• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9A
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• pp.1290-1298
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• 2000

Turbo Code decoder is an iterate decoding technology, which extracts extrinsic information from the bit to be decoded by calculating both forward and backward metrics, and uses the information to the next decoding step Turbo Code shows excellent performance, approaching Shannon Limit at the view of BER, when the size of Interleaver is big and iterate decoding is run enough. But it has the problems which are increased complexity and delay and difficulty of real-time processing due to Interleaver and iterate decoding. In this paper, it is analyzed that MAP(maximum a posteriori) algorithm which is used as one of Turbo Code decoding, and the factor which determines its performance. MAP algorithm proceeds iterate decoding by determining soft decision value through the environment and transition probability between all adjacent bits and received symbols. Therefore, to improve the performance of MAP algorithm, the trust between adjacent received symbols must be ensured. However, MAP algorithm itself, can not do any action for ensuring so the conclusion is that it is needed more algorithm, so to decrease iterate decoding. Consequently, MAP algorithm and Turbo Code performance are analyzed in the nongaussian channel applying Robust equalization technique in order to input more trusted information into MAP algorithm for the received symbols.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.11
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• pp.846-850
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• 2009

Recently, wireless sensor networks have found their way into a wide variety of applications and systems with vastly varying requirements and characteristics such as environmental monitoring, smart spaces, medical applications, and precision agriculture. The sensor nodes are battery powered. Therefore, the energy is the most precious resource of a wireless sensor network since periodically replacing the battery of the nodes in large scale deployments is infeasible. Energy efficient mechanisms for gathering sensor readings are indispensable to prolong the lifetime of a sensor network as long as possible. There are two energy-efficient approaches to prolong the network lifetime in sensor networks. One is the compression scheme to reduce the size of sensor readings. When the communication conflict is occurred between two sensor nodes, the sender must try to retransmit its reading. The other is the MAC protocol to prevent the communication conflict. In this paper, we propose a novel approaches to reduce the size of the sensor readings in the MAC layer. The proposed scheme compresses sensor readings by allocating the time slots of the TDMA schedule to them dynamically. We also present a mathematical model to predict latency from collecting the sensor readings as the compression ratio is changed. In the simulation result, our proposed scheme reduces the communication cost by about 52% over the existing scheme.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.731-751
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• 2021

The Internet of Things is a huge group of devices communicating each other and the interconnection of objects in the network is a basic requirement. Choosing a reliable device is critical because malicious devices can compromise networks and services. However, it is difficult to create a trust management model due to the mobility and resource constraints of IoT devices. For the centralized approach, there are issues of single point of failure and resource expansion and for the distributed approach, it allows to expand network without additional equipment by interconnecting each other, but it has limitations in data exchange and storage with limited resources and is difficult to ensure consistency. Recently, trust management models using fog nodes and blockchain have been proposed. However, blockchain has problems of low throughput and delay. Therefore, in this paper, a trust management model for selecting reliable devices in a fog-based IoT environment is proposed by applying IOTA, a blockchain technology for the Internet of Things. In this model, Directed Acyclic Graph-based ledger structure manages trust data without falsification and improves the low throughput and scalability problems of blockchain.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.133-137
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• 2018

Arduino is a small microcomputer that is used in a variety of industry fields and especially is widely used as an open source hardware IoT device. The multi-tasking method of Arduino is divided into super loop timing and RTOS thread method. The super loop timing method is simple and easy to understand. However, when one task is long, it affects the execution of the next task. In addition, RTOS threading has the advantage of being able to run without being influenced by other work time. However, Arduino, a small microcomputer, has a disadvantage in that, when the number of threads increases, the context switching time of the thread causes additional time not included in the super loop timing method have. In this paper, we use Arduino Uno R3 and FreeRTOS to analyze these different features, and the task for the experiment is to send 8000 digital signals to the built-in LED port. If two tasks of the same size are executed, the super loop method executes 3 ms faster than FreeRTOS multitasking. If multiple tasks are executed simultaneously, superloop type task is sequential execution and difference in execution time between first task and last task is large. FreeRTOS method can be executed concurrently, but execution time delay of about 30 ms occurs in context switching time.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.277-284
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• 2021

Recently, the smart home environment is expected to be a platform that collects, integrates, and utilizes various data through convergence with wireless information and communication technology. In fact, the number of smart devices with various sensors is increasing inside smart homes. The amount of data that needs to be processed by the increased number of smart devices is also increasing, and big data processing systems are actively being introduced to handle it effectively. However, traditional big data processing systems have all requests directed to cluster drivers before they are allocated to distributed nodes, leading to reduced cluster-wide performance sharing as cluster drivers managing segmentation tasks become bottlenecks. In particular, there is a greater delay rate on smart home devices that constantly request small data processing. Thus, in this paper, we design a Apriori-based big data system for effective data processing in smart home environments where frequent requests occur at the same time. According to the performance evaluation results of the proposed system, the data processing time was reduced by up to 38.6% from at least 19.2% compared to the existing system. The reason for this result is related to the type of data being measured. Because the amount of data collected in a smart home environment is large, the use of cache servers plays a major role in data processing, and association analysis with Apriori algorithms stores highly relevant sensor data in the cache.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.525-532
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• 2021

