Wireless Sensor Network (WSN) technology used to sense various types of physical and environmental conditions with the availability of small and low-cost sensor nodes. Main drawback in WSN is limited battery power in the sensor nodes. It is needed to distribute the energy dissipated through WSN and also needed to maximize the lifespan of sensor nodes. Energy efficiency can be accomplished through hierarchical routing protocols. One of the fundamental protocol in this class is Low Energy Adaptive Clustering Hierarchy (LEACH). This paper gives a survey of LEACH routing protocol for WSN and compared the performance in homogeneous and heterogeneous environment. Here, first analyzed the basic distributed clustering routing protocol LEACH, which is in a homogeneous environment, then analysed with the heterogeneity concept in nodes to increase the life of WSN. Simulation results were obtained using MATLAB that shows the LEACH heterogeneous environment significantly reduces energy consumption and increases the total lifetime of the WSN than LEACH homogeneous environment.

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Software fault prediction plays a vital role in software quality assurance. Identifying the faulty modules helps to better concentrate on those modules and helps improve the quality of the software. With increasing complexity of software nowadays feature selection is important to remove the redundant, irrelevant and erroneous data from the dataset. In general, Feature selection is done mainly based on filter and wrapper. In this paper a hybrid feature selection method is proposed which gives a better prediction than the traditional methods. NASA's public dataset KC1 available at promise software engineering repository is used. To evaluate the performance of the software fault prediction models Accuracy, Mean absolute error (MAE), Root mean squared error (RMSE) values are used.

Rainfall data is collected to predict the storm warnings from the hydrological data. This is considered as a research idea as it consumes huge number of records from the distributed system. This paper describes a novel solution to manage the data based on spatial temporal characteristics using a Map Reduce Framework. The workload is classified using Support Vector Machine (SVM). It uses feature selection and reduction algorithm associated with the dataset.Various rainstorm concept prediction is achieved using the big raw rainfall data. The dataset impact parameters are classified into local, hourly, and overall storms. The proposed system serves as a tool for predicting rainstorm from a large amount of rainfall data in a efficient manner. The result indicates the proposed system improves the performance in terms of accuracy and efficiency.