• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.1
• /
• pp.83-97
• /
• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.3
• /
• pp.137-147
• /
• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.2B
• /
• pp.36-43
• /
• 2005

This paper suggests a method to effectively apply an application model of fuzzy-neural network to the optimal load distribution algorithm, considering the complication and non-linearity of the web server environment. We use the clustering web server in the linux system and it consists of a load balancer that distributes the network loads and some of real servers that processes the load and responses to the client. The previous works considered only with the scrappy decision information such as the connections. That is, since the distribution algorithm depends on the input of the whole network throughput, it was proved inefficient in terms of performance improvement of the web server. With the proposed algorithm, it monitors the whole states of both network input and output. Then, it infers CPU and memory states of each real server and effectively distributes the requests of the clients. In this paper, the proposed model is compared with the previous method through simulations and we analysis the results to develop the optimal and intelligent load balancing model.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.11A
• /
• pp.903-911
• /
• 2011

In this paper we proposed an energy adaptive data aggregation and transmission mechanism to solve the problem of energy limitation in wireless sensor networks (WSNs). Hierarchical structure methods are wildly used in WSNs to improve the energy efficiency. LEACH and TEEN protocols are the typical techniques. In these methods, all nodes, including nodes who have sensed data to transmit and nodes who haven't, are set frame timeslots in every round. MNs (member nodes) without sensed data keep active all the time, too. These strategies caused energy waste. Furthermore, if data collection in MNs is same to the previous transmission, it increases energy consumption. Most hierarchical structure protocols are developed based on LEACH. To solve the above problems, this paper proposed a method in which only MNs with sensed data can obtain allocated frame to transmit data. Moreover, if the MNs have same sensed data with previous, MNs turn to sleep mode. By this way redundant data transmission is avoided and aggregation in CH is lightened, too.

• Journal of Satellite, Information and Communications
• /
• v.10 no.1
• /
• pp.56-61
• /
• 2015

In this paper, the 3D positioning of IoT sensors with the Unity engine of android platform based home-hub was proposde for IoT(Internet of Things) users. Especially, the monitoring of IoT sensor and battery status was designed with the clustering of IoT sensor's position. The 3D positioning of RSSI(received signal strength indicator) and angle for new IoT sensor according to clustering method was described with the cooperation of beacon and received arrival signal time. This unity engine based smart hub platform can monitor the working situation of IoT sensors, and apply 3D video with texture for the life-cycling of many IoT sensors simultaneously. rs was described with RSSI(received signal strength indicator) and received angle.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.47 no.5
• /
• pp.9-17
• /
• 2010

In this paper we propose an improved hierarchical routing protocol for wireless hybrid mesh network. The proposed method efficiently manages network topology and reduces overhead traffic for setting routing path by considering link stability. The simulation results show that the proposed method outperforms the HOLSR (hierarchical optimized link state routing) method in aggregate goodput, packet delivery ratio, and end-to-end delay.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.10
• /
• pp.52-58
• /
• 2007

This paper presents the wind-power model for generation adequacy assessment. Both wind-power and system load depend on time of a year and show their periodic nature with similar periods. Therefore, the two quantities have some probabilistic relations, and if one of them is given, the other can be decided with some probability. In this paper, the two quantities are quantized by k-means clustering algorithm and related probabilities among the cluster centers are calculated using sequential wind-power and system load data. The proposed model is highly expected to be applied for generation adequacy assessment by Monte-Carlo simulation with state sampling method.

• Journal of Digital Convergence
• /
• v.14 no.9
• /
• pp.309-315
• /
• 2016

The node mobility in the wireless mobile network environment increases the energy consumption. This paper proposes a CNMA (cluster-based node management algorithm) to reduce the energy consumption caused by node mobility, and to prolong the life cycle for cluster member nodes. The proposed CNMA traces the mobility for nodes between cluster header and member, and it analyses the energy capacity as monitoring periodically their relationship. So, it makes a division and merges by analysing the state transition for nodes. This paper is to reduce the energy consumption due to the node mobility. The simulation results show that the proposed CNMA can efficiently control the energy consumption caused by mobility, and it can improve the energy cycle.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.96-98
• /
• 2014

Wireless Sensor Network(WSN) is a wireless real-time information(Acquired from the sensor nodes that have the computing power and wireless communication capabilities.) collected, and to take advantage of processing techniques. Currently it is very diverse, such as environmental monitoring, health care, security, smart home, smart grid applications is that. Thus it is required in the wireless sensor network, the algorithm for the efficient use of the limited energy capacity. Suggested by the algorithm for selecting a cluster head node for a hybrid type and clustered, by comparing the amount of energy remaining and a connection between the nodes In this paper, we aim to increase efficiency and accuracy of the wireless sensor network.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.127-136
• /
• 2024

Because the hazardous working environments and high labor intensity of the root industry can potentially impact the health of workers, current regulations have focused on measuring and controlling environmental factors, on a semi-annual basis. However, there is a lack of quantitative criteria addressing workers' health conditions other than the physical work environment. This gap makes it challenging to prevent occupational diseases resulting from continuous exposure to harmful substances below regulatory thresholds. Therefore, this paper proposes a machine learning-based method for determining the peak level of risk in root industry work environments and enables real-time safety assessment in workplaces utilizing this approach.