Monitoring pilots’ mental states during training is important for ensuring safety and optimizing performance, yet existing full-cap EEG systems are impractical for operational use. In this work, we evaluate a headphone-style 9-channel electrode montage rather than a complete headphone EEG device, and propose a transfer learning framework to preserve performance under sparse coverage. Our two-stage approach first applies self-supervised pretraining on 64-channel full-cap EEG, and then adapts the learned representations to the headphone-style montage through a position correction module that accounts for electrode misplacement. We validate the framework in a flight simulator with 12 participants on two tasks: motor intention (left vs. right) and cognitive workload (low vs. high). Despite limited coverage, the headphone-style montage achieves within-subject accuracies of 85% for motor imagery and 89% for workload, compared to full-cap performance of 88% and 91%. Cross-subject accuracies reached 66% and 73%, demonstrating generalizability across users. Ablation analyses show that both self-supervised pretraining and position correction independently improve performance, and together provide a 12% boost in cross-subject decoding accuracy. These findings highlight the feasibility of headphone-style EEG montages for practical pilot monitoring, while clarifying methodological limitations and the need for future real-time and user-centered evaluation.

Online ride-hailing services (ORHS) are changing the travel mode. The quality evaluation of ORHS is essential to regulate driver behavior and guide passengers in choosing good services. The existing quality evaluation methods of ORHS rely on subjective passenger feedback, while they are susceptible to malicious or paranoid feedback, resulting in untrustworthy evaluation results. This paper proposes a non-subjective trust mechanism for ORHS to supplement existing evaluation methods. Inspired by the trust machine, this mechanism defines the concept of non-subjective trust to measure the quality of ORHS. It uses trajectory data collected by infrastructure as a parameter to calculate the non-subjective trust value of ORHS, which can ensure the trustworthiness and authenticity of the calculation results. In addition, a blockchain is adopted to store trajectory data and trust values in the infrastructure. It promotes its flexible management and use and ensures the security of data and traceability of evaluation results. Security analysis and extensive experiments show that the trust value calculated by this mechanism is resistant to attacks and trustworthy.

This paper investigates the fundamental covert rate performance in a D2D-enabled cellular network consisting of a cellular user Alice, a base station BS, an active warden Willie, and a D2D pair with a transmitter$D_{t}$and a full-duplex receiver$D_{r}$. To conduct covert communication between Alice and BS, the full-duplex$D_{r}$transmits jamming signal to confuse the active Willie and also receives signal from$D_{t}$simultaneously. With spectrum sharing,$D_{t}$can operate over either an underlay mode reusing cellular spectrum or an overlay mode using dedicated spectrum. With power control,$D_{r}$can send jamming signal to confuse Willie's detection of the transmission from Alice. We first provide theoretical results for the outage probabilities of the cellular and D2D transmissions, the average minimum detection error probability at Willie, and the achievable covert rate from Alice to BS. We then explore the power control for covert rate maximization (CRM) under the underlay mode as well as the joint designs of power control and spectrum partition for CRM under the overlay mode. We further consider a mode selection that flexibly switches between these two modes with a probability, and also investigate the covert rate modeling and joint designs of power control, spectrum partition and mode selection probability for CRM. Finally, numerical results are presented to illustrate the covert rate performances of the network under the underlay mode, overlay mode and mode selection.

This paper investigates the joint secrecy and covert communication in a two-hop relaying system consisting of a transmitter Alice, a receiver Bob, an eavesdropper Eve and a Relay. Eve always overhears the secret message from Alice. Meanwhile, Alice transmits covert message on top of secret message without being detected by Relay. First, we propose a covert transmission scheme that Alice will transmit covert message only when the signal-to-interference-plus-noise-ratio (SINR) of the secret message is greater than a threshold. Then, we provide the closed-form expressions of covert performance, i.e., the optimal detection threshold of Relay and the corresponding minimal detection error probability as well as the average minimum detection error probability. Finally, extensive numerical results are presented to illustrate the impacts of the system parameters on covert performances. Remarkably, the corresponding theoretical results well match with the simulation ones indicating that our theoretical analysis can accurately model the covert performance of the considered system.

Recent Blockchain-based Internet of Vehicles (BIoV) solutions are proposed to provide the capabilities of trust management and incentive distribution for traffic information interaction in decentralized trustless Internet of Vehicles (IoV). However, existing trust management methods in BIoV are designed based on subjective user feedback, which is vulnerable to bad-mouthing and collusion attacks. Besides, these incentive strategies achieve accurate information interaction based on the game theory, yet it is challenging for the practical IoV scenario without completely explicit parameters. To address these issues, we propose TI-BIoV, a traffic information interaction system based on three blockchains for IoV with the nonsubjective trust evaluation and optimal incentive with partial inexplicit parameters. Specifically, a nonsubjective trust mechanism is designed based on the traffic information offset calculated by other related traffic information, which ensures the change of vehicle trust value without any subjective factors. On this basis, a trust-based consensus protocol, which selects entities with high trust values as participants, is given to realize the reliable public audit of transactions. According to traffic information accuracy measurements, we develop a$Q$-learning-based algorithm to encourage vehicles continuously submit accurate traffic information and optimally schedule the incentive for both platform and vehicle via training with incompletely explicit parameters of TI-BIoV. Finally, we analyze the security properties and common attacks of TI-BIoV and implement a prototype. The experimental results show that TI-BIoV achieves reliable consensus with nonsubjective trust evaluation and runs stably for a long time with two-sided incentive strategies.

