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.

With the proliferation of the Internet of Things (IoT) and the development of wireless communication technologies such as 5G, new types of services are emerging and mobile data traffic is growing exponentially. The mobile computing model has shifted from traditional cloud computing to mobile edge computing (MEC) to ensure QoS. The main feature of MEC is to “sink” network resources to the edge of the network to meet the needs of delay-sensitive and computation-intensive services, and to provide users with better services. Computation offloading is one of the major research issues in MEC. In this paper, we summarize the state of the art in task offloading in MEC. First, we introduce the basic concepts and typical application scenarios of MEC, and then we formulate the task offloading problem. In this paper, we analyze and summarize the state of research in the industry in terms of key technologies, schemes, scenarios, and objectives. Finally, we provide an outlook on the challenges and future research directions of computational offloading techniques and indicate the suggested direction of follow-up research work.

The existing methods used for hiding the iris feature data were time-consuming for iris feature extraction. Meanwhile, the information security after hiding was also low, leading to low efficiency and security of information hiding. Therefore, a method of hiding iris features data generative information based on a Gaussian fuzzy algorithm was proposed. In the preprocessing stage of the image, the weighted average method was adopted for the gray-level transformation of the iris image, and the Gaussian fuzzy algorithm was used to smooth the image. In addition, the Laplacian convolution kernel was used to sharpen the image. The iris regions were normalized. The iris feature data was extracted by employing 2D Gabor wavelet. Moreover, the iris feature data was encrypted and decrypted using the AES algorithm, and hence, effectively enhancing the security of the generative information of iris feature data. Experimental results show that the proposed method can extract iris feature information within ten seconds, and the data security coefficient is high thus the proposed method efficiently realizes the information hiding.