This letter investigates the proactive eavesdropping for multiple suspicious links either through interfering or assisting the links. Considering the power constraint at eavesdropper, our objective is to maximize weighted sum eavesdropping rate of multiple suspicious links via jointly optimizing their intervention strategies (jamming or relaying) and the corresponding transmit power at eavesdropper. The formulated problem is shown to be a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard in general. By identifying the separable structure of the formulated problem, we decouple the complex MINLP problem into two subproblems: 1) a jamming subproblem; and 2) a relaying subproblem. These two subproblems are then solved by further recasting them into a combinational problem and a typical concave optimization problem, respectively. Numerical simulations show that our proposed approach can achieve higher eavesdropping rate than conventional eavesdropping approaches.

With the rapid development of wireless communication, traditional cloud computing cannot fully support low-latency services, especially in sensor networks. Mobile edge computing (MEC) can improve the quality of experience of end users and save the energy consumption of mobile end devices by providing computing resources and storage space. However, it may cause discontinuity of services if these mobile end devices roam around different MEC servers’ areas. To solve the aforementioned problem, in this article, we propose an effective edge-intelligent service placement algorithm (EISPA), which transforms the service placement problem into finding a globally optimal solution via nature-inspired particle swarm optimization (PSO). Moreover, we use a shrinkage factor and combine it with the simulated annealing (SA) algorithm to adjust the position of particles in our algorithm, which aims to avoid falling into an optimal local solution to a certain extent. Performance analysis results show that the EISPA is approaching the optimal enumeration collaborative computation offloading algorithm, and system cost under energy constraints is 83.6%, 20.4%, and 20.3% lower than that in Only Local, Finding the Nearest Edge, and the genetic SA-based PSO algorithms, respectively, which proves that the EISPA has better performance.

The continuously increasing number of connected smart devices has led to the emergence of a crucial fault detection challenge to the Internet of Things (IoT). In this study, we aim to identify a method for the effective detection of faults in IoT devices. An IoT network model is first established, and a data edge verification mechanism based on blockchain is proposed; the blockchain is used to ensure that the data cannot be tampered with, and their accuracy is verified using the edge. Finally, a dataset accuracy weighted random forest based on PSO is proposed. Simulation results demonstrate that the proposed detection algorithm is both effective and efficient.

With the development of 5G technology and Internet of Things, all kinds of real life data are collected and recorded by a large number of sensors. It is of great significance to mine and analyze the hidden information in the data for applications like future prediction. However, due to interferences or instability of collection equipment, collected sensory data are often incomplete, and this incompleteness hinders the in-depth analysis of data in the cloud. Therefore, processing around missing values is significant. Relying on cloud machine learning methods is not enough to deal with the problem of missing data in the Artificial Intelligence of Things (AIoT) environment, however, edge computing provides a promising solution. In this article, gated recurrent units filling is employed at the edge nodes. A mobile edge node can not only find the historical information of the current missing data node but also acquire the data of the nodes adjacent to the missing data node. These ensure that the missing data are restored to the maximum extent at the source. The experimental results show that the missing value filling based on edge computing not only outperforms other filling methods in quality but also greatly reduces the energy consumption in AIoT.

Data in industrial informatics could be high-dimensional and mislabeled. The irrelevant or noisy features present significant challenges to high-dimensional mislabeled instances detection. Traditional methods often adopt the following method: firstly, searching for the relevant subspace; secondly, using the obtained feature subspace for mislabeled data detection. However, such a two-step method separates the relationship between feature selection and mislabeled detection, which can degrade the mislabeled detection performance. In this paper, we propose a novel sequential ensemble method SENF that aggregates the above two phases. Our method learns the sequential ensembles to obtain a refined feature subspace and improves detection accuracy by iterative sparse modeling with noise scores as the regression target attribute. Through extensive experiments on several real-world high-dimensional datasets, we show that SENF performs significantly better or at least similar to the individual baselines as well as the existing state-of-the-art label noise detection methods.

Feature selection, an essential technique in data mining, is often confined to batch learning or online idealization of data scenarios despite its significance. Existing online feature selection methods have specific assumptions regarding the data stream, such as requiring a fixed feature space with an explicit pattern and complete labeling of samples. Unfortunately, data streams generated in many real scenarios commonly exhibit arbitrarily incomplete feature spaces and scarcity labels, making existing approaches unsuitable for real applications. To fill these gaps, this study proposes a new problem called Online Feature Selection with Varying Features Spaces (OFSVF). OFSVF has a three-fold main idea: 1) it leverages Gaussian Copula to model the incomplete feature correlation in a complete latent space, encoded by continuous variables, 2) it employs a novel tree-ensemble-based approach to select the most informative features on-the-fly, and 3) it develops the underlying geometric structure of instances to establish the relationship between unlabeled and labels. Experimental results are documented to demonstrate the feasibility and effectiveness of our proposed method.