&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

In hyperconnectivity scenario, managing the amount of data acquired from sensors in the Body Area Networks (BANs) is one of the major issues. In this paper we propose an on-chip context predictor based sparse sensing technology with smart transmission architecture which makes use of confidence interval calculation from the features that present in the data, thereby achieving statistical guarantee. The proposed architecture uses intelligent sparse sensing, which eradicates the collection of redundant data, thereby reducing the amount of data generated. For the performance analysis, we considered ECG data acquisition and transmission system. The proposed architecture when applied on the data collected from 10 patients reduces the duty cycle of the sensing unit to 27.99%, by achieving an energy saving of 72% and the mean deviation of sampled data from the original data is 2%. © 2014 IEEE.