• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.62-65
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• 2018

This paper comes out with the study on sensing data collection strategy in a Software-Defined Mobile Edge vehicular networking. The two cooperative data dissemination are Direct Vehicular cloud mode and edge cell trajectory prediction decision mode. In direct vehicular cloud, the vehicle observe its neighboring vehicles and sets up vehicular cloud for cooperative sensing data collection, the data collection output can be transmitted from vehicles participating in the cooperative sensing data collection computation to the vehicle on which the sensing data collection request originate through V2V communication. The vehicle on which computation originate will reassemble the computation out-put and send to the closest RSU. The SDMEVN (Software Defined Mobile Edge Vehicular Network) Controller determines how much effort the sensing data collection request requires and calculates the number of RSUs required to support coverage of one RSU to the other. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed solution.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.313-317
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• 2008

유비쿼터스 환경에 대한 관심이 증폭됨에 따라, 센서는 다양한 어플리케이션들에서 점점 더 많이 사용되고 있다. 이러한 센서 시스템에서, 최소 개의 센서를 가지고 대상 공간이 복수 개의 센서에 의해 완전히 센싱되게 하기 위해서는, 센서를 어디에 배치하느냐가 중요한 문제이다. 또한 복수 개의 센서에 의해 센싱된 데이터로부터 의미 있는 정보를 추출하기 위해서는 센서 서로 간의 거리가 너무 가까워서는 안 된다. (최소거리 요건). 이를 위하여 우리는 TRE-based approach 라고 하는, 최소거리 요건을 만족하며 3-coverage 문제를 해결하는 방법을 제안하며, 이를 기반으로 3-coverage 문제를 3 차원으로 확장시킬 때 가능한 센싱 coverage 모델과 그 확장 가능성에 대해 논의한다.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.334-336
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• 2003

Satellite and airborne hyperspectral sensor images are suitable for investigating the vegetation state in agricultural land. However, image data obtained by an optical sensor inevitably includes mixels caused by high altitude observation. Therefore, mixel analysis method, which estimates both the pure spectra and the coverage of endmembers simultaneously, is required in order to distinguish the qualitative spectral changes due to the chlorophyll quantity or crop variety, from the quantitative coverage change. In this paper, we apply our agricultural independent component analysis (ICA) model to an airborne hyperspectral sensor image, which includes noise and fluctuation of coverage, and estimate pure spectra and the mixture ratio of crop and soil in agricultural land simultaneously.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.11
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• pp.463-468
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• 2017

To sense a wide area with mobile nodes, the uniformity of node deployment is a very important issue. In this paper, we consider the coverage problem to sense big data in sparse mobile ad hoc networks. In most existing works on the coverage problem, it has been assumed that the number of nodes is large enough to cover the area in the network. However, the coverage problem in sparse mobile ad hoc networks differs in the sense that a long-distance between nodes should be formed to avoid the overlapping coverage areas. We formulate the sensor coverage problem in sparse mobile ad hoc networks and provide the solution to the problem by a self-organized approach without a central authority. The experimental results show that our approach is more efficient than the existing ones, subject to both of coverage areas and energy consumption.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.10-18
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• 2007

In this paper, we address the node positioning method for minimizing the overlap sensing areas in wireless sensor networks with adjustable sensing ranges. To find a optimal node position, we derive a optimal equations by using the overlapped areas, each node's radiuses and expended angles of opposite neighboring nodes. Based on it, we devise a new node positioning method, called as ASRC(Adjustable Sensing Ranges Control). Unlike existing condition based model, our proposed method is derived from mathematical formula, and we confirm its validity through various simulations.

• Journal of Satellite, Information and Communications
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• v.9 no.3
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• pp.91-97
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• 2014

In this paper, we propose weighted detection probability with distance between primary user and secondary users by using cooperative spectrum sensing based on energy detection. And we analysis and simulate the result. We suggest different distance between primary user and secondary users and the wireless channel between primary user and secondary users is modeled as Gaussian channel. From the simulation results of the cooperative spectrum sensing with weighted method make coverage bigger compared with non-weight, and We show higher sensing efficiency when we put weight detection probability than before method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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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.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1757-1766
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• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.

• Proceedings of the KSRS Conference
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• v.1
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• pp.130-133
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• 2006

Three scatterometers will be launched by Europe, India, and China in the next few years and they will fly in tandem with QuikSCAT of the United States. The potential improvement in coverage will open up new operational and research applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3B
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• pp.453-461
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• 2010

A critical issue in wireless sensor networks is an energy-efficiency since the sensor batteries have limited energy power and, in most cases, are not rechargeable. The most practical manner relate to this issue is to use a node wake-up scheduling protocol that some sensor nodes stay active to provide sensing service, while the others are inactive for conserving their energy. Especially, CTC (Connected Target Coverage) problem has been considered as a representative energy-efficiency problem considering connectivity as well as target coverage. In this paper, we propose a new energy consumption model considering multiple-targets and create a new problem, CMTC (Connected Multiple-Target Coverage) problem, of which objective is to maximize the network lifetime based on the energy consumption model. Also, we present SPT (Shortest Path based on Targets)-Greedy algorithm to solve the problem. Our simulation results show that SPT-Greedy algorithm performs much better than previous algorithm in terms of the network lifetime.