NAND flash chips have been innovated from two-dimension (2D) design which is based on planar NAND cells to three-dimension (3D) design which is based on vertical NAND cells. Two types of NAND flash technologies–charge-trap (CT) and floating-gate (FG) are presented in this paper to introduce NAND flash designs in detail. The physical characteristics of CT-based and FG-based 3D NAND flashes are analyzed. Moreover, the advantages and disadvantages of these two technologies in architecture, manufacture, interference and reliability are studied and compared.

Data island and information opacity are two major problems in collaborative administration. Blockchain has the potential to provide a trustable and transparent environment encouraging data sharing among administration members. However, the blockchain only stores Hash values and transactions in blocks which makes it unable to store big data and trace their changes. In this paper, a labor arbitration scheme based on blockchain was proposed to share labor arbitration data. In the system, a collaborative administration scheme that provides a big data storage model combined blockchain and interplanetary file system ( IPFS) is designed. It can store big data and share these data among different parties. Moreover, a file version control mechanism based on blockchain is designed to manage the data changes in IPFS network. It creates a tracing chain that consists of many IPFS objects to track changes of stored data. The relationship of previous and current IPFS objects recorded by blockchain can describe the changes of administration data and trace the data operations. The proposed platform is used in Rizhao City in China, and the experiment result shows collaborative administration scheme achieves traceability with high throughput and is more efficient than traditional hypertext transfer protocol ( HTTP) way to share data.

Attacks on web servers are part of the most serious threats in network security fields. Analyzing logs of web attacks is an effective approach for malicious behavior identification. Traditionally, machine learning models based on labeled data are popular identification methods. Some deep learning models are also recently introduced for analyzing logs based on web logs classification. However, it is limited to the amount of labeled data in model training. Web logs with labels which mark specific categories of data are difficult to obtain. Consequently, it is necessary to follow the problem about data generation with a focus on learning similar feature representations from the original data and improve the accuracy of classification model. In this paper, a novel framework is proposed, which differs in two important aspects: one is that long short-term memory (LSTM) is incorporated into generative adversarial networks (GANs) to generate the logs of web attack. The other is that a data augment model is proposed by adding logs of web attack generated by GANs to the original dataset and improved the performance of the classification model. The results experimentally demonstrate the effectiveness of the proposed method. It improved the classification accuracy from 89.04% to 95.04%.

In order to detect and cancel the self-interference (SI) signal from desired binary phase-shift keying (BPSK) signal, the polarization-based optimal detection (POD) scheme for cancellation of digital SI in a full-duplex (FD) system is proposed. The POD scheme exploits the polarization domain to isolate the desired signal from the SI signal and then cancel the SI to obtain the interference-free desired signal at the receiver. In FD communication, after antenna and analog cancellation, the receiver still contains residual SI due to non-linearities of hardware imperfections. In POD scheme, a likelihood ratio expression is obtained, which isolates and detects SI bits from the desired bits. After isolation of these signal points, the POD scheme cancels the residual SI. As compared to the conventional schemes, the proposed POD scheme gives significantly low bit error rate (BER), a clear constellation diagram to obtain the boundary between desired and SI signal points, and increases the receiver's SI cancellation performance in low signal to interference ratio (SIR) environment.

Face recognition has been a hot-topic in the field of pattern recognition where feature extraction and classification play an important role. However, convolutional neural network (CNN) and local binary pattern (LBP) can only extract single features of facial images, and fail to select the optimal classifier. To deal with the problem of classifier parameter optimization, two structures based on the support vector machine (SVM) optimized by artificial bee colony (ABC) algorithm are proposed to classify CNN and LBP features separately. In order to solve the single feature problem, a fusion system based on CNN and LBP features is proposed. The facial features can be better represented by extracting and fusing the global and local information of face images. We achieve the goal by fusing the outputs of feature classifiers. Explicit experimental results on Olivetti Research Laboratory (ORL) and face recognition technology (FERET) databases show the superiority of proposed approaches.

