This PDF file contains the front matter associated with SPIE Proceedings Volume 12455 including Title page, Copyright information, Table of Contents, and Conference Committee Page.

The fluent ship targets with micro-motion which is caused by oceanic waves leading to defocused images. Due to the large size ship, there is a multi-component echo signal in one range bin, thus it is crucial to extract the micro-Doppler (m-D) features quickly and precisely to refocus the images. This paper puts forward a novel micro-motion feature extraction and estimation method. The method is composed of two steps, and the first step is preprocessing to do the Short-Time Fourier Transform (STFT). After that, we propose a new form of synchrosqueezing transform to concentrate the energy spread curves which can be established as a state translation model. Then in the second step, we use the RFS-based Bernoulli filter to estimate the parameters of the multi-component signal. In this step, the method avoids the disturbance of stray points and empty areas so that the m-D parameters can be estimated accurately. The experimental results prove the availability of the proposed method and the accuracy of the estimation of m-D parameters.

The micro-Doppler modulation feature in radar echo can reflect the geometric structure and motion characteristics of targets, and is widely used in target parameter extraction and pattern recognition. Aiming at the problem of low micro-Doppler resolution under the condition of short dwell time, a sample missing signal reconstruction algorithm based on factor analysis (FA) model is proposed. Firstly, FA is used to describe the unknown complete signal, and then the mathematical model between the sample missing observation signal and the unknown complete signal is established. Then Bayesian theory is used to transform it into a full probability model. The model is solved by variable Bayesian expectation maximization (VBEM), so as to obtain the reconstruction of the complete signal. At the same time, for the problem of determining the number of FA factors, the automatic correlation determination (ARD) prior is introduced into the model to realize the automatic determination of the number of factors. Experimental results based on measured data show that the proposed method can achieve better reconstruction performance than the traditional compressive sensing (CS) method.

Traditional maneuvering target tracking algorithms assume that the target motion model is one fixed or a limited number of them. For high-speed and strong maneuvering targets, when the model set cannot cover the maneuvering mode or the deviation is large, the performance of the tracker will drop rapidly. Therefore, this paper proposes a new maneuvering target tracking method – a random motion model based on Random Kalman Filtering (RKF). This algorithm uses a random model to describe the target maneuver, which is more widely used than traditional algorithms and is more stable when the target maneuver is not covered by the model set. Compared with the traditional single model, the classic related algorithm is Kalman Filtering (KF), the new method significantly improves the tracking effect when the target is maneuvering. At the same time, when the model set of the Interacting Multiple Model algorithm (IMM) does not match the real maneuvering state, the tracking error of the new method is smaller than IMM and there is no divergence trend.

With the rapid development of China's economy, the development speed and scale of transportation engineering have also expanded rapidly. But the problem of environmental pollution problems have also gradually emerged, and the use of environmental protection materials has become an inevitable trend of green transportation development. This paper puts forward the traffic optimization method at five-way intersection based on green building materials and signal processing. Firstly, the green and environment protection hot-melt reflective marking paint and color asphalt concrete pavement materials are used to construct the travel space of motor vehicles, non-motor vehicles and pedestrians at five-way intersection. Then, on this basis, the phase design method based on clipping method and the corresponding signal timing method are proposed to optimize the traffic flow travel time at five-way intersection. Finally, the evaluation and analysis is done by the VISSIM traffic simulation software, the results show that this method based on green building materials effectively defines the travel space of five-way intersection. On this premise, the traffic efficiency is further improved through signal processing optimization technology, which effectively reduces the number of conflict point at the five-way intersection and provides a basis for the research on the optimal control of deformed intersections based on green building materials and signal processing.

