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For the trajectory tracking problem of nth-order uncertain nonlinear systems with strict feedback, a fuzzy controller based on command filtered and event-triggered technology is designed to improve the tracking error of the system. Concurrently, a fault-tolerant control scheme is introduced to effectively solve the problem of sudden output sensor failure. Additionally, the proposed controller can also greatly avoid the "differential explosion" problem of derivations of virtual control laws, which makes the design of the controller simpler. Furthermore, an effective observer is designed to solve the problem of system state immeasurability. The control law designed by this method does not need to include the known function of the system state equation. Therefore, the proposed control scheme makes the design of the controller more convenient and flexible. According to Lyapunov stability theory, it is proved that all the signals in the closed-loop system are uniformly and ultimately bounded. Finally, two simulation examples of second-order nonlinear system and single link robot show the effectiveness of the proposed scheme.

This paper is a comparative study on the performance of the fog-enabled sensor cloud (FSC) and traditional cloud computing and fog computing modes in a smart logistics park. Based on our previous work, we describe the physical sensor virtualization scheme and framework of the proposed FSC, construct the network model, and mathematically describe the parameters of the FSC. To assess the performance of the proposed platform, we take a large logistics enterprise in China as an example and illustrate the network setup of the proposed platform in a real logistics scenario. The experiment proves that the FSC for smart logistics parks has a practical advantage over the traditional cloud computing and fog computing modes in terms of bandwidth consumption and service latency.

Media reports and the number of rumors in the media have an important impact on the spread of rumors. Currently, studies that consider these two factors comprehensively on rumor propagation are uncommon. It is better to consider both effects comprehensively than to consider one of them alone. In this paper, we established a new propagation model, which regards the number of rumors in the media as a subclass that changes with time, and then comprehensively considered the effect of these two factors on the process of rumor propagation. We proved the existence and stability of equilibrium points of the model. Then, we selected two reasonable control variables: science popularization intensity and punishment intensity. We find that the control effects are optimal when these two control variables take the maximum value. Theoretical analysis and numerical simulation results showed that positive media publicity can reduce the spread of rumors, but cannot stop the spread of rumors. And under the parameters given in this paper, the optimal control parameter values satisfying the constraints can be calculated through quantitative analysis. This method provided a new idea for studying the optimal control of rumor propagation.

With the increase of private cars, traditional traffic signal control methods cannot alleviate the traffic congestion problem. Reinforcement learning (RL) is increasingly used in adaptive traffic light control. As urban traffic becomes more complex, reinforcement learning algorithms solely based on value or policy are not suitable for such scenarios. Moreover, the centralized method does not show a good effect on the multi-intersection, in particular, when the traffic flow is in the high-dimensional continuous state space. In this article, we propose a decentralized framework based on the advantage actor-critic (A2C) algorithm by assigning global control to each local RL agent or intersection. A2C algorithm involved in this article is a product of combining policy function and value function, which has good convergence ability and can be applied to continuous state space. The decentralized methods may put a new challenge: from the perspective of each local agent, the environment becomes partially observable. We overcome this problem by putting forward a region-aware cooperative strategy (RACS) based on graph attention network (GAT), which can incorporate the spatial information of the surrounding agents. We carry out experiments on synthetic traffic grid and real-world traffic network of Monaco city to compare with the existing A2C and Q-learning algorithms. Experimental results confirm that our RACS method has a shorter queue length and less waiting time than the two existing algorithms, and can reduce the total vehicle travel time.