While solving unimodal function problems, conventional meta-heuristic algorithms often suffer from low accuracy and slow convergence. Therefore, in this paper, a novel meta-heuristic optimization algorithm, named proton-electron swarm (PES), is proposed based on physical rules. This algorithm simulates the physical phenomena of like-charges repelling each other while opposite charges attracting in protons and electrons, and establishes a mathematical model to realize the optimization process. By balancing the global exploration and local exploitation ability, this algorithm achieves high accuracy and avoids falling into local optimum when solving target problem. In order to evaluate the effectiveness of this algorithm, 23 classical benchmark functions were selected for comparative experiments. Experimental results show that, compared with the contrast algorithms, the proposed algorithm cannot only obtain higher accuracy and convergence speed in solving unimodal function problems, but also maintain strong optimization ability in solving multimodal function problems.

With the rapid development of wireless communication technology and the explosive growth of mobile data traffic,  more and more users are eager to get faster and better internet access. In order to meet the needs of users, energy  and spectrum utilization are becoming more and more important as new challenges in wireless communication  networks. In recent years, reconfigurable intelligent surface ( RIS ) technology has been proposed in a  programmable intelligent way to improve the performance and quality of wireless communication systems. In  addition, the RIS performs better in terms of energy efficiency than other technologies. Therefore, the RIS has  become research hotspot rapidly because of its unique wireless communication ability. This paper aims to review the  RIS, including channel model, design for transmitter and receiver, information theory, and the latest development  of RIS-assisted multiple-input multiple- output (MIMO) systems. The applications of RISs in physical layer  security, device to device (D2D) and cell coverage extension are also introduced in detail. In addition, we discuss  major research challenges related to the RIS. Finally, the potential research directions are proposed.

Robot grabbing has been successfully applied to a range of challenging environments but met the resource bottleneck. To answer this question, a hybrid cloud-based robot grabbing system is proposed, which supports centralized bin-picking management and deployment, large-scale storage, and communication technologies. The hybrid cloud combines the powerful computational capabilities through massive parallel computation and higher data storage facilities in the public cloud with data privacy in the private data center. The benchmark tasks against a public cloud based on robot grabbing method are evaluated, whose results indicate that the whole system reduces the data collection time and increases elastic resource scheduling and is adapted in the real industry.

At present, there is an urgent need for blockchain interoperability technology to realize interconnection between various blockchains, data communication and value transfer between blockchains, so as to break the ‘ value silo’ phenomenon of each blockchain. Firstly, it lists what people understand about the concept of interoperability. Secondly, it gives the key technical issues of cross-chain, including cross-chain mechanism, interoperability, eventual consistency, and universality. Then, the implementation of each cross-chain key technology is analyzed, including Hash-locking, two-way peg, notary schemes, relay chain scheme, cross-chain protocol, and global identity system. Immediately after that, five typical cross-chain systems are introduced and comparative analysis is made. In addition, two examples of cross-chain programmability and their analysis are given. Finally, the current state of cross-chain technology is summarized from two aspects: key technology implementation and cross-chain application enforcement. The cross-chain technology as a whole has formed a centralized fixed mechanism, as well as a trend of modular design, and some of the solutions to mature applications were established in the relevant standards organizations, and the cross-chain technology architecture tends to be unified, which is expected to accelerate the evolution of the open cross-chain network that supports the real needs of the interconnection of all chains.

Blockchain technology is used in edge computing ( EC) systems to solve the security problems caused by single point of failure ( SPOF) due to data loss, task execution failure, or control by malicious nodes. However, the disadvantage of blockchain is high latency, which contradicts the strict latency requirements of EC services. The existing single-level sharded blockchain system ( SLSBS) cannot provide different quality of service for different tasks. To solve these problems, a multi-level sharded blockchain system ( MLSBS) based on genetic algorithm ( GA) is proposed. The shards are classified according to the delay of the service, and the parameters such as the shard size of different shards are different. Using the GA, the MLSBS obtains the optimal resource allocation strategy that achieves maximum security. Simulation results show that the proposed scheme outperforms SLSBS.