In order to improve the localization accuracy in complex environments, a sound source localization method based on dual microarrays (DMA) and deep learning is studied. Generalized cross correlation-phase transform (GCCPHAT) sequence and the maximum value information of the sequence are used as localization cues, the three-dimensional coordinates of the sound source are used as the output of the network, and the mapping rules from input features to output are learned through the improved CNN network based on VGG16 network structure (referred to as V_CNN for short). Through simulation experiments, the sound source localization method based on circular array and V_CNN, the sound source localization method based on dual microarrays and ordinary convolutional neural network (CNN), and the sound source localization method based on dual microarrays and V_CNN are compared. The experimental results show that the sound source localization method in this paper has high localization accuracy under different noise and reverberation environments.

Identification of cascaded dynamic nonlinear systems has received more and more attention since it can be applied to characterize a wide class of engineering problems. However, this problem is inherently non-convex, and few efforts have been devoted to identifying these systems. In this paper, we propose a novel non-convex optimization method for identifying the cascaded nonlinear systems. The identification problem is modeled as a multi-objective consensus problem. Numerical simulation shows that the proposed algorithm is able to alleviate the non-convex characteristic and identifying the nonlinear dynamic cascade systems.

The signal detection is presented based on deep learning (DL) for Multiple-in-Multiple-out Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Systems. In MIMO-OFDM systems, a receiver is designed to eliminate successive interference cancellation based on DL for multiple users. The signal detection and channel estimation are carried out using deep neural network (DNN) which is trained offline depend on simulation data. And the symbols online are recovered directly. The simulation results show that Deep learning (DL) method is better than those traditional methods for channel estimation. The error propagation effects are reduced by DNN in the signal detector. The inter-symbol interference (ISI) of systems is serious, which shows that the DL approach can achieve the better performance by the DL approach than the maximum likelihood approach.

This paper studies the overall landscape patterns in Huai’an-Yangzhou section along the Grand Canal with RS (Remote Sensing) and GIS (Geographic Information System) based on ZY-3 satellite images of this area. During the study, the landscapes of Huai’an-Yangzhou section along the Grand Canal have been classified into six types, i.e. farmland landscape, garden and forest landscape, water landscape, architectural landscape, road landscape, and other landscapes. Meanwhile PD (Patch Density), perimeter area fractal dimension (PAFRAC), and AI (agglomeration index) of different landscapes in these two heritage sites have been calculated, whose results show that farmland landscape, architectural landscape, and water landscape are the three most obvious landscapes with a high degree of concentration in the two heritage areas. The findings can provide basic data for landscape protection and management of Huai’an-Yangzhou section along the Grand Canal.

This paper studies the simulation and algorism of acousto-optic method used in the wireless communication between water and air. Binary frequency shift keying (2FSK) is selected as the way of modulate underwater acoustic signals. Then, laser interferometry was introduced to detect the signal of underwater acoustic communication. Further, transmission information was demodulated by the differential-and-cross-multiplying (DCM) algorism. Information transmission rate of 2FSK was analyzed through the numerical simulation. The results shows that the maximum of information transmission rate is 67 bit/s at error free.

Aiming at the advantages of less I2C interface lines and simplified control mode, I2C interface is used to read the data of sensor equipment. Firstly, the sensor node is composed of six modules: power supply module, sensor module, calculation module, storage module, communication module and embedded software system, and the functions of these modules are described in detail. Then the master-slave devices corresponding to I2C drive are introduced; Then the I2C protocol and the functions of bme680 sensor used in this scheme are summarized, including the bus composition, communication principle and bus physical topology of I2C. The functions of bme680 sensor introduce bme680 sensor, pin layout and pin assignment. The whole I2C driver design includes the configuration process of bme680 sensor, read-write timing and driver design. The I2C bus device driver is designed, and the driver that reads the data of bme680 sensor device in the sensor node with the new Tang processor m263kiaae is implemented. The driver can successfully read bme680 sensing data.