Driven by the fast increase of food demand world wide, the safety of grain storage becomes increasingly important. TWo key factors, i.e., temperature and moisture, greatly influence the safety of stored grain. The traditional methods of detecting grain temperature and moisture are time-consuming, expensive, and inconvenient to use. In this paper, we develop a TagSense system for robust wheat moisture and temperature sensing using cheap commercial-off-the-shelf (COTS) RFID devices. We first validate the feasibility of using tag impedance for robust moisture and temperature sensing. We then propose a distance- free algorithm and an angle-agnostic method to mitigate the impact of different measurement distances and angles. Our experimental results demonstrate that the TagSense system can achieve satisfactory sensing accuracy of wheat moisture and temperature in different rotation angles and at different distances.

The development of Internet-of-Things (IoT) technology promotes the advances of grain condition detection and analysis systems. Temperature monitoring is a main element to maintain grain quality, and effective control of grain temperature is crucial to safe storage of grain. In this article, an encoder–decoder model with attention mechanism is proposed to accurately forecast the temperature of stored grain. Considering that the points on the gradient direction of the temperature surface have a great influence on the temperature of the target point, the Sobel operator is used to extract the local characteristics of the target point. In addition, considering the correlation structure in the sensory data, the attention mechanism is used to extract the global features of the target point. The extracted spatial features are fed into long short-term memory (LSTM) networks to obtain the long-term state information of spatial factors. LSTM unit and convolutional neural network are used to encode the spatial features of the target points. Taking meteorological factors as the external input of the decoder, temporal attention mechanism and LSTM unit are used to complete the decoding process and realize the prediction of grain temperature in the future. The results with real grain storage data show that the proposed model outperforms several schemes, including Kalman-modified the least absolute shrinkage and selection operator (Kalman-modified LASSO), temporal graph convolutional network (T-GCN), LSTM, CNN-LSTM, and convolutional LSTM (Conv-LSTM), with considerable gains.

Unmanned aerial vehicle (UAV) swarm connected to millimeter wave (mmWave) cellular networks is emerging as a new promising solution to provide ubiquitous high-speed and long distance wireless communication services for supporting various applications. To satisfy different quality of service (QoS) requirements in future large-scale applications of such networks, this article investigates the rate performance, fairness and their tradeoff in the networks with directional antennas in terms of sum-rate maximization, fairness index maximization, max-min fair rate and proportional fairness. We first consider a more realistic mmWave 3D directional antenna array model for UAVs and base station (BS), where the antenna gain depends on the radiation angle of the antenna array. Based on this antenna array model, we formulate the performance, fairness and their tradeoff as four constrained optimization problems, and propose corresponding iterative algorithm to solve these problems by jointly optimizing elevation angle, azimuth angle and height of antenna array at BS in the downlink transmission scenario. Furthermore, we also explore them in uplink transmission scenario, where the interference issue among links is carefully considered. Finally, according to the sum rate, minimum rate and fairness index under each optimization problem, numerical results are provided to illustrate the impacts of network parameters on the performance, fairness and their tradeoff, and also to reveal new findings under both downlink and uplink transmission scenarios, respectively.

Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatio-temporal attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods.

The shortest-path algorithm is one of the most important algorithms in geographical information systems. Bellman’s principle of optimization (BPO) is implicit in the shortest-path problem; that is, any involved node must be located in the simple paths between source and destination nodes. Unfortunately, BPO has never been explicitly used to exclude irrelevant nodes in existing methods, potentially leading to unnecessary searches among irrelevant nodes. To address this problem, we propose a BPO-based shortest-path acceleration algorithm (BSPA). In BSPA, a high-level graph is built to locate the necessary nodes and is used to partition the graph and divide a given task into independent subtasks. This allows the speed of any existing method to be improved using parallel computing. In a test using random graphs, on average, at most only 1.209% of the nodes need to be involved in the calculation. When compared with existing algorithms in real-world road networks, the BSPA shows faster preprocessing and query times, being respectively 118 and 463 times faster in the best case. In the worst case, they remain slightly faster.

In this paper, we propose a real-time, nondestructive, and low-cost wheat mildew detection system using commodity WiFi devices, which is a new application of the Internet of Things (IoT) to agriculture applications. We first introduce wheat mildew and validate the feasibility of wheat mildew detection using WiFi Channel State Information (CSI) amplitude data. We then present the MiFi system design, including CSI sensing, preprocessing, radial basis function (RBF) neural network based detection modeling, and mildew detection. Our experimental results validate the effectiveness of the proposed MiFi system. The average detection accuracy of the MiFi system is over 90% under both line-of-sight (LOS) and non-line-of-sign (NLOS) scenarios.

In this paper, we present a non-destructive and economic wheat moisture detection system with commodity WiFi. First, we experimentally validate the feasibility of wheat moisture detection by using CSI amplitude and phase difference data. We then design Wi-Wheat system, where data preprocessing, feature extraction and support vector machine (SVM) classification are implemented for CSI processing module. For data preprocessing, we employ outlier detection, data normalization and eliminating noise for obtaining clear CSI amplitude and phase difference data. Then, we consider principal component analysis (PCA) based feature extraction for Wi-Wheat system. For SVM classification, Gaussian radial basis function (RBF) is used as the kernel function for wheat moisture detection. The experimental results show the Wi-Wheat system can achieve higher classification accuracy for LOS and NLOS scenarios.

Moisture content of cereal grains is a highly important factor in safe storage and food processing. The existing detection methods are either time-consuming, sensitive to the environment, or have a high cost. In this paper, we propose DeepWMD, a deep LSTM network based system for multi-class wheat moisture detection. We first collect CSI amplitude and phase difference data to detect wheat moisture content. Then, we design the DeepWMD system with commodity Wi-Fi devices in the 5GHz band, including data preprocessing of collected CSI data, offline training, and online testing. Our experimental results verify the efficacy of the proposed DeepWMD system, and demonstrates that DeepWDM can achieve high-precision multi-class wheat moisture detection in different indoor storage environments.