Abstract: Performance predictions of component assemblies and obtaining performance properties from these predictions are a crucial success factor for component based systems. The number of methods and tools has been developed that analyze the performance of software systems. These methods and tools aim at helping software engineers by providing them with the capability to understand design trade-offs and optimize their design by identifying performance or predict a systems performance within a specified deployment environment. In this paper, we establish a basis to select an appropriate prediction method and to provide recommendations for future research, which could improve the performance prediction of componentbased systems.

• Publication Date: 25-May-2014
• DOI: 10.15224/978-1-63248-028-6-01-01
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Abstract: A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions and to cooperatively pass their data through the network to a Base Station. Due to the increase in the quantity of data across the world, it turns out to be very complex task for analyzing those data. Categorize those data into remarkable collection is one of the common forms of understanding and learning. This leads to the requirement for better data mining technique. These facilities are provided by a standard data mining technique called Clustering. Clustering can be considered the most important unsupervised learning technique so as every other problem of this kind; it deals with finding a structure in a collection of unlabeled data. This paper reviews four types of clustering techniques- K-Means Clustering, LEACH, HEED, and TEEN. K-Means clustering is very simple and effective for clustering. It is appropriate when the large dataset is used for clust

• Publication Date: 25-May-2014
• DOI: 10.15224/978-1-63248-028-6-01-02
• Views: 0
• Downloads: 0