In this paper, the content related to scattering information is mainly focused in the presence of complex additive Gaussian white noise (CAGWN). In our previous works, the theoretical formula of the scattering information is already derived with integral operation. To get more brief expressions, we derive the approximate expressions in cases where the signal-to-noise ratio (SNR) is high and low separately. Noting that the a posteriori probability density function (PDF) obeys Gaussian distribution in the case of high SNR so it’s natural to derive the expression of the scattering information’s approximate expression on account of information theory. In low SNR regime, an approximate formula can be obtained with the reason of signal is submerged in the noise. Numerical results are presented to verify the effectiveness of the proposed approximation.

The widespread deployment of wireless devices in indoor environments has made location-based services a hot research topic. Indoor positioning based on received signal strength is the key to providing accurate location services among them. But it needs to build a fingerprint database for the sensing area. Therefore, whether it is to establish or update the fingerprint map, a large number of RSS values of the reference points need to be sampled. This process is time-consuming and labor-intensive, with a huge amount of work. To solve this problem, researchers collect RSSs of some reference points and use them to interpolate other points to form a map of the entire area. This method can effectively reduce the time to create and update the map, but it also reduces the positioning accuracy. According to the propagation characteristics of wireless signals, the signal of the insertion points are formed by the superposition of multiple directional signals. Therefore, the correlation of neighboring points should not be considered only, but should be expanded in different directions. According to the propagation characteristics of actual signals, this paper designs a method based on multi-chain interpolation, which combines the influence of different propagation links on the insertion point to evaluate the signal strength. The basic idea of this method is to perform interpolation calculation in different directions under the given sampling rules. Then the predicted values of insertion points are obtained by using inverse distance weighting. Next, the corresponding signal attenuation Models are obtained by fitting in each direction and the errors are calculated as the direction weights. Finally, the estimation values of the insertion points are obtained. Through repeated iteration, the fingerprint database composed of real points and virtual points is finally formed. Two sampling models are used in this paper. And the sampling rates are 25% and 50% of that of full sampling, which means that the workload of map construction is reduced by 75% and 50% respectively. According to large-scale experiments, the positioning accuracy of the two MCI sampling methods is 13.58% and 4.74% higher than that of the full sampling method respectively. Compared with the classical interpolation method, the MCI method has better stability. Especially when the sampling amount is small, the advantage is more obvious. When the sampling amount is only 25%, the average accuracy is 18.50% higher than that of the full sampling method.

Multispectral cameras of filter array are susceptible to interference streaks at the edges of each band due to the imaging mechanism and process characteristics. Aiming at the problem of interference streaks of multispectral image strips, a new method of grey correction algorithm for stripe edge is proposed. Firstly, determine the coordinates of the effective area of each strip image for strip image cropping; Secondly, a grey scale change function is fitted using the grey scale of pixels in the undisturbed area at the edges of each strip, and the grey scale of pixels in interference area at the edges of the strip is predicted to obtain the fitted pixel grey scale values; Then, the ratio of the grey value of each fitted pixel to the mean grey value of the true pixel is used as the grey correction factor for the corresponding row to eliminate edge interference stripes. Finally, the grey-scale processed images of each strip are stitched together to obtain a consistent grey-scale image of each single band. The theoretical analysis and experimental results show that this method can not only effectively solve the problem of grey-scale interference at the edges of the multispectral images of filter array, and can maintain the spectral information characteristics of the features to the maximum extent.

Sample quality is the key to automated cloud detection from regional remote sensing images, and scale is one of the major impediments to sample quality control. In this paper, we select the southwest mountainous area in China, which is fragmented, cloudy, and rainy, as the study area. We proposed a method for constructing a cloud detection dataset based on the idea of downscaling and the spectral characteristics of vegetation. Finally, we validated the dataset by the U-Net+ deep learning model. The experimental results show that the cloud detection accuracy reaches 95.11% when using the dataset constructed in this paper, which is approximately 40% higher than the cloud detection accuracy with large-scale samples. Additionally, it reduced the workload of masking a large number of samples for a specific region and realizing the possibility of efficient cloud detection in the region.

