Device-free localization (DFL), as an emerging technology that locates targets without any attached devices via wireless sensor networks, has spawned extensive applications in the Internet of Things (IoT) field. For DFL, a key problem is how to extract significant features to characterize raw signals with different patterns associated with different locations. To address this problem, in this paper, the DFL problem is formulated as an image classification problem. Moreover, we design a three-layer convolutional autoencoder (CAE) neural network to perform unsupervised feature extraction from raw signals followed by supervised fine-tuning for classification. The CAE combines the advantages of a convolutional neural network (CNN) and a deep autoencoder (AE) in the feature learning and signals reconstruction, which is expected to achieve good performance for DFL. The experimental results show that the proposed approach can achieve a high localization accuracy rate of 100% for a reasonable grid size on the raw real-world data, i.e., the collected raw data without added Gaussian noise, and is robust to noisy data with a signal-to-noise ratio greater than -5 dB. Additionally, its time cost for the classification of a single activity is 4 ms, which is fast enough for the IoT applications. The proposed approach outperforms the deep CNN and AE in terms of localization accuracy and robust ability against noise.

Fault detection is a fundamental requirement for Industrial Internet of Things (IIoT), such as the process industry. This article first reviews the recent studies focusing on applying the fault detection techniques to the IIoT networks. However, we find that numerous studies focus on the resource utilization and workload allocation. The fault detection toward IIoT facilities is still in its immature stage because the existing approaches are not accurate enough for the stringent fault detection in IIoT networks. To this end, we present a novel algorithm, named Gaussian Bernoulli restricted Boltzmann machines (GBRBMs)-based deep neural network (DNN), to transform the fault detection into a classification problem. The real trace-driven experiments show that the proposed scheme outperforms other baseline machine learning methods. We anticipate that this article can inspire blooming studies on the related topics of smart IIoT networks.

In practical device-free localization (DFL) applications, for enlarging the monitoring area and improving localization accuracy, too many nodes need to be deployed, which results in a large volume of DFL data with high dimensions. This arises a key problem of seeking an accurate and efficient approach for DFL. In order to address this problem, this paper regards DFL as a problem of sparse-representation-based classification; builds a sparse model; and then proposes two sparse-coding-based algorithms. The first algorithm, sparse coding via the iterative shrinkage-thresholding algorithm (SC-ISTA), is efficient for handling high-dimensional data. And then, subspace techniques are further utilized, followed by performing sparse coding in the low-dimensional signal subspace, which leads to the second algorithm termed subspace-based SC-ISTA (SSC-ISTA). Experiments with the real-world data set are conducted for single-target and multi-target localization, and three typical machine learning algorithms, deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, are compared. Experimental results show that both SC-ISTA and SSC-ISTA can achieve high localization accuracies of 100% and are robust to noisy data when SNR is greater than 10 dB, and the time costs for sparse coding of SC-ISTA and SSC-ISTA are 2.1 × 10 ^-3 s and 2.1 × 10 ^-4 s respectively, which indicates that the proposed algorithms outperform the other three ones.

With consumer electronics (CE) development, green Internet-of-Vehicles (IoV)-based renewable energy systems have attracted an upsurge in interest. Nonetheless, efficient energy management and high renewable energy efficiency are facing crucial challenges. To tackle such issues, motivated by the virtual power plant (VPP), we propose a smart energy management scheme, integrating the merits of software and hardware. Particularly, leveraging electrical vehicles (EVs) and reconfigurable CE chips, the proposed system enables EVs to autonomously charge or discharge batteries for intelligent storing of surplus electricity generated by renewable resources. However, such a system requires frequent sharing of sensitive data (i.e., EV users’ identification information, locations, etc.) between the control center and CE chips, resulting in privacy issues and communication overhead. For this, we propose a federal EV decision learning (FEVDL) approach. FEVDL allows EVs to share trained models without revealing sensitive data. FEVDL can achieve a competitive accuracy of about 99% compared with isolated edge learning. Meanwhile, it separately improves inference accuracy by about 4%, 15%, and 25% under three challenging conditions. Therefore, a privacy-preserving green IoV-VPP system can efficiently operate the distributed EV batteries as a large-scale power-storage facility.