Extremely large-scale reconfigurable intelligent surfaces (XL-RISs) have been recognized as a promising technology to enhance the capabilities of communication systems and mitigate severe path loss. However, due to the large size of XL-RISs, simply adopting the deployment strategies of RIS may result in coverage areas with relatively low performance improvement. To investigate the potential contribution of each position on the designated surface, we propose a Shapley value sampling method to evaluate their Shapley values. Based on the approximated Shapley values, we propose a linear-time algorithm to determine the near-optimal XL-RIS deployment in terms of expected total transmission rate. Finally, simulation results show that the proposed Shapley sampling method effectively predicts the contribution of each XL-RIS module in total capacity, and we identify the scenarios where our deployment algorithm outperforms the conventional rectangular XL-RIS with the same number of RIS elements.

With the increasing prevalence of Internet of Things (IoT) devices, security vulnerabilities and malware infections have emerged as significant risks. To address these challenges, advanced vulnerability detection tools are essential for enhancing IoT security assessments. In this study, we analyzed common vulnerabilities and evolving attack methodologies to develop improved detection techniques. Our research focuses on two key areas: 1) comprehensive vulnerability detection and 2) malware infection testing strategies. Through on-site testing and detailed analysis, we identified prevalent security flaws in IoT devices and developed a suite of tools tailored for detecting these vulnerabilities. Additionally, we discovered that some devices exhibit inherent immunity to specific malware strains, emphasizing the need for novel malware infection detection strategies. Real-world evaluations uncovered previously unknown vulnerabilities and weaknesses, revealed widespread susceptibility to DoS attacks, and demonstrated that not all devices are vulnerable to malware infections. These findings confirm the effectiveness of our approach in identifying risks and enhancing IoT security.

The integration of artificial intelligence (AI) with the Internet of Things (IoT) marks a significant advancement in sixth-generation (6G) networks. The complexity of these AIoT services has promoted an as-a-service model, where service providers offer tailored architectures to meet varied application needs. Despite the critical importance of optimizing both training and inference in service architectures, this aspect remains under-explored. Our study introduces service scenarios such as ‘no shared (NS)’, where tenants manage their data and models independently, ‘data shared (DS)’, where tenants provide data for collective training, and ‘parameter sharing (PS)’, where only model parameters are shared. We utilize a tandem queue model to simulate the communication and computing demands across cloud-edge-fog architectures. Our proposed Cost and Delay Resource Allocation (CDRA) method significantly reduces costs, with edge and fog-based training and inference lowering costs by up to 44% compared to cloud setups. The evaluation shows that the NS scenario is resource-intensive but offers high privacy, DS is cost-effective and improves model accuracy, and PS balances privacy with longer wait times. These findings provide service providers with a comprehensive comparison of service scenarios and architectures, offering guidance for strategic and economically sound decisions in the ever-evolving landscape of AIoT.

Reconfigurable intelligent surfaces (RIS) is a promising technique to improve communication quality by adjusting the phase shift value of the passive reflected elements equipped on RIS. However, the phase shift value controlled by RIS may suffer from inevitable errors brought by hardware impairment and interference during transmission, which might lead to performance degradation. On the other hand, the assumption of uniform amplitude response made by most literature is not practical due to the imperfect reflection efficiency of the material. To jointly address these two issues, we consider a practical amplitude model that is a function of phase shift value and the phase shift value in our system is imposed an additional error following the Von-Mises distribution. To find the optimal solution that minimizes the average mean square error (MSE) of the received signal, we proposed a gradient descent method (GDM)-based phase shift algorithm that iteratively follows the gradient flow until converges. The numerical result is presented to show the superiority of our proposed algorithm over other existing algorithms.

Platooning is a representative application of connected and autonomous vehicles. The information exchanged between connected functions and the precise control of autonomous functions provide great safety and traffic capacity. In this paper, we develop an advanced consensus-based approach for platooning. By applying consensus-based fault detection and adaptive gains to controllers, we can detect faulty position and speed information from vehicles and reinstate the normal behavior of the platooning. Experimental results demonstrate that the developed approach outperforms the state-of-the-art approaches and achieves small steady state errors and small settling times under scenarios with faults.

BACKGROUND: Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks. RESULTS: We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism. CONCLUSIONS: In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.