategy of using SWIPT technology in mobile edge computing, which provides more options for MEC to make accurate offloading decisions and efficient resource allocation; due to the many problems in the implementation of wireless energy-carrying communication, with the progress and development of multiple antenna technology of spatial diversity and multifrequency antenna technology, this paper prompts to adopt a similar frequency division multiplexing approach to open a new research path for wireless energy-carrying communication. The good performance of the frequency division SWIPT technique proposed in this paper for energy minimization scheduling strategy in mobile edge computing offload is demonstrated through simulation analysis and comparison
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