Hashing technologies have been widely applied for large-scale multimodal retrieval tasks owing to their excellent performance in search and storage tasks. Although some effective hashing methods have been proposed, it is still difficult to handle the intrinsic linkages that exist among different heterogeneous modalities. Moreover, optimizing the discrete constraint problem through a relaxation-based strategy results in a large quantization error and leads to a suboptimal solution. In this article, we present a novel asymmetric supervised fusion-oriented hashing method, named (ASFOH), which investigates three novel schemes to remedy the above issues. Specifically, we first explicitly formulate the problem as matrix decomposition into a common latent representation and a transformation matrix, combined with an adaptive weight scheme and nuclear norm minimization to ensure the information completeness of multimodal data. Then, we associate the common latent representation with the semantic label matrix, thereby increasing the discriminative capability of the model by constructing an asymmetric hash learning framework, thus, making the generated hash codes more compact. Finally, an efficient discrete optimization iterative algorithm based on nuclear norm minimization is proposed to decompose the nonconvex multivariate optimization problem into several subproblems with analytical solutions. Comprehensive experiments on the MIRFlirck, NUS-WIDE, and IARP-TC12 datasets testify that ASFOH outperforms the compared state-of-the-art approaches.

Pilot spoofing attack brings new challenges to physical layer secure transmission. However, this method will not work without any knowledge about the pilot sequence and active eavesdropping can be detected by constructing random pilot sequence. Intelligent reflecting surface (IRS), with the real-time programmable features for wireless channels, provides new possibilities for effective pilot spoofing. In this paper, the IRS is deployed near the legitimate users and the legitimate signal can always be passively reflected. Then the control strategy is embedded into the communication process under time-division duplex mode to assist eavesdroppers to conduct pilot spoofing. By setting different phase shifts at the IRS during the uplink phase and downlink phase, the channel reciprocity disappears, and thus secure beamforming vector is biased towards the eavesdropper. Furthermore, in order to overhear more information, the average secrecy rate minimization problem based on statistical channel state information is established by carefully designing the phase shifts, which is non-trivial to solve. With alternating optimization and Charnes-Cooper transformation technique, the original problem is transformed into convex form and a near optimal solution is achieved. Finally, simulation results show that our proposed scheme can pose serious secure threat without any energy footprint. What's more, if the IRS is not utilized by the internal users properly, it will bring more threat.

Mobile crowd-sensing (MCS) is a well-known paradigm used for obtaining sensed data by using sensors found in smart devices. With the rise of more sensing tasks and workers in the MCS system, it is now essential to design an efficient approach for task allocation. Moreover, to ensure the completion of the tasks, it is necessary to incentivise the workers by rewarding them for participating in performing the sensing tasks. In this paper, we aim to assist workers in selecting multiple tasks while considering the time constraint of the worker and the requirements of the task. Furthermore, a pricing mechanism is adopted to determine each task budget, which is then used to determine the payment for the workers based on their willingness factor. This paper proves that the task-allocation is a non-deterministic polynomial (NP)-complete problem, which is difficult to solve by conventional optimization techniques. A worker multitask allocation-genetic algorithm (WMTA-GA) is proposed to solve this problem to maximize the workers welfare. Finally, theoretical analysis demonstrates the effectiveness of the proposed WMTA-GA. We observed that it performs better than the state-of-the-art algorithms in terms of average performance, workers welfare, and the number of assigned tasks.

The body sensor network (BSN), consisting of wearable or implantable devices, is a monitoring system applied to a healthcare environment based on the IoT technology. In BSN, prolonging the service cycle of the network is a major challenge due to the limited battery capacity and energy supply for sensors. To this end, improving energy efficiency and harvesting energy are the keys for the network to maintain sustainability. In this paper, we propose a transmission scheduling and energy harvesting strategy to manage energy supply and consumption, and build several dynamic models to capture the stochastic processes in BSN. Besides, a system utility maximization problem is formulated. Since this problem is a multi-objective mixed-integer optimization problem (MMOP) which is difficult to solve directly, we provide a solution framework where MMOP is decomposed into several subproblems by the Lyapunov optimization method. Based on this framework, we propose an online energy sustainability optimization algorithm to solve these subproblems, such as the matching problem and convex optimization problem, and theoretically prove that it can achieve the near-optimal system utility. Additionally, the appropriate sizes of the data buffer and battery capacity are derived, which can give a guidance to determine the sizes of these components. Simulation results show the impact of the system parameter on the utility and data and energy queues, and verify that the proposed strategy and methods can maintain the sustainable operation of BSN effectively.

Electrocardiogram sensor network (ECG-SN) is a medical monitoring system based on the IoT technology, which can detect heart bioelectric signals in real time. But the ECG signal is vulnerable to human mobility and sensitive to the age of information (AoI). In this paper, we first analyze the impact of human mobility on channel fading based on a real-world activity trace dataset, and designed a two-state ECG work model based on the trade-off between the ECG signal quality and energy consumption. Furthermore, an AoI model is proposed to evaluate the data timeliness. Then, an online transmission optimization algorithm is proposed to maximize the system utility by optimizing the sampling rate, transmission power, and data dropping rate. Further, performance analysis presents the bounds for data buffer, battery capacity, and AoI. Numerical results show the dynamics of the system and the impact of the parameters on system performance, which verify that the proposed design has a larger utility and a smaller AoI in comparison with two benchmark schemes.

Effective diabetes management requires regular and accurate blood glucose monitoring; however, traditional invasive methods often cause discomfort and inconvenience. Non-invasive techniques such as photoplethysmography (PPG) have been explored, though single-wavelength PPG systems are limited by the overlapping absorption characteristics between glucose and other biological components, such as water and fat. In this study, a novel multi-wavelength PPG system integrated with temperature and humidity sensors is introduced, coupled with a neural network framework featuring attention mechanisms to enhance glucose prediction. The system employs six optical sensors covering wavelengths from the visible to near-infrared (NIR) spectrum, enabling deeper tissue penetration and enhanced glucose specificity by targeting distinct absorption peaks—especially those above 1000 nm. The system was validated using a robust dataset of 26,063 measurements from 254 participants. The experimental results demonstrate significant improvements, with the model achieving 86.49% compliance with the ISO 15197: 2013 standards and 91.80% of measurements falling within Zone A of the Parkes error grid. The introduction of multiple wavelengths clearly improves performance over single-wavelength systems, and wavelengths above 1000 nm were shown to have a higher contribution in glucose prediction. In addition, the incorporation of temperature and humidity data also enhanced performance by accounting for environmental and physiological factors, and that demographic and meal-related factors significantly impact prediction accuracy, thereby underscoring the potential of this system as a reliable, non-invasive, and personalized glucose monitoring tool.