• 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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.867-872
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• 2005

This study introduces a method to evaluate the probability of a specific area to be affected by a drought of a given severity and shows its potential for investigating agricultural drought characteristics. The method is applied to Gyeonggi as a case study. The proposed procedure includes Standard Precipitation Index(SPI) time series, which are linearly transformed by the Empirical Orthogonal Functions(EOF) method, These EOFs are extended temporally with AutoRegressive Moving Average(ARMA) method and spatially with Kriging method. By performing these simulations, long time series of SPI can be simulated for each designed grid cell in whole Gyeonggi area. The probability distribution functions of the area covered by a drought and the drought severity are then derived and combined to produce drought severity-area-frequency(SAF) curves.

• 한국수자원학회논문집
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• 제39권8호
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• pp.691-702
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• 2006

본 연구에서는 우리나라 가뭄의 공간적인 특성을 파악하고 가뭄의 진행에 따른 피해규모를 산정하기 위하여 가뭄 심도-영향면적-생기빈도 곡선을 작성하여 제시하였다. 이를 위하여 전국의 기상관측소 지점별로 SPI를 산정하였으며, 산정된 지점별 SPI 자료를 이용하여 EOF 분석을 실시하였다. EOF 분석으로부터 추출된 핵심 공간패턴자료들은 다시 공간적으로는 Kriging 기법을 이용하여 보다 세밀한 공간정보를 갖는 자료로 확장되었으며, ARMA 모형을 이용하여 장기간의 가뭄사상을 모의발생하였다. 모의발생된 공간적인 장기간의 가뭄사상들로부터 특정 가뭄심도별 영향면적별 생기빈도 곡선을 작성할 수 있었다.