Webshell exists as a command execution environment in the form of a web page file, which is often referred to as a backdoor. After hacking a website, hackers usually upload it to the web directory of the web server and mix it with the normal web files, and then access the backdoor program through the browser, which can achieve the purpose of controlling the browser. Since there are many kinds of web backdoors in the form of asp, php, jsp or cgi files, here we choose the more popular php file as the research object. In this paper, the Webshell dataset comes from common Webshell samples on the Internet, and the white samples mainly use common open source software developed based on PHP. We use bag-of-words and TF-IDF models for feature extraction, and construct Webshell detection models based on the LightGBM algorithm. The results show that our model is more than 98% accurate and has higher performance in space and time compared to the current popular classification models.

The system design architecture of wireless sensor terminal is introduced in detail. The wireless sensor terminal is composed of M263 Series NuMicro MCU, bme680 sensor of Bosch and Lora wireless communication module. This paper comprehensively considers the functions of each module, communication reliability, power consumption and power saving, and expounds the design points and principles in detail. As the main control software, MCU is responsible for realizing the functions of driving and business processing, As an environmental sensor, bme680 can collect temperature, humidity, air pressure and other information. The communication protocol of Lora wireless communication module adopts the micro power wireless network communication protocol of the Internet of things for power transmission and transformation equipment of the State Grid. Bme680 is responsible for collecting environment information to MCU. MCU and wireless communication module interact with each other through serial port. The wireless communication module transmits data to and receives information from the sink node through wireless mode, so as to realize intelligent management of power equipment operation. Based on the realization of service functions, this paper focuses on the design of MCU software and wireless communication module, as well as the power consumption and power saving of sensor terminals.

Phase unwrapping algorithm is difficult to apply in INSAR image for the local high-density noise phase attributed to significant blocky noise. To achieve its application in such case, the pixels of noise phase are first detected, and are set to 0 with the automatic mask technique. For the phase that has a blocky noise region, the iteration algorithm of phase filling based on Least-Squares is developed in this study by calculating the unwrapping coefficient k to rebuild the true phase. The algorithm is promoted by MPIPU's ability to fill in the missing phase; it can also significantly suppress the error transfer attributed to iteration filling in the non-mask phase. Some experiments are performed on simulated data. As revealed from the results, the proposed method exhibits robust performance of phase unwrapping on local noise phase.

Speaker recognition is a technology that verifies the identity of a person by his or her voice. Different feature parameters have different potential information in speaker recognition. In order to solve the problem that a single feature parameter cannot fully represent a speaker's identity, this paper proposes a feature fusion approach based on embedding mechanism. The fusion features adopted in our approach are filter bank coefficients (Fbank) and mel frequency cepstrum coefficients (MFCC). Potential and complementary information in two features can be obtained by a neural network model, which takes our embedded features as inputs. The d-vector output of the neural network model is classified using the Softmax loss function and optimized using the generalized end-to-end loss function. Both of the most common models, long short term memory network (LSTM) and bi-directional long short term memory network (BiLSTM), are used as our testbed. Results show that, by using our proposed feature fusion approach, the performance of both models are improved. In particular, the minimum equal error rate is 4.17% under the BiLSTM model, compared with the single MFCC or Fbank feature, which are reduced by 72.2% and 28.4% respectively.

Space-time adaptive processing (STAP) can effectively suppress the clutter, which plays an important role in ground moving target indication (GMTI). However, it is difficult to obtain sufficient training samples with an increase in the number of spatial channels and adaptive processor dimensions in large arrays, especially in a complex geomagnetic detection environment. Traditional reduced-dimension STAP methods cannot offer significant benefits in real data processing in this issue. Thus, in this paper, a reduced-dimension post-Doppler STAP method based on tensor Tucker decomposition is proposed. Firstly, the distribution characteristics of the clutter spectrum in the post-Doppler domain are analyzed. Then, the feature spaces of beam and Doppler are extracted by tensor Tucker decomposition. Finally, the data dimension is reduced by the feature spaces, and clutter suppression is carried out. The results of the experiments based on real measured data demonstrate that the proposed method can achieve good performance with fewer samples than traditional methods.

