The optimization problem of the number of mirrors under energy efficiency (EE) maximization for vehicular visible light communication (VVLC) via mirror array-based intelligent reflecting surface (IRS) is investigated. Under considering that the formulated optimization problem is subject to the real and non-negative of the transmitted signal, the maximum power consumption satisfied luminous ability and eye safety, the minimum achievable rate, and the required bit error ratio (BER), EE is proved to be a unimodal function of the number of mirrors. Then, the binary search-conditional iteration (BSCI) algorithm is proposed for quickly finding the optimal number of mirrors with maximum EE. Numerical results demonstrate that fewer mirrors can obtain the maximum EE, and the computational complexity of the BSCI algorithm is reduced by ${10}^5$ orders of magnitude, compared with the Bubble Sort method.

In the visible light communication (VLC) for parallel vehicles, the light emitted by the headlamps cannot reach the photodiode (PD), which is the receiver installed on another vehicle. To solve this problem, the mirror array-based intelligent reflecting surface- (IRS-) aided VLC system is designed for parallel vehicles, and the system performance with different number of mirrors and transmission distance is analyzed. The maximum distance between adjacent IRSs is calculated via the exhaustive method, and the nearest neighbor iterative search (NNIS) algorithm is proposed for IRS node selection. Numerical results show that the SNR increases with the number of mirrors increasing. If the number of mirrors is in the IRS, the maximum distance between adjacent IRSs is 37 meters. When the interval between adjacent IRSs is 32 meters, the required BER can be satisfied with merely three IRSs working together according to the NNIS algorithm.

In this paper, focusing on the application of wireless sensor networks (WSNs) in smart agriculture, unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) is used to tackle the issue of Line-of-Sight (LoS) channel between a sensor node (SN) and the fusion center being blocked by obstacles. In particular, the optimal number of reflecting elements is studied under the condition that the spectral efficiency (SE) and energy efficiency (EE) are balanced when the UAV hovers to collect sensors’ data. Firstly, the relationship between SE and EE versus the number of reflecting elements for a single SN is analyzed, and then the Conditional Judgment-Binary Search algorithm (CJ-BS) is proposed to seek the optimum number of reflecting elements. Secondly, on this basis and considering that multiple sensor nodes (SNs) send data to the UAV by adopting time-division multiple access, this paper further analyzes the optimum number of reflecting elements when different SNs use the same or different numbers of reflecting elements (SNRE or DNRE scheme) at different time slots. Thirdly, to find the optimal one in these two schemes we analyze the EE and compare them. Under the above cases, we prove that the EE is a unimodal function of the number of reflecting elements. Simulation results show that with the increase of the number of reflecting elements and the hovering altitude, the relative EE improvement of SNRE versus DNRE first increases and then decreases. Furthermore, considering that the SNRE scheme’s complexity is lower, it is a better choice for practical implementation.

One of the important features of the “new information age” is “big data”. In the age of big data, with the introduction of student information management systems, colleges and universities are increasingly paying attention to systematically managing student information management and improving their efficiency, effective management of student information and innovative development to build a new model of management system. In studying the evolution of student information management, big data technologies emerging in the information age play an important role. By leveraging big data to improve systematic management of student information, colleges and universities can manage student information faster and more efficiently and make new contributions to the advancement of student information management. In this article, information management is considered as the theoretical basis for research, big data technology is used as the main search algorithm, and its important content is combined to analyze and study the possibilities of the student information management system. In this article, an information management system for university students is used as a research object, which is optimized and improved through big data technologies. Big Data technology can be seen as a kind of algorithm for analyzing and processing data. Therefore, it can be used to create a student information management strategy. The experimental results show that this research has a better impact on the creation and implementation of an information management system for students using Big Data technology and the application of Big Data technology in the information management system for students.

Uncrewedaerial vehicle (UAV)-carried intelligent reflecting surfaces (U-IRSs) can be utilized to assist blocked communications between sensor nodes (SNs) and the fusion center in wireless sensor networks (WSNs). This article investigates a U-IRS-assisted data collection system in WSNs that employs the hover priority scheme. Given the energy constraints of UAV, the combined energy consumption from UAV moving/hovering and IRS reflecting elements configuration circuitry poses significant challenges to improving the system’s energy efficiency (EE). To address this challenge, we formulate a multiobjective optimization problem under the constraints of UAV and SN power budgets to make a tradeoff between EE and spectral efficiency. Due to the nonconvexity of the formulated problem, we divide the main problem into three subproblems: user association, the number of reflecting elements, and UAV trajectory optimization. An alternating optimization algorithm integrating the genetic algorithm, the CJ-BS-based cyclic iteration algorithm, Dinkelbach’s algorithm, and the successive convex approximation method is proposed to solve these subproblems. Simulation results demonstrate that the proposed solution outperforms the UAV hovering directly above each SN scheme.

To address the non-line-of-sight (NLOS) transmission challenge between wireless sensor nodes (SNs) and a fusion center in smart agriculture, the unmanned aerial vehicle (UAV)-mounted intelligent reflecting surfaces (IRS) is applied to assist data collection in wireless sensor networks in this paper. The fly-hover-communicate protocol (FHCP) is considered, where the UAV visits a set of hovering positions and communicates with one corresponding SN while hovering at each position. The optimization problem of making a trade-off between spectrum and energy efficiency (EE) under the transmission prioritized scheme (TPS) in FHCP is analyzed. For the single-SN case, the problem is decomposed into two sub-problems: UAV trajectory and number of reflecting elements (NoRE) optimization. For the multi-SN case, it is decomposed into three sub-optimization problems: user association, NoRE, and UAV trajectory planning. An efficient alternating optimization algorithm incorporating the genetic algorithm, conditional judgment-binary search algorithm, and successive convex approximation algorithm are applied to tackle this problem. Simulation results indicate that the TPS effectively increases EE compared with that when the UAV hovers directly above each SN.