In cloud storage, how to search sensitive data efficiently and securely is a challenging problem. The searchable encryption technique provides a secure storage method without loss of data confidentiality and utilization. As an important branch of searchable encryption, public-key encryption with keyword search (PEKS) is widely studied by scholars. However, most of the traditional PEKS schemes are vulnerable to the inside keyword guessing attack (IKGA) or some other weaknesses. Resisting the inside keyword guessing attack is likely to become a must-have property of all new PEKS schemes. For a long time, mitigating IKGA has been inefficient and difficult, and thus most existing PEKS schemes fail in achieving their security goals. To improve the security and efficiency of PEKS, we define the notion of Dual-server Public-key Authenticated Encryption with Keyword Search (DPAEKS), which protects against IKGA with high computation efficiency, and supports the authentication property. Then, we provide a construction of DPAEKS without bilinear pairings, which is secure against IKGA by leveraging two servers that do not cooperate. Experimental results obtained using a real-world dataset show that our scheme is highly efficient and provides strong security, making it suitable for deployment in practical applications.

Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are always existing new categories and large amounts of unlabeled data in realistic scenarios. Previous work for GZSL usually maps the visual information of the visible classes and the semantic description of the invisible classes into the identical embedding space to bridge the gap between the disjointed visible and invisible classes, while ignoring the intrinsic features of visual images, which are sufficiently discriminative to classify themselves. To better use discriminative information from visual classes for GZSL, we propose the n-CADA-VAE. In our approach, we map the visual feature of seen classes to a high-dimensional distribution while mapping the semantic description of unseen classes to a low-dimensional distribution under the same latent embedding space, thus projecting information of different modalities to corresponding space positions more accurately. We conducted extensive experiments on four benchmark datasets (CUB, SUN, AWA1, and AWA2). The results show our model’s superior performance in generalized zero-shot as well as few-shot learning.

As more data from Industrial Internet of Things (IIoT) devices are been outsourced to the cloud, the need to ensure/achieve data privacy will be increasingly pressing. Searchable public-key encryption is a promising tool to achieve data privacy without sacrificing data usability. However, most existing searchable public-key encryption schemes are either inefficient (e.g., expensive in terms of encryption and searching keywords) or lack the required (security) features such as forward privacy. To address these limitations, we propose a lightweight searchable public-key encryption with forward privacy. Specifically, the scheme allows us to achieve forward privacy in a public-key cryptography (i.e., asymmetric) setting, and has the same search performance as a number of practical searchable symmetric encryption schemes. The formal security analysis shows that the proposed scheme is chosen-keyword attack resilient and achieves forward privacy. Finally, the experimental results on a real-world dataset demonstrate that the proposed scheme is highly efficient and scalable in IIoT applications.

Infectious diseases have a devastating impact on individual health and social development. Different external environmental factors will affect the scale and the speed of disease outbreaks, such as sanitary conditions and policy interventions. In this paper, we attempt to establish a dual-system susceptible-infectious-quarantine-recovered model with different environmental impacts to explore it. For the deterministic model, we calculate the basic reproduction number, simultaneously, and investigate the local asymptotic stability of the disease-free equilibrium and the endemic equilibrium. Numerical simulations and theoretical analyses verify the conclusion of this paper. It is a surprise that there is a great probability of finding a quarantine inflection point, which can effectively control the scale of infection outbreak when two different systems of infection rate and recovery rate are determined.

The reliability and validity of traffic data plays an important role in intelligent transportation systems. Most of the data detection schemes target the network level, and rarely discuss the impact of vehicle data detection at intersections on the intelligent control of traffic lights. Moreover, most data detection is processed in a centralized cloud center, which is not suitable for complex and changeable intersections. In this regard, we propose an edge computing based data detection scheme for traffic light intersections. In our scheme, traffic lights act as edge nodes to detect vehicles data. We first consider a single intersection scenario. We collect vehicle data through V2E communication through base station, and use the quotient filter to detect and verify the validity of the data. We further consider the multiple intersections scenario. We merge the vehicle data of the two adjacent intersections, and then detect the validity of the data by the quotient filter. In addition, the scheme uses mmh3 hash functions in the QF filter to reduce the computing resource occupation of edge nodes and the bit error rate. The experimental results show that the data detection solution is effective in detecting data quickly even there is a large number of vehicles and complex vehicle data. Our proposed scheme can also verify the reliability and effectiveness of vehicles with a smaller delay.