Linear Frequency Modulation Continuous Wave (LFMCW) radar is widely used for automotive applications as its high range resolution and frequency resolution. In this article, a multiple target detection radar is presented to monitor aircraft docking. The proposed radar adopts FMCW+CW mode and two receiving antennas to calculate the range, velocity and angle of the multiple targets. This method was verified by simulation and measurements.

A time delay prediction method of train network based on wireless transmission is proposed. EMD is used to decompose the time delay series. The decomposed components with large sample entropy are DWT to form new components, in order to reduce the complexity of prediction. The components with similar sample entropy are combined into new components to reduce the amount of model calculation. Finally, each data component is predicted by particle swarm optimization LSSVM model. The simulation results show that the proposed method has high prediction accuracy.

Based on the characteristic in time, frequency and spatial domain, radio physical layer can provide powerful basic support for constructing new radio communication security mechanism. How to integrate the radio layer security concept with the traditional radio communication security mechanism is one of the most important studies during 6G technology research. This paper focuses on the proximity based services (ProSe) that show the characteristics of radio communication in 6G, analyzes the potential security enhancement space of the current 3GPP 5G ProSe standardization scheme, and proposes a proximity radio communication security enhancement mechanism based on radio physical layer key generation technology. The mechanism uses the uniqueness of the radio channel characteristics of both sides of the communication as a shared physical layer private information source, constructs a PC5 interface security model across the physical layer and the ProSe protocol layer, and enhances the security protection of discovery messages and signaling messages on PC5 interface in proximity communication. The mechanism can effectively enhance the radio communication security when the traditional security mechanism is not fully activated. Finally, potential application scenarios for radio physical layer key generation techniques in 6G proximity communication services are discussed.

In order to effectively detect network attacks, machine learning is widely used to classify different types of intrusion detection. Traditional detection usually used a single model to train data, which was prone to the problems of large generalization error and over fitting. In order to solve this problem, the idea of ensemble learning is introduced, and an intrusion detection model based on CNN and ensemble learning is proposed. Firstly, CNN is used to mine the deep information in the original data, and then the mined information is taken as the input and detected by using the ensemble learning model. According to the stacking strategy, a variety of heterogeneous models are used as the base-learner, and support vector machine is selected as the meta-learner. The NSL-KDD dataset is used to train and test the intrusion detection model. The experimental results show that the model can obtain higher accuracy and has very good intrusion detection classification effect.

Generative Adversarial Network used in the field of Network Intrusion Detection has become very common, but mode collapse of Generative Adversarial Network and unbalanced distribution of training dataset in Network Intrusion Detection are problems worth solving. Generator and discriminator of Generative Adversarial Network can not fully learn feature information. In this paper, the Twin Auxiliary Classifier GAN is combined with the idea of mutual information modeling. The training is carried out on the Network Intrusion Detection dataset UNSW-NB15. After comparing the original dataset trained by Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Multi-layer Perceptron Machine with the expanded dataset generated by Twin Auxiliary Classifier GAN training, the results show that the methods proposed in this paper can improve the performance of each classifier on the test set.

The currently used URL identification methods require a large number of tags. The malicious URLs update quickly, and it is difficult to collect enough comprehensive URL tags, resulting in unstable identification accuracy. After calculating the boundary similarity of the URL string, the Skip-Gram model is used to embed the URL. The processed word vector is used as the generator input of the semi-supervised learning GAN to obtain the malicious URL identification result. The experimental results show that the accuracy of the URL recognition using GAN is higher than 96%, the fluctuation of the F1 value is small, and the recognition results are more reliable.

