As a paradigm for encouraging users to contribute data spontaneously, mobile crowdsensing (MCS) has received considerable attention recently. It is crucial to evaluate the truthfulness of MCS data by proper truth discovery mechanisms. Although recent truth discovery schemes can determine truthful information, they either provide limited privacy preservation or have heavy computation and communication overheads. Moreover, most of them are not resilient to malicious faults and active attacks. To tackle the above problems, we propose two fault-tolerant and privacy-preserving truth discovery solutions. Our first scheme is mainly used for scenarios with a relatively stable number of users, where participants do not frequently join or leaves. Integrating ring signature with the perturbation technique, we design an anonymous and privacy-preserving truth discovery scheme, namely RsAnonTD, which can achieve privacy preservation and resist active attacks. To address the challenge with dynamically changed workers, we devise a multi-client inner product functional encryption scheme with a lightweight zero-knowledge proof protocol (namely McFeKDeTD) for defending against active attacks. The security analysis shows that both schemes can preserve the privacy of sensory data, weights, and estimated truths while resisting active attacks, thereby guaranteeing fault tolerance. Extensive experiments demonstrate that our designs achieve superior performance than other schemes in terms of accuracy, convergence speed, and system overheads. For example, compared with the state-of-the-art approach RPTD-II, which has a security level comparable to ours, our proposed schemes, RsAnonTD and McFeKDeTD, reduce the computational overheads approximately by 98% and 69%, respectively.

With highly mobile and flexible-configurable, autonomous aerial vehicles (AAVs) are becoming crucial wireless communication infrastructures. To improve the reliability and throughput of data delivery for wireless networks, network coding, as a progressive technology, can be applied in AAV networks. However, network coding incurs a security problem called pollution attacks for AAV networks. Although homomorphic network coding signature can prevent pollution attacks, existing schemes are not suitable for AAV networks due to cumbersome certificate management, the key-escrow issue, or insecurity. In this article, we propose an efficient certificateless homomorphic network coding signature scheme for secure transmission of AAV networks, which can avoid certificate management and the key-escrow issue. Then our scheme is proven to be secure against adaptive chosen identity-and-subspace attacks in the random oracle model, thus our scheme can guarantee data integrity and authenticity to resist pollution attacks. We provide a performance evaluation for the proposed scheme and prior research, and experimental results illustrate the efficiency and feasibility of our scheme for practical application, reducing the verification overhead by 42.918% for a 72-dimensional data vector.

A critical challenge for predicting urban traffic flows is to simultaneously process time series and spatial features from heterogeneous traffic data collected by diverse Internet of Things (IoT) devices. Despite the advent of Transformer-based models with an advanced network structure and excellent prediction performance, standard Transformer models are still struggling to combine both spatial information and temporal relations of traffic flows. To address these challenges, we design a novel Transformer network, namely temporal-spatial traffic-flow Transformer (TST-Trans), for traffic flow prediction with high accuracy. In particular, we use learnable position encoders to replace traditional fixed position encoders. Meanwhile, we introduce a spatiotemporal embedding method that integrates temporal relationships and spatial information with external inputs, thereby capturing the spatiotemporal dependencies of traffic flows. Experiments with the real-world datasets demonstrate that our proposed TST-Trans achieves better prediction accuracy than state-of-the-art methods while requiring fewer parameters. The research results increased by more than 10% compared with Transformer. Compared to spatiotemporal deep hybrid neural network, there is a 2% to 10% improvement in performance on different datasets.

Graph Neural Networks (GNNs) show great power in Knowledge Graph Completion (KGC) as they can handle non-Euclidean graph structures and do not depend on the specific shape or topology of the graph. However, many current GNN-based KGC models have difficulty in effectively capturing and utilizing the substantial structure and global semantic information in Knowledge Graphs (KGs). For more effective use of GNN for KGC, we innovatively propose the Semantic Similarity-based Interaction Graph Attention Network (SemSI-GAT) for the KGC task. In SemSI-GAT, we utilize BERT, a pre-trained language model, to learn the global semantic information and obtain semantic similarity between entities and their neighbors. Furthermore, we creatively design a novel encoder network called the interaction graph attention network and introduce a semantic similarity sampling mechanism to optimize the aggregation of interaction information between neighbors. By aggregating local features with interaction features, this network can generate more expressive structural embeddings. This network generates more expressive embeddings by fusing global semantic information, local structure features, and interaction features. The experimental evaluations demonstrate that the proposed SemSI-GAT outperforms existing state-of-the-art KGC methods on four benchmark datasets.

Secret key generation exploiting inherent channel randomness stands as an important paradigm for physical-layer security in wireless networks. However, existing work relying on quantization has some difficulties in eliminating inconsistent key bits due to the impact of ambient noise. Recent studies propose to match the segmented channel samples (i.e., channel episodes) of similar variation patterns between legitimate peers to achieve error-free key generation, but they also suffer from high computational overhead and reduced accuracy for large key lengths. This work proposes a secret key generation method based on sorted indices matching (SIM-SKG), aiming at efficient and error-free key generation. Specifically, we sort the channel samples to ensure each channel episode with a unique variation pattern for accurate matching. To avoid the impact of half-duplex communication mode and ambient noise, we propose to match the indices instead of the channel samples as in existing studies. We also develop a noise perturbation scheme that further mitigates the ambiguity during indices matching. Extensive experimental studies demonstrate the high efficiency and accuracy of SIM-SKG under various scenarios for both RSS and CSI channel measurements. Specifically, SIM-SKG achieves error-free key generation with a length of 2048 bits within as little as 1.7$msec$. Moreover, theoretical analyses and experiments also confirm the security of the SIM-SKG method against various attacks.