In the ocean, it is difficult to separate the effects of one cause due to the multiple causes, but the self-organizing map can be analyzed by adding other factors to the cluster result. Therefore, in this study, the results of the clustering of sea level data were applied to sea surface temperature. Sea level data was clustered into a total of 6 nodes. The difference between sea surface temperature and sea level height has a one-month delay, which applied sea surface temperature data a month ago to the clustered results. As a result of comparing the mean of sea surface temperature of 140 to 150°E, where the sea surface temperature was variously distributed, in the case of nodes 1, 3, and 5, it was possible to find a meandering sea surface temperature distribution that is clearly distinguished from the sea level data. While nodes 2, 4 and 6, the sea surface temperature distribution was smooth. In this study, sea surface temperature data were applied to the clustered results of sea level data, but later it is necessary to apply wind or geostrophic velocity data to compare.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.4
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• pp.81-93
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• 2000

In a methodological approach to improve the processing performance of modulo exponentiation which is the primary arithmetic in RSA crypto algorithm, we present a new RSA hardware architecture based on high-radix modulo multiplication and CRT(Chinese Remainder Theorem). By implementing the modulo multiplier using radix-16 arithmetic, we reduced the number of PE(Processing Element)s by quarter comparing to the binary arithmetic scheme. This leads to having the number of clock cycles and the delay of pipelining flip-flops be reduced by quarter respectively. Because the receiver knows p and q, factors of N, it is possible to apply the CRT to the decryption process. To use CRT, we made two s/2-bit multipliers operating in parallel at decryption, which accomplished 4 times faster performance than when not using the CRT. In encryption phase, the two s/2-bit multipliers can be connected to make a s-bit linear multiplier for the s-bit arithmetic operation. We limited the encryption exponent size up to 17-bit to maintain high speed, We implemented a linear array modulo multiplier by projecting horizontally the DG of Montgomery algorithm. The H/W proposed here performs encryption with 15Mbps bit-rate and decryption with 1.22Mbps, when estimated with reference to Samsung 0.5um CMOS Standard Cell Library, which is the fastest among the publications at present.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.149-156
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• 2021

OTT service, which allows video content to be viewed as a streaming service on the Internet network, has recently attracted a lot of attention, and the number of users is also increasing rapidly. It would be a natural strategy for OTT companies to acquire more content to gain a competitive advantage in relations with traditional media companies and other OTT companies. However, there are research results to show that the investment in facilities by Internet service providers who must transport the increasing Internet traffic from OTT provider to end users should increase as the amount of Internet traffic originated by OTT services also increases. This study empirically analyzed how content investment by Netflix, a leading OTT company, affects its revenue growth and network investment by Internet service providers through a polynomial distributed lag model. And the analysis results show that Netflix's content investment contributes to the company's increase in revenue, and also has an effect on the increase in network investment by Internet service providers. This result confirms that OTT operators' content acquisition strategy is a valid management strategy, and empirically supports the study results that OTT operators need to share the cost of Internet network facility investment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.193-200
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• 2022

In this study, the finite-failure NHPP software reliability model was applied to the software development model based on the Weibull lifetime distribution (Goel-Okumoto, Rayleigh, Type-2 Gumbe), which is widely used in the software reliability field, and then the cost attributes were compared and evaluated. For this study, failure time data detected during normal operation of the software system were collected and used, the most-likelihood estimation (MLE) method was applied to the parameter estimation of the proposed model, and the calculation of the nonlinear equation was solved using the binary method. As a result, first, in the software development model, when the cost of testing per unit time and the cost of removing a single defect increased, the cost increased but the release time did not change, and when the cost of repairing failures detected during normal system operation increased, the cost increased and the release time was also delayed. Second, as a result of comprehensive comparative analysis of the proposed models, it was found that the Type-2 Gumble model was the most efficient model because the development cost was lower and the release time point was relatively faster than the Rayleigh model and the Goel-Okumoto basic model. Third, through this study, the development cost properties of the Weibull distribution model were newly evaluated, and the analyzed data is expected to be utilized as design data that enables software developers to explore the attributes of development cost and release time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.317-321
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• 2022

In response to Incheon tap water accident in 2019, the Ministry of Environment has created the "Comprehensive Measures for Water Safety Management" to improve water operation management, provide systematic technical support, and respond to accidents. Accordingly, K-water is making a smart water supply management system for the entire process of tap water. In order to advance the response to water accidents, it is essential to secure the reliability of real-time water operation data such as flow rate, pressure, and water level, and to develop and apply a warning algorithm in advance using big data analysis techniques. In this paper, various statistical techniques are applied using water supply operation data (flow, pressure, water level, etc) to prepare the foundation for the selection of the optimal operating range and advancement of the monitoring and alarm system. In addition, the arrival time is analyzed through cross-correlation analysis of changes in raw water turbidity between the water intake and water treatment plants. The purpose of this paper is to study the model that predicts the raw water turbidity of a water treatment plant by applying raw water turbidity data considering the time delay according to the flow rate change.