Internet of Things (IoT)-enabled smart grids can achieve more reliable and high-frequency data collection and transmission compared with existing grids. However, this frequent data processing may consume a lot of bandwidth, and even put the user’s privacy at risk. Although many privacy-preserving data aggregation schemes have been proposed to solve the problem, they still suffer from some security weaknesses or performance deficiency, such as lack of satisfactory data confidentiality and resistance to malicious data mining attack. To address these issues, we propose a novel privacy-preserving data aggregation scheme (called PDAM) for IoT-enabled smart grids, which can support efficient data source authentication and integrity checking, secure dynamic user join and exit. Unlike existing schemes, the PDAM is resilient to the malicious data mining attack launched by internal or external attackers and can achieve perfect data confidentiality against not only a malicious aggregator but also a curious control center for an authorized user. The detailed security and performance analysis show that our proposed PDAM can satisfy several well-known security properties and desirable efficiency for a smart grid system. Moreover, the comparative studies and experiments demonstrate that the PDAM is superior to other recently proposed works in terms of both security and performance.

Vehicular ad hoc networks ( VANETs ) and the services they support are an essential part of intelligent transportation. Through physical technologies, applications, protocols, and standards, they help to ensure traffic moves efficiently and vehicles operate safely. This article surveys the current state of play in VANETs development. The summarized and classified include the key technologies critical to the field, the resource-management and safety applications needed for smooth operations, the communications and data transmission protocols that support networking, and the theoretical and environmental constructs underpinning research and development, such as graph neural networks and the Internet of Things. Additionally, we identify and discuss several challenges facing VANETs, including poor safety, poor reliability, non-uniform standards, and low intelligence levels. Finally, we touch on hot technologies and techniques, such as reinforcement learning and 5G communications, to provide an outlook for the future of intelligent transportation systems.

While deep face recognition (FR) systems have shown amazing performance in identification and verification, they also arouse privacy concerns for their excessive surveillance on users, especially for public face images widely spread on social networks. Recently, some studies adopt adversarial examples to protect photos from being identified by unauthorized face recognition systems. However, existing methods of generating adversarial face images suffer from many limitations, such as awkward visual, white-box setting, weak transferability, making them difficult to be applied to protect face privacy in reality. In this paper, we propose adversarial makeup transfer GAN (AMT-GAN), a novel face protection method aiming at constructing adversarial face images that preserve stronger black-box transferability and better visual quality simultaneously. AMT-GAN leverages generative adversarial networks (GAN) to synthesize adversarial face images with makeup transferred from reference images. In particular, we introduce a new regularization module along with a joint training strategy to reconcile the conflicts between the adversarial noises and the cycle consistence loss in makeup transfer, achieving a desirable balance between the attack strength and visual changes. Extensive experiments verify that compared with state of the arts, AMT-GAN can not only preserve a comfortable visual quality, but also achieve a higher attack success rate over commercial FR APIs, including Face++, Aliyun, and Microsoft., Accepted by CVPR2022. Code is available at https://github.com/CGCL-codes/AMT-GAN

Worker recruitment is an important problem in mobile crowdsourcing (MCS), which aims to find sufficient and suitable participants to perform tasks. However, existing worker recruitment approaches mainly focus on how to select the most suitable workers for tasks from a large worker pool, while the recruitment problem under insufficient workers (e.g., a new MCS system) has not been well addressed. In this paper, we focus on the insufficient participation problem of MCS systems with limited number of workers, and propose to leverage social network to recruit workers for task completion as well as expanding the worker pool. To this end, we propose a dynamic incentive mechanism, called SocialRecruiter, to encourage workers on the MCS platform to propagate tasks through social networks, so that inviting friends to join in the MCS platform to further propagate and complete tasks. Motivated by the SIR epidemic model, we propose a novel task-specific epidemic model to characterize the status change of users for task propagation and completion through social networks. In order to encourage task completion and propagation, the propagating reward and completing reward are provided according to workers’ actions. In particular, in order to maximize the task completion within the financial budget, the propagating and completing rewards are dynamically updated at each cycle according to real-time worker recruitment progress. The extensive experimental results on two real-world datasets demonstrate that SocialRecruiter outperforms the state-of-the-art approaches in terms of worker recruitment and task completion.

