Zhu Han’s research while affiliated with University of Houston and other places

The growing use of smart devices requires improving privacy and security. Conventional biometrics confront false positives and unauthorized access, stressing cautious user input. We enhance security by analyzing distinctive human physiological characteristics rather than relying on conventional methods susceptible to spoof attacks. Drinking, a common physiological activity, can provide continuous authentication. SipDeep , proposed innovative system, utilizes bone-conducted liquid intake sound, incorporating unique biometrics from bone and pharyngeal characteristics. The system captures these elements in the external auditory canal, offering a novel transparent authentication applicable to a diverse user range. Our noise filtering system eliminates environmental and anatomical interferences during drinking, including subtle body movements. The study introduces a hybrid event detection technique integrating wavelet transform with start/end points detection. Next, we extract physiological features from bone structure, liquid intake sound, and liquid intake pattern. We used the physiological features to train a deep learning algorithm based on a Triplet-Siamese network to classify authentication. The proposed model has been thoroughly compared with advanced models such as DenseNet169, ResNet18, and VGG16. Following extensive experimentation involving multiple users across various environments, SipDeep demonstrates 96.5% authentication accuracy, coupled with a 98.33% resistance to spoof attacks.

By utilizing intelligent mobile terminals, mobile crowdsensing (MCS) can realize the sensing data collection effectively and economically. However, the privacy security and freshness quality of the obtained sensing data are two major concerns to be addressed in MCS, as they directly impact the system security and timeliness performance. In this regard, we focus on improving the data freshness performance and protecting sensing data content, sensing terminals' identification, and location information simultaneously. Accordingly, based on the artificial noise (AN)-based differential privacy and covert communication technologies, we aim to jointly minimize the Age of Information (AoI) metric and weighted privacy preservation budget in the single terminal scenario. Besides, we achieve the goal of average AoI optimization with data computing requirements in multiple terminal systems, where the privacy preservation budget is treated as the critical constraint. Furthermore, by using the backward induction (BI) method and block successive upper-bound minimization (BSUM) approach, we solve the above two optimization problems, respectively. Finally, compared with the listed baselines, the results evaluate the proposed schemes' effectiveness under various simulation settings.

The Internet of Things (IoT) ecosystem is undergoing a significant evolution through its integration with satellite networks, empowering remote and computation-intensive IoT tasks to leverage computing services via satellite links. Current research in this field predominantly focuses on minimizing latency and energy consumption in computation offloading, yet overlooks the substantial costs incurred by satellite resource utilization. To address this oversight, we introduce a Cost-Effective Hybrid Computation Offloading (CE-HCO) paradigm in satellite-terrestrial integrated networks (STINs) in this paper. First, we propose the 5G-based system framework facilitates gNB and UPF functionalities on satellites and fosters collaboration between public cloud providers and satellite operators. The framework is in line with the latest 3GPP activities and business models in satellite computing. Then, we formulate the CE-HCO problem, aiming to minimize total computation offloading costs while satisfying diverse user latency requirements and adhering to satellite energy constraints. To tackle this NP-hard problem, we develop an algorithm employing the penalty method and successive convex approximation to simplify the complex mixed-integer nonlinear programming into tractable convex iterations. Simulation results show that our approach outperforms existing baselines in balancing performance and cost, and offer guidance on pricing policies for satellite computing services to promote future commercial growth.

Existing studies on ultraviolet (UV) non-line-of-sight (NLoS) channel modeling primarily focus on scenarios without any obstacle, which makes them unsuitable for small transceiver elevation angles in most cases. To address this issue, a UV NLoS channel model incorporating an obstacle was investigated in this paper, where the impacts of atmospheric scattering and obstacle reflection on UV signals were both taken into account. To validate the proposed model, we compared it to the related Monte-Carlo photon-tracing (MCPT) model that had been verified by outdoor experiments. Numerical results manifest that the path loss curves obtained by the proposed model agree well with those determined by the MCPT model, while its computation complexity is lower than that of the MCPT model. This work discloses that obstacle reflection can effectively reduce the channel path loss of UV NLoS communication systems.

As transceiver elevation angles increase from small to large, existing ultraviolet (UV) non-line-of-sight (NLoS) models encounter two challenges: i) cannot estimate the channel characteristics of UV NLoS communication scenarios when there exists an obstacle in the overlap volume between the transmitter beam and the receiver field-of-view (FoV), and ii) cannot evaluate the channel path loss for the wide beam and wide FoV scenarios with existing simplified single-scattering path loss models. To address these challenges, a UV NLoS scattering model incorporating an obstacle was investigated, where the obstacle's orientation angle, coordinates, and geometric dimensions were taken into account to approach actual application environments. Then, a UV NLoS reflection model was developed combined with specific geometric diagrams. Further, a simplified single-scattering path loss model was proposed with a closed-form expression. Finally, the proposed models were validated by comparing them with the Monte-Carlo photon-tracing model, the exact single-scattering model, and the latest simplified single-scattering model. Numerical results show that the path loss curves obtained by the proposed models agree well with those attained by related NLoS models under identical parameter settings, and avoiding obstacles is not always a good option for UV NLoS communications. Moreover, the accuracy of the proposed simplified model is superior to that of the existing simplified model for all kinds of transceiver FoV angles.

