Articles

Found 2 Documents
Search
Journal : JUTI: Jurnal Ilmiah Teknologi Informasi

MODELING HIDDEN NODES COLLISIONS IN WIRELESS SENSOR NETWORKS: ANALYSIS APPROACH Rachman, A. Sjamsjiar; Wirawan, Wirawan; Hendrantoro, Gamantyo
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 8, No 1, Januari 2010
Publisher : Teknik Informatika, ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v8i1.a69

Abstract

This paper studied both types of collisions. In this paper, we show that advocated solutions for coping with hidden node collisions are unsuitable for sensor networks. We model both types of collisions and derive closed-form formula giving the probability of hidden and visible node collisions. To reduce these collisions, we propose two solutions. The first one based on tuning the carrier sense threshold saves a substantial amount of collisions by reducing the number of hidden nodes. The second one based on adjusting the contention window size is complementary to the first one. It reduces the probability of overlapping transmissions, which reduces both collisions due to hidden and visible nodes. We validate and evaluate the performance of these solutions through simulations.
PEMODELAN ARIMA DAN DETEKSI OUTLIER DATA CURAH HUJAN SEBAGAI EVALUASI SISTEM RADIO GELOMBANG MILIMETER Mauludiyanto, Achmad; Hendrantoro, Gamantyo; P, Mauridhi Hery; Suhartono, Suhartono
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 3, Januari 2009
Publisher : Teknik Informatika, ITS Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v7i3.a76

Abstract

The purpose of this paper is to provide the results of Arima modeling and outlier detection in the rainfall data in Surabaya. This paper explained about the steps in the formation of rainfall models, especially Box-Jenkins procedure for Arima modeling and outlier detection. Early stages of modeling stasioneritas Arima is the identification of data, both in mean and variance. Stasioneritas evaluation data in the variance can be done with Box-Cox transformation. Meanwhile, in the mean stasioneritas can be done with the plot data and forms of ACF. Identification of ACF and PACF of the stationary data is used to determine the order of allegations Arima model. The next stage is to estimate the parameters and diagnostic checks to see the suitability model. Process diagnostics check conducted to evaluate whether the residual model is eligible berdistribusi white noise and normal. Ljung-Box Test is a test that can be used to validate the white noise condition, while the Kolmogorov-Smirnov Test is an evaluation test for normal distribution. Residual normality test results showed that the residual model of Arima not white noise, and indicates the existence of outlier in the data. Thus, the next step taken is outlier detection to eliminate outlier effects and increase the accuracy of predictions of the model Arima. Arima modeling implementation and outlier detection is done by using MINITAB package and MATLAB. The research shows that the modeling Arima and outlier detection can reduce the prediction error as measured by the criteria Mean Square Error (MSE). Quantitatively, the decline in the value of MSE by incorporating outlier detection is 23.7%, with an average decline 6.5%.