DNS (Domain Name System /DNS) is one of the most important infrastructures of Internet. People can easily access the rich network resources worldwide using the DNS technology. However, the Domain Generation Algorithm (DGA) is also accompanied by the DNS technology, which is used to generate malicious domain names. To detect DGA malicious domains, the previous studies often used unreal small DNS domain name datasets to train the detection models that always overlooked real user data traffic. These models generally did not have good generalization performance. In this paper, we propose a new DGA malicious domain name detection model based on Bi-directional LSTM network. We also propose a new evaluation metric to evaluate the real unlabeled DNS traffic data. Compared with LSTM model, the detection effect of our proposed model is improved effectively. The experimental results show that the precision of the model and the value of AUC reach 98.4% and 0.9079, respectively.

In order to improve the accuracy of Internet of Things access control and the clustering of data transmission, a big data peak clustering method based on reinforcement learning is proposed. A big database management model of Internet of Things access control architecture is established by adopting global data pattern. Based on heterogeneous parameters among big data sources of Internet of Things access control architecture, combined with structural feature analysis of data sources, a big data interference filtering model of Internet of Things access control architecture is established by adopting the feature analysis method of blockchain fusion control and association rule mining, and feature extraction of big data peaks of Internet of Things access control architecture is carried out through reinforcement learning algorithm. According to the change of Internet of Things access mode, cluster analysis and pattern recognition of Internet of Things access control architecture big data peak are realized. By constructing the spatial-temporal distribution model of Internet of Things access control architecture big data and Internet of Things transmission channel, the spectral density cluster analysis method is adopted, according to the quantitative parameter analysis of real-time Internet of Things access control architecture data stream, the quantitative recursive analysis method is adopted, and the online Internet of Things access control architecture big data cleaning is used to realize the identification and cluster analysis of Internet of Things access control architecture big data peak features, so as to improve the Internet of Things access control ability. The simulation results show that this method is highly reliable for peak clustering of big data in the access control architecture of the Internet of Things, and has strong dynamic analysis and recognition ability for Internet of Things access and data scheduling, good convergence of data clustering, and low error rate.

In the Internet of Things (IoT) environment, embedded IoT devices have limited computing and storage resources. Their data lacks encryption protection. To solve this problem, this paper proposes a lightweight encryption algorithm based on SM4. First, this paper analyzed the encryption and decryption principle of SM4 algorithm. Then the S-box used in the encryption and decryption process was extended from high-order domain to low-order domain. The S-box component is constructed by a few logic gates to realize the lightness of the algorithm. Finally, the optimized SM4 module was embedded in the 5G communication terminal for testing. The results show that the lightweight encryption algorithm for IoT device can adapt to the data encryption of resource-constrained devices well. It is helpful to avoid the data leakage.

Cloud computing network and next-generation network is now widely used, which brings more security threats. Based on the process and the network tuples communication to construct defense system, the key technologies of defense under IPv4/IPv6 dual stack network are researched. It presents the NRLS Sunday based on improvement of the longest unique substring and Sunday algorithm, to avoid excessive repetitive character comparison and improve the matching efficiency of a single pattern string. Compared with BM, Sunday algorithm, the experiment shows the improved algorithm optimizes its time complexity. Under high-speed network, packet data contents is carried on rapid detection and analysis. Based on intelligent traffic restrictions for process and network, the intrusion detection and defense is conducted for network. In this paper, parallel packet detection method is studied under the high speed. Application web collaboration, and the unified arrangements and issued policies is applied in a complex network environment, proposing and realizing the effective method of defense in the complex network environment.

