• Proceedings of the Korea Information Processing Society Conference
• /
• 2024.05a
• /
• pp.175-178
• /
• 2024

IoT is becoming more and more popular, along with the massive availability of cheap and easy-to-use IoT devices. One protocol that is often used in IoT devices is the Message Queuing Telemetry Transport (MQTT) protocol. By default, the MQTT protocol does not activate encrypted data security features. This MQTT default feature makes the transmitted and received message data vulnerable to attacks, such as eavesdropping. Therefore, this paper will design and implement encrypted data security using the lightweight cryptography algorithm. The focus of this paper will be on securing MQTT message data at the application layer. We propose a method for encrypting specific MQTT message fields while maintaining compatibility with the protocol's functionalities. The paper then analyzes the timing performance of the MQTT-LEA implementation on the Raspberry Pi 3+. Our findings demonstrate the feasibility of using LEA at the application layer to secure MQTT message communication on resource-constrained devices.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.1
• /
• pp.1-15
• /
• 2022

The MobileNetV3 is specially designed for mobile devices with limited memory and computing power. To reduce the network parameters and improve the network inference speed, a new lightweight network is proposed based on MobileNetV3. Firstly, to reduce the computation of residual blocks, a partial residual structure is designed by dividing the input feature maps into two parts. The designed partial residual structure is used to replace the residual block in MobileNetV3. Secondly, a dual-path feature extraction structure is designed to further reduce the computation of MobileNetV3. Different convolution kernel sizes are used in the two paths to extract feature maps with different sizes. Besides, a transition layer is also designed for fusing features to reduce the influence of the new structure on accuracy. The CIFAR-100 dataset and Image Net dataset are used to test the performance of the proposed partial residual structure. The ResNet based on the proposed partial residual structure has smaller parameters and FLOPs than the original ResNet. The performance of improved MobileNetV3 is tested on CIFAR-10, CIFAR-100 and ImageNet image classification task dataset. Comparing MobileNetV3, GhostNet and MobileNetV2, the improved MobileNetV3 has smaller parameters and FLOPs. Besides, the improved MobileNetV3 is also tested on CPU and Raspberry Pi. It is faster than other networks

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.12
• /
• pp.152-158
• /
• 2016

In this paper, we implemented the full stack platform design with MongoDB database of open source platform Raspberry PI 3 model. We experimented the triggering of event driven with acceleration sensor data logging with wireless communication. we captured the image of USB Camera(MS LifeCam cinema) with 28 frames per second under the Linux version of Raspbian Jessie and extended the functionality of wireless communication function with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the full stack platform for recognizing the event triggering characteristics of detecting the acceleration sensor action and gathering the temperature and humidity sensor data under IoT environment. Especially we used MEAN Stack for developing the performance of full stack platform because the MEAN Stack is more akin to working with MongoDB than what we know of as a database. Afterwards, we would enhance the performance of full stack platform for IoT clouding functionalities and more feasible web design with MongoDB.

• Journal of Apiculture
• /
• v.32 no.3
• /
• pp.191-198
• /
• 2017

Vespa hornets are notorious predators of honeybees in Korean beekeeping. Detection of vespa hornet attacking on honeybee colony was tried through analysis of wing beat frequency profiling from Vespa mandarinia. Wing beat profiles of V. mandarinia during active flight and resting were distinctively different. From the wing beat profiling, algorithm of automated detection of vespa attack was encoded, and alert system was developed using Teensy 3.2 and Raspberry pi 3. From the laboratory testing, the prototype system successfully detected vespa wing beats and delivered the vespa attack information to the user wirelessly. Further development of the system could help precision alert system of the vespa attack to apiary.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.19 no.3
• /
• pp.151-158
• /
• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• Journal of Internet of Things and Convergence
• /
• v.2 no.3
• /
• pp.1-6
• /
• 2016

This paper aims to present the physical computing learning model applicable in teaching the information and communication technology for technology and engineering education. This model is based on the physical computing and deals with the information creation and information transfer in one framework, thus provides students with the total understanding and practice opportunity about information and communication. The proposed learning models are classified into the client-server based model and the web based model. In the implemented learning model, the acquirement and control of information is performed by sketch on Arduino and the communication of information is performed by the Python socket on Raspberry Pi well known as an education platform. Our proposed learning model can be used for teaching students to understand the concept of Internet of Things (IoT), which provides us with world wide control and communication of information.

• Journal of Internet of Things and Convergence
• /
• v.2 no.3
• /
• pp.7-15
• /
• 2016

These days, a lot of research has been in progress for a home automation system. In this study, the intelligent home automation system is developed for automatically controlling the temperature and illumination of the house. The difference with other research can be adjusted individually according to your taste and lifestyle of each individual, it is that it provides a more comfortable living environment through the accumulated lifestyle database. In addition, proposed system can use the mobile app to easily control the lighting devices, a electric fans, and heaters. Moreover the mobile app of proposed system offers function that allows you to search for residential environments statistics on their lifestyle.

• The Journal of Korean Association of Computer Education
• /
• v.17 no.6
• /
• pp.9-20
• /
• 2014

With the change of global awareness of Computing education and introspection about Computer education focused on ICT literacy, efforts are being made to reflect computational thinking in the new curriculum. But if computational thinking would be possible at school, it require tremendous cost to prepare computers for school. In this study, we investigate potentiality and constraints of the one board computer to teach computational thinking in school. We study fundamental performance, application of physical computing and programming education, maintenance of the computers, power consumption of the one board computers which is raspberry pi, beagle bone black, and pcduino3. The result of the study show that one board computer can substitute desktop of the school unless tasks related to require massive data storage and processing. We draw a conclusion that Pcduino3 is well-suited for computational thinking education.

• The Journal of the Convergence on Culture Technology
• /
• v.4 no.1
• /
• pp.291-297
• /
• 2018

In this paper, we present an automatic image object tracking system for Studio cameras on the stage. For object tracking, we use the OpenCV-based Python program using PC, Raspberry Pi 3 and mobile devices. There are many methods of image object tracking such as mean-shift, CAMshift (Continuously Adaptive Mean shift), background modelling using GMM(Gaussian mixture model), template based detection using SURF(Speeded up robust features), CMT(Consensus-based Matching and Tracking) and TLD methods. CAMshift algorithm is very efficient for real-time tracking because of its fast and robust performance. However, in this paper, we implement an image object tracking system for studio cameras based CMT algorithm. This is an optimal image tracking method because of combination of static and adaptive correspondences. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the stage in real time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.10
• /
• pp.1247-1255
• /
• 2016

The safety and autonomous flight function of micro UAV or drones is crucial to its commercial application. The requirement of own building stable drones is still a non-trivial obstacle for researchers that want to focus on the intelligence function, such vision and navigation algorithm. The paper present a GCS using commercial drone and hardware platforms, and open source software. The system follows modular architecture and now composed of the communication, UI, image processing. Especially, lane-keeping algorithm. are designed and verified through testing at a sports stadium. The designed lane-keeping algorithm estimates drone position and heading in the lane using Hough transform for line detection, RANSAC-vanishing point algorithm for selecting the desired lines, and tracking algorithm for stability of lines. The flight of drone is controlled by 'forward', 'stop', 'clock-rotate', and 'counter-clock rotate' commands. The present implemented system can fly straight and mild curve lane at 2-3 m/s.