• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2561-2576
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• 2013

The ship ad-hoc network (SANET) extends the coverage of the high data-rate terrestrial communications to the ships with the reduced cost in maritime communications. Cognitive radio (CR) has the ability of sensing the radio environment and dynamically reconfiguring the operating parameters, which can make SANET utilize the spectrum efficiently. However, due to the dynamic topology nature and no central entity for data fusion in SANET, the interference brought into the primary network caused by the hidden primary user requires to be carefully managed by a sort of decentralized cooperative spectrum sensing schemes. In this paper, we propose a self-weighted decentralized cooperative spectrum sensing (SWDCSS) scheme to solve such a problem. The analytical and simulation results show that the proposed SWDCSS scheme is reliable to detect the primary user in SANET. As a result, secondary network can efficiently utilize the spectrum band of primary network with little interference to primary network. Referring the complementary receiver operating characteristic (ROC) curves, we observe that with a given false alarm probability, our proposed algorithm reduces the missing probability by 27% than the traditional embedded spectrally agile radio protocol for evacuation (ESCAPE) algorithm in the best condition.

• 한국통신학회논문지
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• 제33권1B호
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• pp.8-15
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• 2008

CR(Cognitive Radio)은 우선 사용자 네트워크와 공존함으로써 주파수 효율을 극대화할 수 있는 기술이다. 이러한 CR 기술의 가장 우선해야 될 과제는 우선 사용자 시스템에 대한 보호이다. 이를 위해 CR 네트워크는 주기적으로 우선 사용자의 주파수 사용을 탐지하고 우선 사용자 시스템에 간섭을 주지 않기 위해 통신 파라미터를 조절하게 된다. 하지만 CR 네트워크의 특성상 스펙트럼 검출을 통해서도 발견되지 않는 우선 사용자인 HN(Hidden Node)가 존재하게 된다. 이러한 HN가 사용하는 무선 자원을 CR 네트워크에서는 유휴자원이라 판단함으로써 우선 사용자에게 간섭을 주는 문제가 발생하며, 이는 CR 네트워크의 올바른 운용을 방해하는 요인으로 작용한다. 따라서 CR 네트워크의 안전하고 올바른 운용을 위해서는 HN 문제를 해결할 수 있는 방법이 절실히 요구된다. 본 논문에서는 이러한 HN 문제를 해결할 수 있는 방법을 제시하며, 이에 대한 성능 평가를 수행하였다.

• Journal of Internet Technology
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• 제20권1호
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• pp.301-313
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• 2019

In this paper, we propose a new MAC protocol for Cognitive Radio (CR)-based IoT networks, called MSMA/CA. We extend the standard CSMA/CA, adopted in IEEE 802.11 WLANs, to the CR networks with the minimal modification since it works well in the real world. We resolve the classical hidden/exposed terminal problems by a variant of RTS/CTS mechanism and further, the hidden primary terminal problem by the mutual spectrum sensing at the transmitter and the receiver. We also modify the backoff process of CSMA/CA to incorporate the blocking of secondary transmitters, with the aim of protecting ongoing primary transmissions from aggressive secondary users. We analyze the throughput and delay of our proposed scheme using the Markov chain model on the backoff procedure, and verify its accuracy by simulations. Simulation results demonstrate that our protocol is suitable for IoT networks since the performance is insensitive to the number of users or devices.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.261-270
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• 2008

Wireless Mesh Networks (WMNs) have emerged as one of the new hot topics in wireless communications. WMNs have been suggested for use in situations in which some or all of the users are mobile or are located in inaccessible environments. Unconstrained transmission in a WMN may lead to the time overlap of two or more packet receptions, called collisions or interferences, resulting in damaged useless packets at the destination. There are two types of collisions; primary collision, due to the transmission of the stations which can hear each other, and hidden terminal collision, when stations outside the hearing range of each other transmit to the same receiving stations. For a WMN, direct collisions can be minimized by short propagation and carrier sense times. Thus, in this paper we only consider hidden terminal collision while neglecting direct collisions. To reduce or eliminate hidden terminal collision, code division multiple access (CDMA) protocols have been introduced. The collision-free property is guaranteed by the use of spread spectrum communication techniques and the proper assignment of orthogonal codes. Such codes share the fixed channel capacity allocated to the network in the design stage. Thus, it is very important to minimize the number of codes while achieving a proper transmission quality level in CDMA WMNs. In this paper, an efficient heuristic code assignment algorithm for eliminating hidden terminal collision in CDMA WMNs with general topology.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• 한국통신학회논문지
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• 제37권3B호
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• pp.189-203
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• 2012

CR 네트워크에서 비인가 사용자는 인가 사용자에게 간섭 영향을 주지 않기 위해 주기적인 센싱 결과를 기반으로 유휴 채널에 기회적으로 접근한다. 하지만 비인가 송신기의 간섭 범위 내에 숨겨진 인가 수신기들이 존재할 수 있기 때문에 지역적인 센싱만으로는 인가 사용자들에 대한 완전한 보호를 보장할 수 없게 된다. 또한 유휴 채널이 존재하지 않을 경우 비인가 시스템의 지속적인 서비스 수행이 불가능해진다. 따라서 동일 채널에서의 인가시스템들을 보호함과 동시에 비인가 사용자들의 채널 이용률을 최대화하기 위해서 다양한 네트워크 시나리오를 염두에 두고 적절한 공존 방안을 도출할 필요가 있다. 본 논문에서는 uplink/downlink 스케줄과 사용자 위치 정보를 사용하여 인가 사용자에게 간섭을 주지 않는 비인가 사용자의 공존 조건을 제안한다. 비인가 기지국으로 주어진 스케줄 정보와 인가 및 비인가 사용자의 위치 정보 획득 가능 여부에 따라 네 가지 시나리오로 분류하였고, 각 시나리오 별로 인가 사용자 및 비인가 사용자의 uplink/downlink 스케줄 조합을 고려하여 공존 상황에서의 비인가 사용자의 최대 가능한 처리량을 분석하였다. 컴퓨터 모의실험 결과는 비인가 장치들의 통신 가능성을 향상시키기 위해서 제안된 방식이 실제 무선 인지 시스템에 적용될 수 있음을 다양한 상황 하에서 보여준다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제21권3호
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• pp.21-34
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• 2016

In the present study, we applied various machine learning techniques comparatively for prediction of subsurface structures based on multiple secondary information (i.e., well-logging data). The machine learning techniques employed in this study are Naive Bayes classification (NB), artificial neural network (ANN), support vector machine (SVM) and logistic regression classification (LR). As an alternative model, conventional hidden Markov model (HMM) and modified hidden Markov model (mHMM) are used where additional information of transition probability between primary properties is incorporated in the predictions. In the comparisons, 16 boreholes consisted with four different materials are synthesized, which show directional non-stationarity in upward and downward directions. Futhermore, two types of the secondary information that is statistically related to each material are generated. From the comparative analysis with various case studies, the accuracies of the techniques become degenerated with inclusion of additive errors and small amount of the training data. For HMM predictions, the conventional HMM shows the similar accuracies with the models that does not relies on transition probability. However, the mHMM consistently shows the highest prediction accuracy among the test cases, which can be attributed to the consideration of geological nature in the training of the model.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.529-538
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a riven large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 추계학술대회
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• pp.115-124
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• 2004

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.