Integrated sensing and communication (ISAC) systems traditionally presuppose that sensing and communication (S&C) channels remain approximately constant during their coherence time. However, a “DISCO” reconfigurable intelligent surface (DRIS), i.e., an illegitimate RIS with random, time-varying reflection properties that acts like a “disco ball”, introduces a paradigm shift that enables active channel aging more rapidly during the channel coherence time. In this letter, we investigate the impact of DISCO jamming attacks launched by a DRIS-based fully-passive jammer (FPJ) on an ISAC system. Specifically, an ISAC problem formulation and a corresponding waveform optimization are presented in which the ISAC waveform design considers the trade-off between the S&C performance and is formulated as a Pareto optimization problem. Moreover, a theoretical analysis is conducted to quantify the impact of DISCO jamming attacks. Numerical results are presented to evaluate the S&C performance under DISCO jamming attacks and to validate the derived theoretical analysis.

As a fundamental technology of the Metaverse, blockchain enables numerous Metaverse applications. However, the blockchain consensus mechanism’s high energy consumption and performance bottlenecks have become an impediment to the green and sustainable development of the Metaverse. To tackle this challenge, we propose a lightweight consensus service framework that amplifies the throughput of the multi-chain system while concurrently mitigating its energy consumption. Specifically, we first establish a reputation rating system by designing a set of reputation rating and bonus-penalty rules. These rules efficiently evaluate the credibility of nodes in the multi-chain and optimally select the consensus node group, thereby streamlining the consensus procedure with security guarantees. Next, we incorporate the rating system into the proposed framework by constructing transaction processing workflows in both single-chain and cross-chain consensus scenarios. Additionally, we analyze the framework’s performance in terms of throughput, transaction latency, and energy consumption. The testing experiments demonstrate that the proposed framework significantly enhances performance compared with conventional blockchain systems, specifically a 48.7% improvement in throughput, a 56% reduction in transaction latency, and a 35% reduction in energy consumption.

Holographic multiple-input-multiple output (HMIMO), which is enabled by large-scale antenna arrays with quasi-continuous apertures, is expected to be an important technology in the forthcoming 6G wireless network. Reconfigurable intelligent surface (RIS)-based antennas provide an energy-efficient solution for implementing HMIMO. Most existing works in this area focus on single-polarized RIS-enabled HMIMO, where the RIS can only reflect signals in one polarization towards users and signals in the other polarization cannot be received by intended users, leading to degraded data rate. To improve multiplexing performance, in this paper, we consider a dual-polarized RIS-enabled single-user HMIMO network, aiming to optimize power allocations across polarizations and analyze corresponding maximum system capacity. However, due to interference between different polarizations, the dual-polarized system cannot be simply decomposed into two independent single-polarized ones. Therefore, existing methods developed for the single-polarized system cannot be directly applied, which makes the optimization and analysis of the dual-polarized system challenging. To cope with this issue, we derive an asymptotically tight upper bound on the ergodic capacity, based on which the power allocations across two polarizations are optimized. Potential gains achievable with such dual-polarized RIS are analyzed. Numerical results verify our analysis.

Unmanned aerial vehicles (UAVs) have been envisioned as a promising technology to support wireless virtual reality (VR) applications. However, the huge energy consumption during the rendering process is challenging for UAV-enabled VR systems. The existing energy management strategies neglecting the dynamic and tiling features of VR tasks may not achieve optimal performance. To cope with these issues, we present a dynamic energy optimization framework for UAV-enabled VR systems. In the proposed framework, a tile-based collaborative rendering scheme for multi-UAVs is developed by exploiting the correlation among different tiles of VR users. Further, we formulate an optimization problem by jointly considering the collaboration decision, resource allocation, and UAV trajectory, aiming to reduce the long-term energy consumption under the motion-to-photon (MTP) latency requirement. Due to the tight coupling variables and the dynamic factors, the formulated problem is non-convex and difficult to tackle by adopting the traditional method. To this end, we first decompose the original problem into two sub-problems, i.e., the UAV trajectory and collaboration decision (UTCD) problem, and the resource allocation (RA) problem. Then, we design a dynamic UTCD algorithm based on deep reinforcement learning (DRL) and present an optimal RA algorithm based on the Lagrangian multiplier iterative (LMI) method. To enhance the solving efficiency, a block coordinate descent (BCD)-based solving method is introduced by embedding the RA algorithm in the learning environment of DRL. Finally, simulation results prove that the proposed scheme can obtain good performance for UAV-enabled VR systems.

To meet the growing demand for joint data and energy transmission, research on wireless information and power transfer is being promoted. The resonant beam enabled information and power transfer (RBIPT), which supports long-distance, high-power, and wide-bandwidth information and power transfer, has sparked widespread interest. The point-to-multipoint RBIPT system shows great promise for enabling simultaneous RBIPT for multiple receivers. However, the enabling system architecture has not been well studied in the literature, which is holding back the system implementation. To solve this problem, we propose a time division multiplexing RBIPT (TDM-RBIPT) system for multiple access, and constract a novel metric to evaluate the information and power transfer performance. We explore the TDM-RBIPT mechanism and design the architectures of the transmitter and the receiver. For the information transfer performance evaluation, we take system latency and throughput into consideration. We propose to estimate the system delay with the martingale theory by modeling the dynamic data processing procedures as Markovian processes with the markov chain monte carlo (MCMC) method. To evaluate the power transmission performance, we consider the transmitter’s power costs and the receivers’ power benefits. Numerical results reveal the effectiveness of the proposed TDM-RBIPT system and validate the accuracy of the proposed metric.