V. Srividhya ¹, T. Shankar ¹, A. Karthikeyan ¹, Pranjay Gupta ¹

Affiliations
1 School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN

Abstract

Objectives: To implement an effective spectrum access technique using cognitive radio technology with distance based clustering and routing algorithm with in wireless sensor networks and test this algorithm with different scenarios by varying position of the base station. Methods: Network region is divided into parts for allocating the spectrum and topology control, by distance based multi hop clustering and routing algorithm which decides cluster forming strategy on the basis of distance from the base station and route the data with less hops. Findings: The simulation shows, the proposed algorithm was able to provide a better Network Life time with the same amount of initial energy. Also, election of cluster heads on the basis of distance has helped in increasing the stability. And, with different efficient distance thresholds deciding single hop and multi hop communication. Application/Improvements: The proposed algorithm increases the scalability of the network, in comparison to the existing algorithms, by an average of 29%, when the base station is kept at the corner and, by 42.5%, when it is kept away from the corner.

Keywords

Cognitive Radio, Clustering, Energy efficient, Routing, Stability, Wireless Sensor Network (WSN).

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V. Srividhya ¹, K. Sujatha ¹, R. S. Ponmagal ²

Affiliations
1 EEE/CSE Department, Center for Electronics, Automation and Industrial Research (CEAIR), Dr. M.G.R. Educational & Research Institute, Maduravoyal, Chennai, Tamil Nadu, IN
2 EEE Department, Meenakshi College of Engineering, Maduravoyal, Chennai – 560064, Tamil Nadu, IN

Abstract

Objective of the Work: The highlight of this research work is to discover the ethylene gas level used for ripening of fruits by detecting ethylene gas (C2H4 in ppm) level employing soft sensor built using image processing and Artificial Neural Networks (ANN) algorithms. Methods/Statistical Analysis: The proposed method relies on the color which denotes the various stages in ripening and in turn indicates the amount of ethylene gas required. The changes in color, texture, intensity variation, mean, variance and standard deviation extracted from the images are the features which enable the personnel to determine the amount of ethylene gas. The Feed Forward Neural Network (FFNN) is used for ethylene gas estimation. This is made possible using Back Propagation Algorithm (BPA) for training the FFNN. As a part of image processing the intensity values in color images and its variation are tracked by dithering which is used as a unique feature input to train the FFNN. Major Findings: The novelty of the proposed method depends on the FFNN estimating the ethylene gas needed for ripening process in a feed forward fashion thereby providing the precision and recall values spontaneously for every instance. Application/Improvements: Earlier a circuit with capacitance model is used to generate ethylene gas for this purpose. Nearly 51 images are considered for training and testing respectively. Testing and confirmation result shows the required precision and recall level are in range of 80 to 89% and 100% respectively.

Keywords

Back Propagation Algorithm, Ethylene Gas, Feed Forward Neural Network Feature Extraction, Image Processing.

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