Performance Analysis of Signaling Delay for Wireless Cellular Networks

In wireless cellular networks, signaling traffic such as location update, paging and handoff due to the user\u27s mobility takes a considerable portion of the total traffic load. In addition, the maximum allowable delays may be different among the signaling packets. In this paper, we present an analytical model for evaluating a total processing delay of signaling packets of wireless cellular networks, which integrates the delays of the radio channel and the processing delay at the wired portion. Through numerical examples, we show that priority processing is effective for reducing the handoff processing delays. We also evaluate the delay difference among cells according to their position within the location area, and the influence of number of terminals upon the processing delays

Offloading and Transmission Strategies for IoT Edge Devices and Networks

We present a machine and deep learning method to offload trained deep learning model and transmit packets efficiently on resource-constrained internet of things (IoT) edge devices and networks. Recently, the types of IoT devices have become diverse and the volume of data has been increasing, such as images, voice, and time-series sensory signals generated by various devices. However, transmitting large amounts of data to a server or cloud becomes expensive owing to limited bandwidth, and leads to latency for time-sensitive operations. Therefore, we propose a novel offloading and transmission policy considering energy-efficiency, execution time, and the number of generated packets for resource-constrained IoT edge devices that run a deep learning model and a reinforcement learning method to find an optimal contention window size for effective channel access using a contention-based medium access control (MAC) protocol. A Reinforcement learning is used to improve the performance of the applied MAC protocol. Our proposed method determines the offload and transmission strategies that are better to directly send fragmented packets of raw data or to send the extracted feature vector or the final output of deep learning networks, considering the operation performance and power consumption of the resource-constrained microprocessor, as well as the power consumption of the radio transceiver and latency for transmitting the all the generated packets. In the performance evaluation, we measured the performance parameters of ARM Cortex-M4 and Cortex-M7 processors for the network simulation. The evaluation results show that our proposed adaptive channel access and learning-based offload and transmission methods outperform conventional role-based channel access schemes. They transmit packets of raw data and are effective for IoT edge devices and network protocols

Enhancing ToF Sensor Precision Using 3D Models and Simulation for Vision Inspection in Industrial Mobile Robots

In recent industrial settings, time-of-flight (ToF) cameras have become essential tools in various applications. These cameras provide high-performance 3D measurements without relying on ambient lighting; however, their performance can degrade due to environmental factors such as temperature, humidity, and distance to the target. This study proposes a novel method to enhance the pixel-level sensing accuracy of ToF cameras by obtaining precise depth data labels in real-world environments. By synchronizing 3D simulations with the actual ToF sensor viewpoints, accurate depth values were acquired and utilized to train AI algorithms, thereby improving ToF depth accuracy. This method was validated in industrial environments such as automobile manufacturing, where the introduction of 3D vision systems improved inspection accuracy compared to traditional 2D systems. Additionally, it was confirmed that ToF depth data can be used to correct positional errors in mobile robot manipulators. Experimental results showed that AI-based preprocessing effectively reduced noise and increased the precision of depth data compared to conventional methods. Consequently, ToF camera performance was enhanced, expanding their potential applications in industrial robotics and automated quality inspection. Future research will focus on developing real-time synchronization technology between ToF sensor data and simulation environments, as well as expanding the AI training dataset to achieve even higher accuracy

Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

We introduce a novel method for indoor localization with the user’s own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all kinds of time-series sensory signal processing. The performance was evaluated on an Android application (app) that exported the trained model and parameters. Our proposed method achieved a distance error of <2.4% and >1.5% on indoor experiments

Vision AI System Development for Improved Productivity in Challenging Industrial Environments: A Sustainable and Efficient Approach

This study presents a development plan for a vision AI system to enhance productivity in industrial environments, where environmental control is challenging, by using AI technology. An image pre-processing algorithm was developed using a mobile robot that can operate in complex environments alongside workers to obtain high-quality learning and inspection images. Additionally, the proposed architecture for sustainable AI system development included cropping the inspection part images to minimize the technology development time, investment costs, and the reuse of images. The algorithm was retrained using mixed learning data to maintain and improve its performance in industrial fields. This AI system development architecture effectively addresses the challenges faced in applying AI technology at industrial sites and was demonstrated through experimentation and application

Auto-Mapping and Configuration Method of IEC 61850 Information Model Based on OPC UA

The open-platform communication (OPC) unified architecture (UA) (IEC62541) is introduced as a key technology for realizing a variety of smart grid (SG) use cases enabling relevant automation and control tasks. The OPC UA can expand interoperability between power systems. The top-level SG management platform needs independent middleware to transparently manage the power information technology (IT) systems, including the IEC 61850. To expand interoperability between the power system for a large number of stakeholders and various standards, this paper focuses on the IEC 61850 for the digital substation. In this paper, we propose the interconnection method to integrate communication with OPC UA and convert OPC UA AddressSpace using system configuration description language (SCL) of IEC 61850. We implemented the mapping process for the verification of the interconnection method. The interconnection method in this paper can expand interoperability between power systems for OPC UA integration for various data structures in the smart grid