With the wide application of 5G technology, more and more computing-intensive tasks and delay-sensitive tasks need to be calculated and processed on user equipment, but limited by the computing power and storage capacity of user equipment, these tasks cannot be efficient processing. The emergence of mobile edge computing (MEC) makes it possible. In this paper, we consider task offloading on Small Cell Network (SCN) structures unique to 5G. Under this network structure, a computational offloading strategy for joint optimization of forward and backward links is designed and implemented. Considering the front-end link and the backward link comprehensively, a computational offloading strategy model aiming at minimizing the total energy cost is established under the premise of delay limitation. Then, the objective function that needs to be optimized for energy is established according to the model, and the objective function is optimized by the improved artificial fish swarm algorithm. Finally, through the simulation of the algorithm, the performance of the improved algorithm is proved.

In the face of intensive data access requests, the current big data storage methods have the problem of poor concurrent read-write performance. Based on Ceph architecture, a distributed big data hierarchical storage method is designed to realize the robust storage service of data files. According to the frequency and popularity of the access object, the cold data with low frequency is eliminated, and the access cache pool or back-end storage pool is selected. Standardize the I/O performance, load and capacity values in the OSD set, calculate the comprehensive weight, and determine the priority of node access. The entire Ceph architecture balances the data distribution. When highly concurrent data access operations occur, a random number is selected to execute the read service of the storage node to ensure the interconnection communication in different clusters. The test results show that the write and read time of the hierarchical storage method based on Ceph architecture is greatly reduced, and good results are achieved in terms of cache hit rate, which can effectively manage and store competitive big data.

Aiming at the problems existing in the current tourism route planning, this paper constructs a city administrative region tourism route recommendation algorithm based on Naive Bayes data mining. The Naive Bayes classifier model is constructed through the attribute tag data of tourists’ once-visited scenic spots, and then the scenic spots in the target city are classified. The scenic spots are sorted according to the weighted Bayesian probability value of each classification, so as to recommend the scenic spots with the highest probability value for tourists. Based on the scenic spots with the optimal probability values, this paper constructs a tourism route algorithm with the lowest cost. Combined with the different travel modes of tourists, it searches the city tourism routes with the lowest cost on traveling distance, time and fee. At the same time, it provides two tourism decision-making plans according to the actual needs of tourists. Experiment shows that the proposed algorithm can recommend the scenic spots with the highest weighted probability value and satisfy the needs of tourists, and the traveling cost on the searched tourism route is the lowest.

The urban public transport system comprises various networks representing different transportation modes with complex internal connections. The common single-layer complex network cannot fully describe the interior characteristics of the system. Therefore, this paper proposes a multi-layer network modeling method for the urban public transport system, which couples different traffic networks into multi-layer networks. We also proposed a centrality measurement of node transfer and designed an attack strategy called the multi-metric joint. The simulation experiment using the geographic information data of Chengdu urban public transport shows that the modeling method can completely and accurately describe the natural urban public transport system. And the multi-metric joint attack strategy is more effective than the traditional attack strategy, which will cause more severe damage to the network. This paper provides a new perspective and method for multilayer network modeling and vulnerability analysis of urban public transport systems.

The digital car key is available on smart devices now. People no longer have to bring the key fob to unlock and start the engine. The existing schemes have some problems, such as poor device compatibility and poor user experience. In this paper, we proposed an unperceived digital car key scheme based on Bluetooth Low Energy (BLE) SIM card. After owner paring, the vehicle automatically locks/unlocks when the terminal device with the BLE SIM card approaches/leaves it. The data of the digital car key is stored in the secure element of the Bluetooth SIM card. The digital credentials are exchanged and mutually authenticated through a Bluetooth connection between the vehicle and the BLE SIM card. Compared to the existing schemes, our scheme has better device compatibility and a better user experience.

In view of the problems of low data processing efficiency, security risks and low utilization rate in the construction of smart city, this paper uses the integration technology of Cloud-Edge-Terminal and blockchain to build the smart city architecture system, and proposes solutions for data storage and sharing in the architecture system, which not only realizes the real-time, efficient and rapid response of the smart city platform system, but further improves the security of data storage and sharing in the construction of smart city.