The next-generation IEEE 802.11be network introduces advanced features such as Multi-Link Operation (MLO) and Enhanced Orthogonal Frequency Division Multiple Access (Enhanced OFDMA), along with a new Multiple Resource Unit (MRU) mechanism that improves spectrum efficiency for multiple users under strict latency constraints. This paper proposes a QoS-aware downlink scheduling mechanism, which jointly considers the MRU type-index mapping and real-time channel conditions to optimize resource allocation in environments with heterogeneous device coexistence. Unlike existing approaches, which overlook the feasibility of MRU allocation under IEEE 802.11be constraints, the proposed scheme achieves significant improvements in total network throughput and spectrum utilization, as demonstrated through simulation. These results highlight the proposed scheduling approach as a practical and scalable solution for next-generation low-latency wireless communication.

IEEE 802.11ax adopts orthogonal frequency division multiple access (OFDMA) technology in the physical layer to address the efficiency degradation problem as the device density increases. OFDMA allows multiple devices to transmit or receive the data from an access point (AP) at the same time by sharing available bandwidth. However, the success rate of random access is highly dependent on the number of devices. Transmission collisions will cause devices with high-priority data to experience higher transmission delays. In this paper, we design a multi-queue priority scheduler with timeslot reservation, which improves the efficiency of random access. Devices with high-priority data have a higher transmission probability. To achieve the expected access proportion, the scheduler can change the average access times of different priority devices based on the access rules and the number of queues. Thus, the proposed mechanism can not only meet the delay requirements of high-priority data but also avoid the starvation problem of low-priority data. Mathematics and simulations show that our mechanism has better throughput and delay performance in IEEE 802.11ax networks.

The use of Bluetooth Low Energy (BLE) beacons enhanced further for connectionless services such as location-specific information and navigation. With BLE 5.0, the way beacons deliver more information can move away from the current model of app-paired-to-device to a connectionless IoT. When users pass areas with installed BLE beacons, their mobile devices can receive specials, promotional contents, and discount coupons. However, the beacons are only broadcasting. This means that beacons are not able to collect information from the users with mobile devices. In this paper, we propose responsive advertising with the wait-slot scheme (RAWS) based on advertising extensions to improve the performance of the discovery process before further communications in BLE networks. Besides, we consider the energy consumption and investigate the latency-energy tradeoff with our proposed scheme. The theoretical lower bounds for both latency and energy of the discovery process as well as their tradeoff are presented. In addition, we formulate the state transition diagram for analyzing the performance of our proposed scheme. Simulation results validated by analysis results show that RAWS indeed improves the latency and the energy consumption of the discovery process in BLE networks.

Recently, application services have become more and more diversified as new technology advances. The applications of Augmented Reality (AR) have received great attention from academics and operators. However, the complex computing required for AR applications has caused service operators to face the problems of poor user experience due to insufficient computing resources. Network function virtualization (NFV) enables operators to directly establish network functions on virtual machines and can flexibly adjust the number of each virtual network function (VNF) according to users' requirements. Thus, the system can reduce unnecessary waste of resources. In addition, we use multi-access edge computing (MEC) to provide computing resources. The edge server is close to users and reduces transmission cost of the system. This paper proposes an MEC-based VNF placement and scheduling scheme for AR applications. Considering the functional topology of AR applications, the compute-intensive application can provide good service quality for a large number of users within limited resources. The simulation shows that the proposed method can maintain good service quality as the number of user requests increases.

As cloud computing services have gained popularity, users view videos on websites (e.g., YouTube) to generate high CPU resource utilization and bandwidth for video streaming data centers. However, popular videos result in power-law features to cause imbalanced resource utilization. In addition, hotspot and idle servers generate extra power consumption in data centers. Previous studies considered to satisfy the requirements of users, provide faster access rates and save power consumption. However, fewer studies considered resource utilization with different popularity videos. Therefore, this paper proposes an energy efficient virtual machine (VM) management scheme with power-law features (VMPL). VMPL predicts the resource utilization of the video in the future based on the popularity, ensures enough resources for upcoming videos, and turns off idle servers for power saving. Simulation results validated by mathematical analysis show that VMPL has the best resource utilization and the lowest power consumption compared with Nash and Best-Fit algorithms.

Bluetooth Low Energy (BLE) Beacon technology is well on the way to becoming the future of business due to its inexpensive and low-power properties. All communications in BLE networks must involve neighbor discovery process (NDP) in the first place since a BLE device needs to create a connection or exchange information with its neighbors. Thus, the performance of the discovery latency is a challenging issue to be addressed for integrating BLE into the Beacon application development as the number of BLE devices increases. In this paper, we propose a twoway communication with wait-slot scheme (TCWS) to minimize the probability of collision occurring on the response frames of BLE devices and improve the latency of NDP. We formulate the state transition diagram for analyzing the performance of our proposed scheme. The results show that TCWS provides much better performance in terms of the probability of collision and the discovery latency in dense BLE networks.

The present study was to explore undergraduate students' performance and eye movement between two groups (non-science major and science major) on online scientific literacy assessments. The domain of scientific literacy in assessments consisted of identifying scientific questions, explaining phenomena scientifically, and using evidence scientifically. Eye-tracking system was synchronized to collect the indicator data of eye movement while students were engaging in cognitive processing during the assessments. The results showed that the science group students outperformed significantly than their peers in the non-science group on the assessments. Eye movement data further supported that the science group students allocated greater attention and made deeper cognitive processing at area of interest (AOI) on critical web pages compared to the non-science group ones. This study provides empirical evidence that the eye movement behaviors can give an insight into the information processing during scientific literacy online assessments.

In this paper, we propose a learning-based system for generating exaggerative caricatures with expression. Most of the previous works can only deal with frontal face images with neutral expression without glasses or hats, and are unable to apply more than one drawing prototype which was learned from the caricatures drawn by one single cartoonist at a time. The proposed caricature generation system exaggerates face images with expressions and learns the drawing prototypes from training data as well. Experimental results show that our system can capture the features selected by the artist and exaggerate them in similar ways.