• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1596-1602
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• 2021

Battery-operated IoT devices in IoT systems require low power consumption. In general, IoT devices enter a sleep state synchronously to reduce power consumption. A problem arises when an IoT device has to handle asynchronous user requests, as the duty cycle must be reduced to enhance response time. In this paper, we propose a new low-power-consumption scheme, called Repetitive Wake-up scheme for IoT systems of asynchronous environments such as indoor lights control. The proposed scheme can reduce power consumption by sending wake-up signals from the smartphone repetitively and by retaining the IoT device in sleep state to the smallest possible duty cycle. In the various environments with IoT devices at home or office space, we showed that the proposed scheme can reduce power consumption by up to five times compared to the existing synchronous interlocking technique.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.23-28
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• 2023

In a wireless smart utility network communication system, an asynchronous low power MAC is standardized and used according to IEEE 802.15.4e. An asynchronous MAC called RIT (Receiver Initiated Transmission) has a characteristic in which delay time and power consumption are greatly affected by a check-in interval (RIT period). By waking up from sleep every check-in interval and checking whether there is data to be received, power consumption in the receiving end can be drastically reduced, but power consumption in the transmitting end occurs due to an excessive wakeup sequence. If an excessive wake-up sequence is reduced by shortening the check interval, power consumption of the receiving end increases due to too frequent wake-up. In the RIT asynchronous MAC technique, power consumption performance according to traffic load and operation of check-in interval is analyzed and applied to Wi-SUN construction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1224-1248
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• 2022

In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor hub. Decreasing the measurement of information transmission in sensor networks becomes an important issue.Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, a new algorithm called SBEA (Snowball Endurance Algorithm) was proposed and tested with a bow. Thus, you can extend the battery life of your WSN by running effective copy detection. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. The proposed algorithm SBEA (Snowball Endurance Algorithm) is a combination of ERCD protocol and a combined mobility and routing algorithm that can identify the cluster head and adjacent cluster head nodes.This algorithm is used to yield the network life time and the performance of the sensor nodes can be increased.

• Journal of IKEEE
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• v.16 no.2
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• pp.86-94
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• 2012

Synchronization MAC Protocol such as S-MAC and T-MAC utilize duty cycling technique which peroidically operate wake-up and sleep state for reducing energy consumption. But synchronization MAC showed low energy efficiency because of additional control packets. For better energy consumption, Asychronization MAC protocols are suggested. For example, B-MAC, and X-MAC protocol adopt Low Power Listening (LPL) technique with CSMA algorithm. All nodes in these protocols joining a network with independent duty cycle schedules without additional synchronization control packets. For this reason, asynchronous MAC protocol improve energy efficiency. In this study, a low-power MAC protocol which is based on X-MAC protocol for wireless sensor network is proposed for better energy efficiency. For this protocol, we suggest preamble numbering, and virtual-synchronization technique between sender and receive node. Using TelosB mote for evaluate energy efficiency.

• Journal of the Korean Society of School Health
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• v.36 no.3
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• pp.113-125
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• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.