Modeling Hidden Nodes Collisions in Wireless Sensor Networks: Analysis Approach

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

Comparison of different TTE predictors on the independent test set.

<p>(<b>A</b>) ROCs of five different methods. The values in the brackets are the average auROCs of each method. (<b>B</b>) Precision-recall curves of five different methods. Values in brackets are the average auPRCs of each method.</p

Gene differential expression distribution of prediction results.

<p>The vertical axis represents the fold changes of the gene expression level when <i>R. solanacearum</i> is cultured in tomato (planta) in comparison to the situation when <i>R. solanacearum</i> is cultured in rich medium (CPG). The number in the bracket is the gene number within this score interval. The statistically significant expression difference is observed between genes with SVM scores <0 and genes with SVM scores ≥1.0 (Mann-Whitney <i>U</i>-test, <i>p</i>-value <0.01).</p

Overview of the proposed TTE predictor BEAN.

<p>A full-length sequence is used to construct its profile (PSSM) via HHblits search. Only the first 2–51 residues of the N-terminal are used to compute the profile-based <i>k</i>-spaced amino acid pair composition. Then, the feature vectors with a dimensionality of 1600 are taken as input to train a linear SVM classification model. Through the parameter transformation of the established model, we obtained the weights of each <i>k</i>-spaced amino acid pair and analyzed the evolutionary conservation and sequence position distribution of each pair. We also used our BEAN to scan a pathogen genome and identify TTE candidates.</p

Classification performance of BEAN.

<p>(<b>A</b>) ROCs of different SVM kernel functions. (<b>B</b>) ROCs of different feature extraction methods. (<b>C</b>) ROCs of classification models using all 1600 features and the 100 top weighted features. The values in brackets are the auROCs of each model. All of above results are based on Wang’s data.</p

Sequence position distribution of <i>k</i>-spaced amino acid pairs.

<p>(<b>A</b>) Each point represents the overall frequency of the 50 most positively weighted amino acid pairs occurring at the N-terminal sequences from TTEs (red triangle) or non-TTEs (blue circle). Trend lines are drawn using <i>loess</i> smoothing for the points from TTEs (red) and non-TTEs (blue), respectively. (<b>B–D</b>) Position density distribution of pairs [SN], [T.V] and [VA] in TTEs (red solid line) and non-TTEs (blue dotted line). The horizontal axis in (<b>B–D</b>) is the same as in (<b>A</b>).</p