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Herein, we propose an effective strategy for preparing Zr@C composite electrodes. First, the UiO-66 crystals are synthesized and introduced into polyimide (PI) by in-situ polymerization and thermal imidization to fabricate composite films. After carbonization, the Zr@C composites are obtained, and the influences of UiO-66 content and carbonization temperature on the structures and electrochemical behaviors are investigated. After optimization, the specific capacitance of the PI/5%UiO-66–800 reaches a maximum of 345F/g at 0.5 A/g, superior to corresponding PI matrix and UiO-66 crystals. As the current density gradually increases to 10 A/g, the specific capacitance drops to 252F/g with a retention of 73 %. Additionally, the PI/5%UiO-66–800 composites are assembled into symmetric supercapacitors, which show a favorable specific capacitance of 271.2 F/g and an outstanding cycling stability about 99 % after 10,000 charge–discharge cycles. Furthermore, the supercapacitor device has delivered a high specific energy of 37.67 Wh kg−1 with a specific power of 500 W kg−1. Moreover, only one coin cell can successfully power a LED, suggesting its promising potential as an alternative electrode material for high-performance supercapacitor applications. Thus, this study demonstrates a simple way to boost capacitive performances of Zr@C composite electrodes for power sources.

The photovoltaic industry is a strategic and sunrise industry with international competitive advantages. Driven by policy guidance and market demand, the new energy industry represented by the photovoltaic industry has been a significant emerging industry in developing the national economy and people’s livelihood. Stable photovoltaic power generation capacity supply is a critical issue in the photovoltaic industry. With the popularization of industrial Internet technology and Internet of things technology, more and more academic and industrial circles begin to introduce new technologies to provide the latest research results and solutions for the photovoltaic industry. Electroluminescence is a standard detection method for photovoltaic production in the application of solar energy production. This method uses human vision to detect whether the solar silicon unit is defective. In this article, due to the three core pain points in traditional electroluminescence detection: low efficiency of offline identification, low accuracy and accuracy of data detection, and no online diagnosis and prediction, we carry out ISEE research based on edge computing unit. ISEE uses the edge device to collect the real-time video image of the solar panel through the camera. Then it uses the powerful neural network processing unit module of the edge computing unit, combined with the convolutional neural network algorithm transplanted to the edge, to detect the defects of solar panels in real time. It completes the research on intelligent detection of photovoltaic power generation production defects based on the Internet of Things. After a large number of experimental design verification, ISEE effectively improves the automation degree and identification accuracy in the production and detection process of solar photovoltaic cells and reduces the cost of operation and maintenance. The accuracy rate reaches 93.75%, which has significant theoretical research significance and practical application value.

With the advent of the era of intelligent Internet of Things (IoT), more and more personal information are collected in the process of deploying IoT in various domains. This brings the problem of data protection in IoT. To address this problem, we propose a data protection algorithm, called DynaPro, based on differential privacy for dynamic wireless sensor networks (WSN), which is the sensor layer of IoT. DynaPro has addressed two issues in data protection of wireless sensor networks. The first issue is the dynamic topology of the network. In order to solve this problem, DynaPro has adopted methods like time window, hierarchical sampling, and graph similarity to process the snapshots of the dynamic network topology. The second issue is addition of noise in differential privacy. To address this issue, DynaPro uses Hierarchical Random Graph (HRG) as middleware to apply differential privacy protection. Instead of adding noise to network data and topology directly, DynaPro adds noise to HRG. In this way, DynaPro can hide the network topology but retain the necessary structural information to support data analysis. Theoretical analysis and experimental results show that DynaPro can preserve the important network features of the original network topology under the premise of the differential privacy protection model.

With the rapid development of information science and the Internet of Things (IoT), people have an unprecedented ability to collect and share data, especially the various sensors as the entrance to data collection. At the same time, edge computing has begun to grasp the public's attention because of the difficult challenges of massive equipment access and massive data. Although such large amount of data provides a huge opportunity for information discovery, the privacy leakage has also been concerned. When the data publisher publishes various statistics, the attacker can obtain the statistical rules in the data by simply using the query function without contacting the user or the data publisher. Therefore, how to protect the data privacy of statistical information has become the focus of attention. In this paper, we proposed a partitioned histogram data publishing algorithm based on wavelet transform. Firstly, a partitioning algorithm based on greedy algorithm is used to obtain a better partition structure. Then, we use wavelet transform to add noise. Finally, for the authenticity and usability of histogram, we get the reductive original histogram structure. On the one hand, our algorithm can reduce the complexity of wavelet tree constructed by wavelet transform. On the other hand, the query noise changes from linear growth to multiple logarithm growth. So the accuracy of histogram counting query is improved. Experiments show that our algorithm has the significant improvement in data availability.

In this paper, a novel smooth group L1/2 (SGL1/2) regularization method is proposed for pruning hidden nodes of the fully connected layer in convolution neural networks. Usually, the selection of nodes and weights is based on experience, and the convolution filter is symmetric in the convolution neural network. The main contribution of SGL1/2 is to try to approximate the weights to 0 at the group level. Therefore, we will be able to prune the hidden node if the corresponding weights are all close to 0. Furthermore, the feasibility analysis of this new method is carried out under some reasonable assumptions due to the smooth function. The numerical results demonstrate the superiority of the SGL1/2 method with respect to sparsity, without damaging the classification performance.

With the development of Internet of Things, many applications need to use people’s location information, resulting in a large amount of data need to be processed, called big data. In recent years, people propose many methods to protect privacy in the location-based service aspect. However, existing technologies have poor performance in big data area. For instance, sensor equipments such as smart phones with location record function may submit location information anytime and anywhere which may lead to privacy disclosure. Attackers can leverage huge data to achieve useful information. In this article, we propose noise-added selection algorithm, a location privacy protection method that satisfies differential privacy to prevent the data from privacy disclosure by attacker with arbitrary background knowledge. In view of Internet of Things, we maximize the availability of data and algorithm when protecting the information. In detail, we filter real-time location distribution information, use our selection mechanism for comparison and analysis to determine privacy-protected regions, and then perform differential privacy on them. As shown in the theoretical analysis and the experimental results, the proposed method can achieve significant improvements in security, privacy, and complete a perfect balance between privacy protection level and data availability.

Internal reliability and external safety of Wireless Sensor Networks (WSN) data transmission have become increasingly outstanding issues with the wide applications of WSN. This paper proposes a new method for access control and mitigation of interfering noise in time synchronization environments. First, a formal definition is given regarding the impact interference noise has on the clock skew and clock offset of each node. The degree of node interference behavior is estimated dynamically from the perspective of time-stamp changes caused by the interference noise. Secondly, a general access control model is proposed to resist invasion of noise interference. A prediction model is constructed using the Bayesian method for calculating the reliability of neighbor node behavior in the proposed model. Interference noise, which attacks the time synchronization, is regarded as the key factor for probability estimation of the reliability. The result of the calculations determines whether it is necessary to initiate synchronization filtering. Finally, a division of trust levels with bilinear definition is employed to lower interference noise and improve the quality of interference detection. Experimental results show that this model has advantages in system overhead, energy consumption and testing errors, compared to its counterparts. When the disturbance intensity of a WSN increases, the proposed optimized algorithm converges faster with a lower network communication load.