Online transactions play an increasingly important role in our daily lives. Recently, online shopping has greatly expanded not only in small and medium enterprises, but also among individual internet users who use social media as online trading platforms. While there are several online-shopping platforms in Saudi Arabia, they are still facing critical obstacles that challenge customers, businessmen, and organizations. In this paper, we present a smart control unit that could help addressing current challenges that face e-commerce and suggesting contemporary governmental legislation dedicated at governing and simplifying online transactions in order to make them more reliable, faster, secure, and competitive.

This paper deals with the experimental characterization of thin tunable absorber developed based on artificial magnetic conductor (AMC). To produce the tunable frequency response, the varactor diodes with varied reverse DC bias voltage are incorporated into the AMC structure of proposed absorber. By using an equivalent circuit of varactor diode, the performance of AMC-based thin tunable absorber is characterized through a simulation software. To improve the absorptivity, a resistor with the value of 470Ω is also incorporated into the AMC structure in parallel to each varactor diode. A 1.6mm thick FR4 epoxy dielectric substrate with the dimension of 104mm × 104mm is used for deploying the design as well as for the realization. The experimental characterization which is performed using a horn antenna shows that the realized absorber has a tunable frequency response from the frequency of 2.26GHz with the reflection coefficient (S 11 ) value of -21.5dB at the reverse DC bias voltage of 0V to the frequency of 3.01GHz with the S 11 value of -19.75dB at the reverse DC bias voltage of 10V. This performance is comparable to the simulation result for the proposed absorber with the varactor diode and 470Ω resistor incorporation.

MEC and IoT are rapidly expanding technologies that offer numerous opportunities to enhance efficiency and application performance. However, the huge volume of data generated by IoT devices, coupled with computational and latency constraints, poses data processing challenges. To address this within the MEC architecture, deploying computing servers at the network edge near IoT devices is a promising approach. This reduces latency and traffic load on the core network while improving the user experience. However, offloading computations task from IoT devices to MEC servers and efficiently allocating computing resources is a complex problem. IoT tasks may have specific requirements in terms of latency, bandwidth and energy efficiency, while computing resources and capacities maybe limited or shared between several users. We propose an approach called FedMeta2Ag, which we evaluate using the MNIST database. With 20 epochs, the training accuracy reached 91.5%, while the test accuracy achieved 92.0%. Performance consistently improved during the initial 20 iterations and gradually stabilized thereafter. Additionally, we compared the performance of our proposed model with existing methods, finding that our approach outperforms existing models in predicting performance more accurately. Thus, this approach effectively meets the demanding performance requirements of wireless communication systems.

The objective of this study is to develop a Bluetooth-based low-cost wearable device for a self-quarantine monitoring system. The designed wearable device focuses on data transmission via Bluetooth, integration of tracking, tracing, and fencing into a single system, and low energy usage from its battery. We design a wearable device using smartphone equipped with GPS, a communication module, Bluetooth low energy (BLE) and a high-capacity battery as a solution for low-cost device with excellent efficiency. We divide the designed system into two parts, the client and the server parts. The client parts are wearable device attached to the individual being monitored and the mobile phone as GPS and telecommunications module. Whereas the server parts are user interface, digital map, notification system, and backend database. Then, the whole system was tested in laboratory and field scale. We tested functions of integrated device such as wearable device, mobile applications, and server for laboratory scale test. Then, performing field test with geofencing, communication module, battery, web interface, and resource computing usage. The field test was conducted on a small scale with a limited number of trial patients. We found that the designed wearable device was successfully implemented for both self-quarantine and centralized quarantine requirements. The majority of the components used met the specifications and functioned properly as well. A BLE-enabled wearable device can be used for tracking self-quarantine patients. The laboratory and field scale tests demonstrate that the designed wearable device functions properly and meets the requirements. We anticipate that this low-cost wearable device is effective in limiting Covid-19 virus spread and preventing the formation of a new Covid-19 virus-infected cluster.

MEC and the Internet of Things (IoT) are two areas rapidly expanding technologies that offer many opportunities to improve efficiency and application performance. However, the huge amount of data generated by IoT devices and the processing and latency constraints imposed by these technologies and mobile networks make processing this data a major challenge. As part of MEC architecture, a promising approach to solving this challenge is to deploy computing servers at the edge of the network, close to IoT devices. This makes it possible to reduce latency and traffic load on the core network, while offering a better user experience. However, offloading tasks from IoT devices to the MEC servers and efficiently allocating the available computing resources is a complex problem. IoT tasks can have latency, bandwidth, and speed requirements, resources, and available computational resources may be limited or shared between multiple users. To solve this problem, we propose an approach based on federated meta-learning. In our approach, each device IoT collects information about the tasks to be performed and the local resources available, then securely shares them with a local MEC server. The local server uses these information to train a meta-learning model that can predict the best task offload and resource allocation decisions for each task. The advantage part of our approach is that it allow us to learn from the experiences of all IoT devices, resulting in more robust and accurate models. We evaluated our FedMeta2Ag algorithm using the MNIST database (Modified National Institute of Standards and Technology database). Considering 20 epochs, the accuracy during training is 91.5% against 92.0% obtained with the test data. In addition, performance continues to increase during the first 20 iterations and gradually becomes stable. Moreover, the accuracy evaluation involves the comparison of the proposed approach with existing methods. This comparison reveals that our approach predict the performance with more accuracy than the existing models. This approach can effectively fulfil the demanding performance of wireless communications systems.

As the wireless research community moves toward shaping the vision of sixth-generation (6G) networks, reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for controlling the propagation environment. Although RIS has not yet been standardized, its versatile applications and enabling capabilities have attracted growing attention in both academia and industry. This survey presents a comprehensive review of RIS technology spanning theoretical foundations, design aspects, and practical deployment considerations. In contrast to existing surveys that focus on isolated aspects, this work offers an integrated view covering use cases, control mechanisms, channel sounding methodologies, and channel estimation strategies. Each of these topics is reviewed through the lens of recent literature, synthesizing the latest advancements to provide updated insights for both academic researchers and industry practitioners. It further addresses emerging topics such as standardization activities and industrial perspectives, which are often overlooked in prior literature. By bridging theoretical insights with practical challenges, this survey aims to provide a holistic understanding of RIS and support its evolution from a research concept toward real-world implementation.