As the use of machine learning in the Internet-of-Medical Things (IoMT) settings increases, so do the data privacy concerns. Therefore, in this article, we propose an efficient privacy-preserving outsourced support vector machine scheme (EPoSVM), designed for IoMT deployment. To securely train the support vector machine (SVM), we design eight secure computation protocols to allow the cloud server to efficiently execute basic integer and floating-point computations. The proposed scheme protects training data privacy and guarantees the security of the trained SVM model. The security analysis proves that our proposed protocols and EPoSVM satisfy both security and privacy protection requirements. Findings from the performance evaluation using two real-world disease data sets also demonstrate the efficiency and effectiveness of EPoSVM in achieving the same classification accuracy as a general SVM.

It is important to transmit data reliably, and efficiently in vehicular networks. Existing works usually study routing strategies, and cooperative scheduling to improve the efficiency of transmission. However, the data transmission remains inefficient because of the lack of full use of communication resources. The transmission is unreliable because information cannot be completely transmitted to the destination vehicles. Moreover, the increasing number of connected vehicles, and the limitation of available communication resources make task scheduling challenging in vehicular networks. In this work, we propose C luster-enabled C ooperative S cheduling based on R einforcement L earning (CCSRL) to improve the communication efficiency, and reliability of vehicular networks, with the goal of maximizing the information capacity. In particular, we leverage the stability to select a cluster head vehicle to enhance data transmission efficiency, and a reinforcement learning-based auxiliary transmission is further designed to guarantee the reliable communication among vehicles. The experimental results demonstrate that the performance of the proposed scheduling algorithm, especially the performance of the packet delivery ratio, and node packet loss ratio, is better than that of the state-of-the-art algorithm.

Summary This paper studies the problem of observer‐based finite time adaptive fault tolerant control for nonaffine nonlinear systems with actuator faults and disturbances. Based on mean value theor...

As the integration of the Internet of Vehicles and social networks, vehicular social networks (VSN) not only improves the efficiency and reliability of vehicular communication environment, but also provide more comprehensive social services for users. However, with the emergence of advanced communication and computing technologies, more and more data can be fast and conveniently collected from heterogeneous devices, and VSN has to meet new security challenges such as data security and privacy protection. Searchable encryption (SE) as a promising cryptographic primitive is devoted to data confidentiality without sacrificing data searchability. However, most existing schemes are vulnerable to the adaptive leakage-exploiting attacks or can not meet the efficiency requirements of practical applications, especially the searchable public-key encryption schemes (SPE). To achieve secure and efficient keyword search in VSN, we design a new blockchain-based searchable public-key encryption scheme with forward and backward privacy (BSPEFB). BSPEFB is a decentralized searchable public-key encryption scheme since the central search cloud server is replaced by the smart contract. Meanwhile, BSPEFB supports forward and backward privacy to achieve privacy protection. Finally, we implement a prototype of our basic construction and demonstrate the practicability of the proposed scheme in applications.

The rapid development of machine learning has spurred wide applications to various industries, where prediction models are built to forecast sales to help enterprises and governments make better plans. Alibaba Cloud and the Yancheng Municipal Government held a competition in 2018, calling for global efforts to build machine learning models that can accurately forecast vehicle sales based on large-scale datasets. This paper presents the design, implementation and evaluation of ForeXGBoost, and our proposed model that won the first place in the competition. ForeXGBoost takes full advantage of carefully-designed data filling algorithms for missing values to improve data quality. By using the sliding window to extract historical sales and production data features, ForeXGBoost can improve prediction accuracy. An extensive study is conducted to evaluate the influence of different attributes on vehicle sales via information gain and data correlation, based on which we select the most indicative features from the feature set for prediction. Furthermore, we leverage the XGBoost prediction algorithm to achieve a high prediction accuracy with short running time for vehicle sales prediction. Extensive experiments confirm that ForeXGBoost can achieve a high prediction accuracy with a low overhead.

