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S. Sitharama Iyengar

Researcher at Indian Institute of Technology Ropar

Publications -  794
Citations -  15356

S. Sitharama Iyengar is an academic researcher from Indian Institute of Technology Ropar. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 53, co-authored 776 publications receiving 13751 citations. Previous affiliations of S. Sitharama Iyengar include Jackson State University & Manipal Hospitals.

Papers
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Book ChapterDOI

Standards for Building Wireless Sensor Network Applications

S. Sitharama Iyengar, +4 more
TL;DR: This chapter contains sections titled: 802.XX Industry Frequency and Data Rates ZigBee Devices and Components ZigBee Application Development Dissemination and Evaluation Problems.

Techniques to explore time-related correlation in large datasets

Sumeet Dua, +1 more
TL;DR: A solution to the problem of finding sequences, in a database of unequal sized sequences, that are similar to a given query sequence, and a unique indexing technique to index identified subsequences within a reference sequence is proposed.
Book ChapterDOI

PCF-Engine: A Fact Based Search Engine

K. C. Srikantaiah, +6 more
TL;DR: A new application called Probability of Correctness of Facts(PCF)-Engine is proposed to find the accuracy of the facts provided by the web pages to find their probability of correctness.
Book ChapterDOI

TGAR: Trust Dependent Greedy Anti-Void Routing in Wireless Sensor Networks (WSNs)

H. Sivasankari, +4 more
TL;DR: Trust dependent Greedy Anti-void Routing (TGAR) is proposed to find the reliable path from source to sink and uses Bayesian estimation model to calculate the trust value for the entire path.
Proceedings ArticleDOI

Authentication by Mapping Keystrokes to Music: The Melody of Typing

Amith K. Belman, +8 more
TL;DR: Using the data from 30 users, who typed fixed strings multiple times on a desktop, shows that these auditory signals are distinguishable between users by both standard classifiers (SVM, Random Forests and Naive Bayes) and humans alike.