A Smart Sensor Data Transmission Technique for Logistics and Intelligent Transportation Systems

When it comes to Internet of Things systems that include both a logistics system and an intelligent transportation system, a smart sensor is one of the key elements to collect useful information whenever and wherever necessary. This study proposes the Smart Sensor Node Group Management Medium Access Control Scheme designed to group smart sensor devices and collect data from them efficiently. The proposed scheme performs grouping of portable sensor devices connected to a system depending on the distance from the sink node and transmits data by setting different buffer thresholds to each group. This method reduces energy consumption of sensor devices located near the sink node and enhances the IoT system’s general energy efficiency. When a sensor device is moved and, thus, becomes unable to transmit data, it is allocated to a new group so that it can continue transmitting data to the sink node

Enhanced 3-D GM-MAC Protocol for Guaranteeing Stability and Energy Efficiency of IoT Mobile Sensor Networks

In wireless sensor networks, energy efficiency is important because sensor nodes have limited energy. 3-dimensional group management medium access control (3-D GM-MAC) is an attractive MAC protocol for application to the Internet of Things (IoT) environment with various sensors. 3-D GM-MAC outperforms the existing MAC schemes in terms of energy efficiency, but has some stability issues. In this paper, methods that improve the stability and transmission performance of 3-D GM-MAC are proposed. A buffer management scheme for sensor nodes is newly proposed. Fixed sensor nodes that have a higher priority than the mobile sensor nodes in determining the group numbers that were added, and an advanced group number management scheme was introduced. The proposed methods were simulated and analyzed. The newly derived buffer threshold had a similar energy efficiency to the original 3-D GM-MAC, but improved performance in the aspects of data loss rate and data collection rate. Data delay was not included in the comparison factors as 3-D GM-MAC targets non-real-time applications. When using fixed sensor nodes, the number of group number resets is reduced by about 43.4% and energy efficiency increased by about 10%. Advanced group number management improved energy efficiency by about 23.4%. In addition, the advanced group number management with periodical group number resets of the entire sensor nodes showed about a 48.9% improvement in energy efficiency

Face Antispoofing Method Using Color Texture Segmentation on FPGA

User authentication for accurate biometric systems is becoming necessary in modern real-world applications. Authentication systems based on biometric identifiers such as faces and fingerprints are being applied in a variety of fields in preference over existing password input methods. Face imaging is the most widely used biometric identifier because the registration and authentication process is noncontact and concise. However, it is comparatively easy to acquire face images using SNS, etc., and there is a problem of forgery via photos and videos. To solve this problem, much research on face spoofing detection has been conducted. In this paper, we propose a method for face spoofing detection based on convolution neural networks using the color and texture information of face images. The color-texture information combined with luminance and color difference channels is analyzed using a local binary pattern descriptor. Color-texture information is analyzed using the Cb, S, and V bands in the color spaces. The CASIA-FASD dataset was used to verify the proposed scheme. The proposed scheme showed better performance than state-of-the-art methods developed in previous studies. Considering the AI FPGA board, the performance of existing methods was evaluated and compared with the method proposed herein. Based on these results, it was confirmed that the proposed method can be effectively implemented in edge environments

An Intelligent Real-Time Traffic Control Based on Mobile Edge Computing for Individual Private Environment

The existence of Mobile Edge Computing (MEC) provides a novel and great opportunity to enhance user quality of service (QoS) by enabling local communication. The 5th generation (5G) communication is consisting of massive connectivity at the Radio Access Network (RAN), where the tremendous user traffic will be generated and sent to fronthaul and backhaul gateways, respectively. Since fronthaul and backhaul gateways are commonly installed by using optical networks, the bottleneck network will occur when the incoming traffic exceeds the capacity of the gateways. To meet the requirement of real-time communication in terms of ultralow latency (ULL), these aforementioned issues have to be solved. In this paper, we proposed an intelligent real-time traffic control based on MEC to handle user traffic at both gateways. The method sliced the user traffic into four communication classes, including conversation, streaming, interactive, and background communication. And MEC server has been integrated into the gateway for caching the sliced traffic. Subsequently, the MEC server can handle each user traffic slice based on its QoS requirements. The evaluation results showed that the proposed scheme enhances the QoS and can outperform on the conventional approach in terms of delays, jitters, and throughputs. Based on the simulated results, the proposed scheme is suitable for improving time-sensitive communication including IoT sensor’s data. The simulation results are validated through computer software simulation

Energy Efficient MAC Scheme for Wireless Sensor Networks with High-Dimensional Data Aggregate

This paper presents a novel and sustainable medium access control (MAC) scheme for wireless sensor network (WSN) systems that process high-dimensional aggregated data. Based on a preamble signal and buffer threshold analysis, it maximizes the energy efficiency of the wireless sensor devices which have limited energy resources. The proposed group management MAC (GM-MAC) approach not only sets the buffer threshold value of a sensor device to be reciprocal to the preamble signal but also sets a transmittable group value to each sensor device by using the preamble signal of the sink node. The primary difference between the previous and the proposed approach is that existing state-of-the-art schemes use duty cycle and sleep mode to save energy consumption of individual sensor devices, whereas the proposed scheme employs the group management MAC scheme for sensor devices to maximize the overall energy efficiency of the whole WSN systems by minimizing the energy consumption of sensor devices located near the sink node. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of active time of sensor devices, transmission delay, control overhead, and energy consumption. Therefore, the proposed scheme is suitable for sensor devices in a variety of wireless sensor networking environments with high-dimensional data aggregate