Dynamic gesture recognition using millimeter-wave radar has a broad application prospect in the industrial Internet of Things (IoT) field. However, the existing methods in the random dynamic interference environment, such as throwing objects and waving and easily cause wrong recognition. This article proposes a dynamic gesture recognition method based on a convolutional neural network (CNN)-Transformer network to solve this problem. First, we reshape the original echoes acquired by the frequency-modulated continuous-wave (FMCW) millimeter-wave radar into 3-D data blocks in terms of Chirps $\times $ Samples $\times $ Frames. And we employ the mean elimination method to eliminate the static interference. Second, we extract dynamic gestures’ distance and Doppler information with the 2-D fast Fourier transform and obtain the range-time map and Doppler-time maps. And we employ the coherent accumulation method to improve the signal-to-noise ratio (SNR). Third, we construct the CNN-Transformer network model for dynamic gesture recognition. The CNN is used to extract the local features of gestures, and multiple Transformer modules are stacked to extract deeper effective features. Finally, we build a data set for gesture recognition, including six kinds of dynamic gestures and two kinds of random interference. The experimental results show that the proposed method has a gesture recognition accuracy of more than 98% and 96% in the noninterference scene and the random dynamic interference scene, respectively, which are superior to the conventional recognition methods.

Underwater acoustic target recognition is an important supporting technology for underwater information acquisition and countermeasure. Usually, ship radiated noise is covered by the underwater acoustic background and previous deep learning methods for this task rely on clear and effective acoustic features. We propose a novel network called AMNet to alleviate the problem in this paper. It consists of a multi-branch backbone network coupled with a convolutional attention network. The proposed network is able to obtain the internal features of radiated noise from the time-frequency map of the original data. The convolutional attention network adaptively selects the effective features by weighting them against the global information of the time-frequency map to assist the multi-branch backbone network in classification recognition. Experimental results demonstrate that our model achieves an overall accuracy of 99.4% (2.4% improvement) on the ShipsEar database.

Due to the large path loss in millimeter wave (mmWave) band, the transmission path between transmitter (Tx) and intelligent reflecting surface (IRS) is considered as a Rayleigh fading channel, and a physics-based channel model is proposed for IRS-assisted mmWave communication system in urban scenario. Also, the horizontal and vertical rotation angles of IRS and the relationship between the scattering gain of IRS reflecting unit and its effective aperture in the incident direction and the desired reflection direction are considered in the proposed model. For the considered communication scenario, the existing reflection phases, which are designed to align the virtual line-of-sight (VLoS) components among Tx, IRS, and receiver (Rx) with the LoS components between Tx and Rx, are not the appropriate reflection phases. Based on the proposed model, we first obtain the statistical phases of the virtual scattering components within a cluster by minimizing phase differences between different IRS reflection units, and then obtain the reflection phases by minimizing the phase differences of the derived statistical phases for all clusters. By comparing with the existing reflection phases, the designed reflection phases can significantly enhance the system performance gains of mmWave communications. Using the designed reflection phases, the expressions of received signal power and upper bound of ergodic sum capacity are derived in this paper, which are validated by using Monte-Carlo simulation results. Numerical results show that the proposed mmWave channel model could accurately simulate the propagation characteristics of IRS. Also, numerical results show that the performance gains of IRS-assisted systems are equivalent to that of large-scale communication systems without using IRS.

The navigation accuracy of the ship can be improved by associating the output tracks of radar with that of the automatic identification system (AIS). The traditional association method only uses the track features (including position, speed, and heading angle) and has low association accuracy and weak scene adaptability. We integrate the track and scene features through deep learning to associate the radar and AIS output tracks. Firstly, we employ the auto-encoder to reduce the noise of the original track data. The statistical distances of the two tracks in position, speed, and heading are obtained as the new track data. We map the track data to the high-dimensional space to extract the track features through the fully connected and dropout layers. Then, we reconstruct the scene data using the Euclidean distances of all tracks in position. The scene data is input into the three-dimensional (3D) convolutional and max-pooling layers to extract the scene features. Finally, we fuse the track and scene features in the channel dimension by the concatenated layer and use the fully connected layer to estimate the probability of two tracks coming from the same target. The simulation results demonstrate that the proposed method has better scene adaptability and higher association accuracy than the traditional methods.

Low energy consumption has always been one of the core issues in the routing design of underwater sensor networks. Due to the high cost and difficulty of deployment and replacement of current underwater nodes, many underwater applications require the routing protocol design to consider the network lifetime extension problem. Based on this, we designed a new routing protocol that takes into account both low energy consumption and balanced energy consumption, and achieves effective extension of the network lifetime, called adaptive power-controlled depth-based routing protocol for underwater wireless sensor networks (APCDBRP). The protocol consists of two phases: (1) the route establishment phase and (2) the data transmission phase. In the route establishment phase, the initial path is established by the sink node broadcasting beacon packets at the maximum transmission power. The receiving nodes update their routing tables based on the beacon information and forward the beacon packets. In the data transmission phase, APCDBRP introduces a novel forwarding factor that considers both energy efficiency and energy balance. It selects the optimal next hop based on high energy efficiency and relatively abundant energy, thus extending the network’s lifetime. Additionally, APCDBRP proposes a new data protection and route reconstruction mechanism to address issues such as network topology changes due to node mobility and data transmission failures. Our simulation is based on AquaSim–Next Generation, which is a specialized tool built on the NS3 platform for researching underwater networks. Simulation results demonstrate that, compared to other typical routing protocols, APCDBRP exhibits superior performance in reducing network energy consumption and extending the network’s lifetime. It also achieves a high packet delivery rate with lower energy consumption.

Underwater acoustic covert communication (UACC) is a promising technology for improving the security of sensitive data transmission in the Ocean Internet of Things (OIoT). Compared with traditional UACC, autonomous underwater vehicles (AUV) are used to improve the performance of UACC by transmitting interference signals and relaying information. However, the UACC process of the covert transmitter is vulnerable to the detector’s detection, and the AUV-assisted UACC countermeasure process lacks theoretical analysis. Hence, we propose an AUV-assisted UACC game (AUACCG) model to study the countermeasure process between the AUV-assisted covert communicator and the detector in OIoT. Specifically, we utilize AUV transmitting the interference signals to reduce the detector’s capability, analyze the utility functions and strategy set of both sides and derive the equilibrium strategies. In addition, we analyze the concealment probability and the outage probability in UACC. Then, we derive the specific Nash equilibrium strategy in AUACCG. Simulation results show that our proposal can improve the UACC performance compared with the state-of-art works.