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The day-to-day lives of humans are changing remarkably due to the evolution in tools, techniques and technology across the planet. This evolution is not only impacting the growth of humans but also contributing to the growth and well-being of society and country. The domain of data analytics (DA) and internet of things (IoT) is very much facilitating this growth. But there have been only a handful of innovations and explorations in the field of agriculture, although it being the backbone and largely contributing to the gross domestic product (GDP) of a country like India. The reason for it may be profuse, such as the erratic weather conditions, improper irrigation, farmers being skeptical using modern tools and many more. But being in a developing country that has its primary focus on invention and innovation, a consensus has to be reached so that the modern tools and technologies, abet agriculture throughout the country. In our work, an attempt is made to analyze the different aspects that influences the variable outcomes in agriculture with the aid of various data analytic algorithms. Rainfall, humidity and temperature are some of the variables that determine the type of crop. Therefore, the task of prediction of crop type given these factors using decision trees and support vector machines (SVM) is implemented, and the accuracy of the models are computed. Here, more focus is given to the state of Karnataka and to its major crops. With rice, ragi and maize being some of the predominant crops, an analysis is portrayed considering the yield across the state.

Wireless sensor networks (WSNs) have become an integral part of healthcare monitoring applications and also using in a wide range of domains such as environmental monitoring, healthcare, asset monitoring modern warfare scenarios, industrial and production monitoring. A number of solutions have been proposed so far which provides assimilation of data on an hourly basis and the capability of sensors are approximated to almost ideal situation. With the exploration and advancement in the field of internet of things (IoT), the focal point has shifted towards the interoperability of WSNs and a cloud-based central data repository which collaborates and comprehends a uniquely identifiable internet like structure. This bottom up internet-like structure has paved the way for a sensor integrated cloud-based architecture REALSENSE. This paper provides a detailed run down on the REALSENSE architecture which integrates WSN with Internet of Things in a robust efficient approach. Using the REALSENSE architecture, a set of real-time applications can be deployed, some of them are illustrated in this paper.

Clustering is a proficient approach to breaking down and locate the enormous, concealed, obscure and fascinating information in expansive scale dataset, which encourages the fast improvement of information mining innovation in late decades. With the advancement of area-based administration, moving article clustering turns into a blossoming subject in related fields as a key some portion of information mining innovation. It is a moderately new subfield of information mining which increased high notoriety. This paper considers the issue of proficiently keeping up a clustering of an active arrangement of information focuses that move persistently in 2D Euclidean space. This paper recommends an improved k-means (i-kmeans) algorithm which is done in four stages, which uses segmentation cluster as part of improved k-means. To describe the effectiveness of the obtained cluster we use Silhouette Coefficient metric. Experimental results reveal that improved i-kmeans technique gives better results in terms of accuracy and quality than the traditional one.