Abstract Clustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets.

The physical layer secret key generation exploiting wireless channel reciprocity has demonstrated its viability and effectiveness in various wireless scenarios, such as the Internet of Things (IoT) network, mobile communication network, and industrial control system. Most of the existing studies rely on the quantization technique to convert channel measurements into secret bits for confidential communications. However, non-simultaneous packet exchanges in time-division duplex systems and noise effects usually induce inconsistent quantization results and mismatched secret bits. Although recent research has spent significant effort mitigating such non-reciprocity, it is still far from practical error-free key generation. Unlike previous quantization-based approaches, we take a different viewpoint to match the randomly manipulated (i.e., permuted or edited) channel measurements between a pair of users by minimizing their discrepancy holistically. Specifically, two novel secret key generation algorithms based on bipartite graph matching (BMSKG) and edited sequence alignment (SA-SKG) are developed. BM-SKG allows two users to generate the same secret key based on the permutation order of channel measurements, while SASKG aims to align the edited channel measurements between a pair of users for secret key agreement. In both algorithms, one user can preset the secret key and embed encrypted messages in the exchanged data packets, which reduces communication overheads in key generation. Extensive experimental results show that both BM-SKG and SA-SKG algorithms achieve error-free key agreement on channel measurements at a low cost under various scenarios.

The recent development of deep learning has brought breakthroughs in image denoising. However, the recovery of image detail, especially high-frequency weak information, still needs to be improved. Firstly, the noise mainly concentrates on the high-frequency signal, and the high-frequency signal is easy to be disturbed, which makes it difficult to recover; Secondly, in the process of image denoising with deep learning, feature extraction of model is used to smooth the noise for image restoration, resulting in a poor recovery effect of high-frequency signal. To solve the above problems and improve the overall image denoising performance, we propose a denoising network for complex frequency band signal processing (CFPNet), which contains three insights: 1) the image input node uses a cosine transform to segment the image noise frequency and divides different image features into signals in different frequency bands for targeted noise reduction; 2) targeted noise reduction is carried out for different frequency band signals via a fine-grained scheme; 3) different frequency band signals are fused and high-frequency signals are enhanced to improve the recovery of detailed signals. The experimental results show that the proposed CFPNet can achieve state-of-the-art performance on both real-world datasets and Gaussian noise fitting datasets.

The site selection of sports facilities is a pivotal link in the construction of city livable environment and the development of sports business in digital-twinning cities. Recent years have witnessed data mining and visualization technologies bringing the convenience as well as opportunities for intelligent site selection. However, the lack of effective and reliable systematic analysis leads to difficulties in developing sports facilities planning schemes and constructing the site-selection system. In this article, we design Sport facility Visual analysis system (SpoVis), an interactive visual analysis system for planning sports facilities as well as site selection. SpoVis provides users with the distribution status and statistical analysis of various sports facilities. Based on a comprehensive consideration of city population distribution, construction cost, existing sports facilities, traffic situation, and development potential, SpoVis provides users with a reasonable site-selection scheme of sports facilities from both macro and microperspectives and recommends results through topology and map. Meanwhile, based on the distribution of existing sports facilities and city influencing factors, a set of visual analysis components are designed to facilitate users to evaluate the status and information of existing sports facilities. We have carried out extensive experiments on a real platform with real-world data. The experimental results show that the proposed site-selection models and algorithms have excellent accuracy and operation efficiency.

Deep neural networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this article presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression; 2) knowledge distillation (KD); and 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future directions.

In the past decades, due to the low design cost and easy maintenance, text-based CAPTCHAs have been extensively used in constructing security mechanisms for user authentications. With the recent advances in machine/deep learning in recognizing CAPTCHA images, growing attack methods are presented to break text-based CAPTCHAs. These machine learning/deep learning-based attacks often rely on training models on massive volumes of training data. The poorly constructed CAPTCHA data also leads to low accuracy of attacks. To investigate this issue, we propose a simple, generic, and effective preprocessing approach to filter and enhance the original CAPTCHA data set so as to improve the accuracy of the previous attack methods. In particular, the proposed preprocessing approach consists of a data selector and a data augmentor. The data selector can automatically filter out a training data set with training significance. Meanwhile, the data augmentor uses four different image noises to generate different CAPTCHA images. The well-constructed CAPTCHA data set can better train deep learning models to further improve the accuracy rate. Extensive experiments demonstrate that the accuracy rates of five commonly used attack methods after combining our preprocessing approach are 2.62% to 8.31% higher than those without preprocessing approach. Moreover, we also discuss potential research directions for future work.

Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes.