Wideband DOA estimation has been applied in various signal source location scenarios, e.g., in wireless communication systems to improve the capacity of communication. Existing wideband DOA methods often require prior knowledge such as the number of sources as well as pre-estimations. Moreover, they may suffer from model-mismatch problem. In this paper, we employ the manifold separation technique and Jacobi-Anger expansion to allow multi-frequency joint processing of wideband DOA, which alleviates the challenge of model-mismatch and leads to a much higher DOA resolution. The proposed method is further formulated to be a multi-convex rank-minimization problem to facilitate the analysis of the problem and to improve the convergence performance. The superior performance of the proposed multi-frequency joint processing method has been demonstrated by several numerical studies.

Internet of Things (IoT) systems have become the critical platform to enable a wide variety of smart applications. During IoT data gathering over wireless network, data may be missing due to the constraints of sensors as well as the reliability of communications. From a graph signal processing perspective, recovery of missing data may be strongly affected by the IoT system topology, which can be characterized by a directed adjacency matrix. To guarantee a robust data gathering, we propose a novel method in this paper to design the optimal topology for IoT networks via Bayesian networks, where the designed directed adjacency matrix is with orthogonal graph frequency components. Moreover, the gathering of IoT data becomes sparser in the graph frequency domain using the designed adjacency matrix and may hence improve the recovery performance of missing data. Experimental results show that our proposed methods outperform several existing algorithms.

Network topology inference based on collected observations plays a fundamental role in many smart applications such as internet of vehicles. However, identifying the variation of network topology may be challenged since the network may be directed, and the topology inference problem is usually modelled as a non-convex problem. Moreover, large amount of data may be required to correctly identify the change of network topology. In this paper, the network topology is characterized by directed graphs, and the estimation of topology variation is modelled as a convex problem based on graph filtering. Since signals filtering over graphs can be considered to be a model-based problem, much less observations are required, and the topology estimation can then be performed with online behaviours. The proposed algorithms are validated on synthetic and real datasets, and both of them demonstrate good estimation performances.

Underwater optical wireless communication (UOWC) networks are promising for many civilian and industrial applications due to underwater high-speed data transmission demand. This paper focuses on the distributed time division multiple access (TDMA) protocol design for UOWC networks. Unlike existing research, we focus on a more general underwater communication scenario where nodes are scattered, and node mobility is considered. We propose a distributed TDMA-based medium access control(MAC) protocol, called cluster-based cross-layer multi-slot MAC(CCM-MAC), which can assign multiple slots to each node according to the slot-occupying information. When using CCM-MAC, the network is divided into clusters. Each node maintains knowledge of the cluster topology and slot-occupying information of surrounding nodes through routing techniques; by updating this knowledge, collisions can also be detected and eliminated dynamically. Analysis results are presented to evaluate the performance of CCM-MAC, and simulations show that high network throughput and low collision rate can be realized in comparison with existing contention-based MAC protocols.

Imbalanced class distributions widely exist in real-world aerial images, which brings a significant challenge to aerial scene classification due to the undesirable bias toward the majority classes as well as overfitting for the minority classes. Although the similarity between different scene classes may be inconsistent, they can be measured by the mean of feature statistics. This motivates us to transfer the statistics of the majority class to the minority class having similar feature statistics. Specifically, based on the observation that the feature statistics of each class may follow the Gaussian distribution, the similarity across different classes would thus be described by the mean of feature statistics. The distributions of minority classes would afterward be calibrated by statistical transfer via interclass similarity (STAIRS), and a sufficient number of features could hence be generated for the minority class to improve its performance in classifier learning. We demonstrate the effectiveness of the proposed method for imbalanced aerial scene classification on the imbalanced aerial image dataset (AID) and NWPU-RESISC45 datasets. The proposed method outperforms alternatives by a large margin in both overall performance and minority classification performance of imbalanced aerial scenes.

Existing device authentication techniques may suffer from heavy communication, computation, and storage overhead for identifying a growing number of devices in collaborations. This paper proposes a novel group authentication (GA) method for decentralized edge collaboration by exploiting the historical collaboration process information, i.e., the distributed learning parameters and results from the previous round of collaboration. Two strategies are developed to generate tokens locally at the edge devices’ side for mutual authentication, named random token generation (R-TG) and privacy-preserving token generation (PP-TG). Specifically, the R-TG strategy randomly selects several historical learning parameters as tokens, while the PP-TG strategy designs a one-way function to defend against privacy leakage by concealing the historical information. A GA protocol is proposed, where each device simultaneously authenticates the others in the same group by repeating the learning process using their tokens. If the process converges to an expected result, all the devices are authenticated as legitimate group members at once. The proposed scheme provides a lightweight flexible solution without pre-generating and distributing any keys/secrets operating on top of a standardized security protocol, and protects the collaboration continuously. The simulation results demonstrate the viability of our scheme and its superior performance compared to several benchmark schemes.

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes.

Extracting the power lines and pylons automatically and accurately from airborne LiDAR data is a critical step in inspecting the routine power line, especially in the remote mountainous areas. However, challenges arise in using existing methods to extract the targets from large scenarios of remote mountainous areas since the terrain is undulating, and the features are difficult to distinguish. In this article, to overcome these challenges, we propose a graph convolutional network (GCN)-based method to extract power lines and pylons from Airborne LiDAR point clouds. First, data augmentation and near-ground filtering methods are developed to overcome the problems of insufficient and imbalanced samples in the LiDAR data. Then, a GCN-based framework is proposed to extract the power lines and pylons, which consist of two main modules, i.e., the neighborhood dimension information (NDI) module and the neighborhood geometry information aggregation (NGIA) module. These two modules are designed to strengthen the model’s ability to portray local geometric details. Besides, an attention fusion module is investigated to further improve the NDI and NGIA features. Finally, a line structure constraint algorithm is proposed to identify individual power lines, where the power corridor is reconstructed using a polynomial-based algorithm. Numerical experiments are conducted based on two different power line scenarios acquired in mountainous areas. The results demonstrate the superior performances of the proposed method over several existing algorithms, where the$F_{1}$score and quality of the power line are 99.3% and 98.6%, and the results of the pylon are 96% and 92.4%, respectively. The identification rate of power line identification is above 98%.

The detection of anomalous Internet-of-Things (IoT) sensor data is extremely important in many industrial applications due to the catastrophic consequences of the faulty or unreliable sensor data. Good anomalous data detection performance with high detection efficiency is indeed a dilemma since it is difficult to derive an explicit detection function to characterize the relationships between the anomalous values and the detection indicator. To overcome this difficulty, the location information of the IoT sensors is exploited in this study to characterize and reconstruct the relationships among the sensor data based on a second-order nonlinear polynomial graph filter (NPGF). The analysis of the sensor data reconstruction model is then conducted in the frequency domain based on the 2-D inverse graph Fourier transform (GFT), and the reconstruction error function for the sensor data is analytically derived based on the second-order GFT coefficients. It is shown that the detection efficiency can be greatly improved if the input graph signal is designed to be bandlimited. The anomalous sensor data detection is then conducted in the frequency domain as high-frequency components are more sensitive to the deviation values. An NPGF-based frequency-domain algorithm is proposed for the anomalous sensor data detection, which is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the detection performance and efficiency improvement of the proposed algorithm in anomaly detection.

The multi-robot formation is a key technology for underwater searching and other tasks. This paper aims to use underactuated underwater robots to establish a leader-follower formation model. Firstly, the kinematics and dynamics characteristics of the single AUV are analyzed and the coordinate transformation is introduced to decouple the dynamics of the AUV. Then the leader-follower model is obtained and the linear feedback controller is proposed, and the Lyapunov direct method is applied in the stability analysis. The simulation experimental results show that the formation of two AUVs was controlled effectively by the leader-follower formation model. We also design hardware platform to verify the reliability of the simulation. Moreover, the experimental results of expanding from two AUVs formation to three AUVs formation also prove the applicability of the method for the multi-AUVs system.

The routine power line inspection is critical to maintain the reliability, availability, and sustainability of electricity supply. As a key part of inspection, power lines and pylons extraction is essential for resource management and power corridor safety, especially in the mountain regions. In this paper, we proposed a deep learning based method to extract power lines and pylons using ALS point clouds. First, a structure information preserved module is designed to mine the relationship of local neighborhood points. Then, a graph convolutional network (GCN) is used as basic module to extract point features. Finally, three categories, power lines, pylons and other objects are segmented from input point clouds. In addition, we provide an effective data enhancement strategy to generate enough samples to train the proposed model. We evaluated our method using a dataset acquired by our ALS scanning system. Experimental results demonstrate that our method is superior to the state-of-the-art methods on descriptiveness and efficiency. The overall accuracy and mean time are 99.1% and 9.3 seconds, respectively.

Three-dimensional point clouds can describe the shape and position of objects more accurately when compared with 2D images, thereby providing richer information for object recognition, detection, and reconstruction tasks. Extracting vehicles in point clouds of underground parking lots can help autonomous vehicles achieve automatic parking. Camera-based perception algorithms will fail in complicate environments, so it is necessary to study algorithms for extracting targets using point cloud data. In this paper, we designed an effective method to extract the vehicles in the underground parking lot. First, the point clouds belonging to the vehicle will be segmented using a neural network based on graph convolution, and then different vehicles will be separated based on clustering. Finally, the minimum bounding box for each car is calculated. The proposed approach achieved much better results on the point cloud dataset than other state-of-the-art methods. Our method achieves 99.6% in Overall Accuracy and 98.5% in Mean IOU (Intersection over Union).

Detecting the existence of anomaly and localizing the faulty sensors in the Internet-of-Things (IoT) systems are extremely critical, since the incorrect data could lead to catastrophic consequences in many vertical industry applications. The difficulties of such problems come from deriving an explicit error function for each sensor in IoT, and the data continuity in the temporal domain would also be seriously challenged. To overcome these difficulties, the irregular spatial information of the IoT sensors is utilized by constructing an adjacency matrix using the distances among different sensors, and the nonlinear polynomial graph filter (NPGF) is employed to characterize the relationships among the collected sensor data. The NPGF provides a more accurate model for reconstructing the sensor data by taking the data nonlinear relationships into account. The error functions at each sensor for newly detection data are theoretically derived, and it is demonstrated that the error at the anomalous sensor performs differently from that of the normal sensors if the adjacency matrix is designed appropriately. The proposed NPGF-based algorithm is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the superior performance of our scheme in both anomaly detection and faulty sensor localization when compared with existing algorithms, such as the graph frequency algorithm and oversampling PCA (OS-PCA) method, especially for the case of small sensor data deviations.

This paper presents a new method for extracting urban roadside trees automatically from mobile laser scanning point clouds. This method mainly includes three steps. First, ground point clouds are removed by voxel-based upward growing method. Second, Euclidean distance segment method is used to cluster non-ground point clouds into certain individual objects. Then crown seeds of the initial layer is found by comparing the number of points in each layer after using the voxel modeling algorithm. Crown seeds in other layers can thereafter be detected via the upward inter-sectional analysis. Third, a crown voxel growing algorithm is used to make the crown grow in horizontal. The experimental results show that the voxel models of the individual roadside trees can be automatically and effectively extracted with our method.