In order to meet various challenges in the Internet of things (IoT), such as identity authentication, privacy preservation of distributed data and network security, the integration of blockchain and IoT became a new trend in recent years. As the key supporting technology of blockchain, the consensus algorithm is a hotspot of distributed system research. At present, the research direction of the consensus algorithm is mainly focused on improving throughput and reducing delay. However, when blockchain is applied to IoT scenario, the storage capacity of lightweight IoT devices is limited, and the normal operations of blockchain system cannot be guaranteed. To solve this problem, an improved version of Raft (Imp Raft) based on Raft and the storage compression consensus (SCC) algorithm is proposed, where initialization process and compression process are added into the flow of Raft. Moreover, the data validation process aims to ensure that blockchain data cannot be tampered with. It is obtained from experiments and analysis that the new proposed algorithm can effectively reduce the size of the blockchain and the storage burden of lightweight IoT devices.

Currentlyradio frequency identification (RFID) technology has been widely used in many kinds of applications. Store retailers use RFID readers with multiple antennas to monitor all tagged items. However, because of the interference from environment and limitations of the radio frequency technology, RFID tags are identified by more than one RFID antenna, leading to the false positive readings. To address this issue, we propose a RFID data stream cleaning method based on K-means to remove those false positive readings within sampling time. First, we formulate a new data stream model which adapts to our cleaning algorithm. Then we present the preprocessing method of the data stream model, including sliding window setting, feature extraction of data stream and normalization. Next, we introduce a novel way using K-means clustering algorithm to clean false positive readings. Last, the effectiveness and efficiency of the proposed method are verified by experiments. It achieves a good balance between performance and price.

Moving data from cloud to the edge network can effectively reduce traffic burden on the core network, and edge collaboration can further improve the edge caching capacity and the quality of service ( QoS). However, it is difficult for various edge caching devices to cooperate due to the lack of trust and the existence of malicious nodes. In this paper,blockchain which has the distributed and immutable characteristics is utilized to build a trustworthy collaborative edge caching scheme to make full use of the storage resources of various edge devices. The collaboration process is described in this paper, and a proof of credit (PoC) protocol is proposed, in which credit and tokens are used to encourage nodes to cache and transmit more content in honest behavior. Untrusted nodes will pay for their malicious actions such as tampering or deleting cached data. Since each node chooses strategy independently to maximize its benefits in an environment of mutual influence, a non-cooperative game model is designed to study the caching behavior among edge nodes. The existence of Nash equilibrium (NE) is proved in this game, so the edge server (ES) can choose the optimal caching strategy for all collaborative devices, including itself, to obtain the maximum rewards. Simulation results show that the system can save mining overhead as well as organize a trusted collaborative edge caching effectively.  

Vehicular Ad-hoc networks (VANETs) are kinds of mobile Ad-hoc networks (MANETs), which consist of mobile vehicles with on-board units (OBUs) and roadside units (RSUs). With the rapid development of computation and communication technologies, peripheral or incremental changes in VANETs evolve into a revolution in process. Cloud computing as a solution has been deployed to satisfy vehicles in VANETs which are expected to require resources (such as computing, storage and networking). Recently, with special requirements of mobility, location awareness, and low latency, there has been growing interest in research into the role of fog computing in VANETs. The merging of fog computing with VANETs opens an area of possibilities for applications and services on the edge of the cloud computing. Fog computing deploys highly virtualized computing and communication facilities at the proximity of mobile vehicles in VANET. Mobile vehicles in VANET can also demand services of low-latency and short-distance local connections via fog computing. This paper presents the current state of the research and future perspectives of fog computing in VANETs. Moreover, we discuss the characteristics of fog computing and services based on fog computing platform provided for VANETs. In this paper, some opportunities for challenges and issues are mentioned, related techniques that need to be considered have been discussed in the context of fog computing in VANETs. Finally, we discuss about research directions of potential future work for fog computing in VANETs. Within this article, readers can have a more thorough understanding of fog computing for VANETs and the trends in this domain.

Electrical connectors play a significant role in the electronic and communication systems. As they are often exposed in the atmosphere environment, it is extremely easy for them to cause electrical contact failure. It is essential to carry out the reliability modeling and predict the lifetime. In the present work, the accelerated lifetime testing method which is on account of the uniform design method was designed to obtain the degradation data under multiple environmental stresses of temperature and particulate contamination for electrical connectors. Based on the degradation data, the pseudo life can be acquired. Then the reliability model was established by analyzing the pseudo life. Accordingly, the reliability function and reliable lifetime function were set up, and the reliable lifetime of the connectors under the multiple environment stresses of temperature and particulate contamination could be predicted for electrical connectors.

Anomaly detection in smart grid is critical to enhance the reliability of power systems. Excessive manpower has to be involved in analyzing the measurement data collected from intelligent motoring devices while performance of anomaly detection is still not satisfactory. This is mainly because the inherent spatio-temporality and multi-dimensionality of the measurement data cannot be easily captured. In this paper, we propose an anomaly detection model based on encoder-decoder framework with recurrent neural network (RNN). In the model, an input time series is reconstructed and an anomaly can be detected by an unexpected high reconstruction error. Both Manhattan distance and the edit distance are used to evaluate the difference between an input time series and its reconstructed one. Finally, we validate the proposed model by using power demand data from University of California, Riverside (UCR) time series classification archive and IEEE 39 bus system simulation data. Results from the analysis demonstrate that the proposed encoder-decoder framework is able to successfully capture anomalies with a precision higher than 95%.

Lithium-ion batteries are the main power supply equipment in many fields due to their advantages of no memory, high energy density, long cycle life and no pollution to the environment. Accurate prediction for the remaining useful life (RUL) of lithium-ion batteries can avoid serious economic and safety problems such as spontaneous combustion. At present, most of the RUL prediction studies ignore the lithium-ion battery capacity recovery phenomenon caused by the rest time between the charge and discharge cycles. In this paper, a fusion method based on wasserstein generative adversarial network (GAN) is proposed. This method achieves a more reliable and accurate RUL prediction of lithium-ion batteries by combining the artificial neural network (ANN) model which takes the rest time between battery charging cycles into account and the empirical degradation models which provide the correct degradation trend. The weight of each model is calculated by the discriminator in the wasserstein GAN model. Four data sets of lithium-ion battery provided by the NASA Ames Research Center are used to prove the feasibility and accuracy of the proposed method.

As a special type of distributed denial of service (DDoS) attacks, the low-rate DDoS (LDDoS) attacks have characteristics of low average rate and strong concealment, thus, it is hard to detect such attacks by traditional approaches. Through signal analysis, a new identification approach based on wavelet decomposition and sliding detecting window is proposed. Wavelet decomposition extracted from the traffic are used for multifractal analysis of traffic over different time scale. The sliding window from flow control technology is designed to identify the normal and abnormal traffic in real-time. Experiment results show that the proposed approach has advantages on detection accuracy and timeliness.

Considering the shortcomings of the existing vehicle-to-vehicle (V2V) communication antennas, this paper proposes a regular hexagon broadband microstrip antenna. By loading shorting pins and etching V-shape slots with different size at each angle of the regular hexagon patch, it realizes impedance matching and obtains better impedance bandwidth. The simulated results show that the relative bandwidth of this antenna reaches 35.55%, covers the frequency band of 4.74 GHz to 6.79 GHz. The antenna acquires an omni-directional radiation pattern in the horizontal plane whose out of roundness is less than 0.5 dB. In addition, the antenna is manufactured and tested, whose tested results are basically consistent with simulated results. Because the height of antenna is 3 mm, it is easy to be hidden on roof of a vehicle for V2V communication.