Achieving low latency and providing real-time services are two of several key challenges in conventional cloud-based smart grid systems, and hence, there has been an increasing trend of moving to edge computing. While there have been a number of cryptographic protocols designed to facilitate secure communications in smart grid systems, existing protocols generally do not support conditional anonymity and flexible key management. Thus, in this article, we introduce a blockchain-based mutual authentication and key agreement protocol for edge-computing-based smart grid systems. Specifically, leveraging blockchain, the protocol can support efficient conditional anonymity and key management, without the need for other complex cryptographic primitives. The security analysis shows that the protocol achieves reasonable security assurance, and the comparative summary for security and efficiency also suggests the potential of the proposed protocol in a smart grid deployment.

In this study, an adaptive neural backstepping control scheme is proposed for a class of nonstrict-feedback time-delay systems with input saturation, full-state constraints and unknown disturbances. A structural property of radial basis function neural network is presented to deal with the design from the nonstrict-feedback formation. This method does not require the parameter separation technique and its assumption. With the help of the Lyapunov-Krasovskii functionals and Young’s inequalities, the effects of time delays are compensated, and the unknown disturbances are eliminated in the design process. The barrier Lyapunov function (BLF) is applied to arrest the violation of the full-state constraints. To overcome the problem of input saturation nonlinearity, the smooth nonaffme function of the control input signal is adopted to approach the input saturation function. Moreover, an adaptive backstepping neural control strategy is proposed. The proposed adaptive neural controller ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Furthermore, the tracking error can converge to a small neighborhood of the origin. The simulation result shows the effectiveness of this method.

Data sharing through the cloud is flourishing with the development of cloud computing technology. The new wave of technology will also give rise to new security challenges, particularly the data confidentiality in cloud-based sharing applications. Searchable encryption is considered as one of the most promising solutions for balancing data confidentiality and usability. However, most existing searchable encryption schemes cannot simultaneously satisfy requirements for both high search efficiency and strong security due to lack of some must-have properties, such as parallel search and forward security. To address this problem, we propose a variant searchable encryption with parallelism and forward privacy, namely the parallel and forward private searchable public-key encryption (PFP-SPE). PFP-SPE scheme achieves both the parallelism and forward privacy at the expense of slightly higher storage costs. PFP-SPE has similar search efficiency with that of some searchable symmetric encryption schemes but no key distribution problem. The security analysis and the performance evaluation on a real-world dataset demonstrate that the proposed scheme is suitable for practical application.

Voice authentication is becoming increasingly popular, which offers potential benefits over knowledge and possession based authentication methods. Meanwhile, the unique features of lip movements during speaking have been proved to be useful for authentication. However, the unimodal biometric authentication systems based on either voice or lip movements have certain limitations. Voice authentication systems are prone to spoofing attacks and suffer from serious performance degradation in noisy environments. Lip movements authentication systems are unstable and are sensitive to the user’s physical and psychological conditions. In this paper, we propose and implement LVID, a multimodal biometrics authentication system on smartphones, which resolves the defects of the original systems by combining the advantages of lip movements and voice. LVID simultaneously captures these two biometrics with the built-in audio devices on smartphones and fuses them at the data level. The reliable and effective features are then extracted from the fused data for authentication. LVID is practical as it requires neither cumbersome operations nor additional hardwares but only a speaker and a microphone that are commonly available on smartphones. Our experimental results with 104 participants show that LVID can achieve 95% accuracy for user authentication, and 93.47% of the attacks can be detected. It is also verified that LVID works well with different smartphones and is robust to different smartphone positions.