S
Sriram Chellappan
Researcher at University of South Florida
Publications - 124
Citations - 1687
Sriram Chellappan is an academic researcher from University of South Florida. The author has contributed to research in topics: Wireless sensor network & The Internet. The author has an hindex of 22, co-authored 118 publications receiving 1430 citations. Previous affiliations of Sriram Chellappan include University of Missouri & Missouri University of Science and Technology.
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Deploying Wireless Sensor Networks under Limited Mobility Constraints
TL;DR: This paper proposes the simple peak-pit-based distributed (SPP) algorithm that uses local requests and responses for sensor movements and demonstrates the effectiveness of the algorithms from the perspective of variance minimization, number of sensor movements, and messaging overhead under different initial deployment scenarios.
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Mobility Limited Flip-Based Sensor Networks Deployment
TL;DR: This paper proposes a minimum-cost maximum-flow-based solution to deployment of sensor networks using mobile sensors that optimizes both the coverage and the number of flips, and observes that increased flip distance achieves better coverage and reduces thenumber of flips required per unit increase in coverage.
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Multimodal Wearable Sensing for Fine-Grained Activity Recognition in Healthcare
TL;DR: A novel approach for in-home, fine-grained activity recognition uses multimodal wearable sensors on multiple body positions, along with lightly deployed Bluetooth beacons in the environment to exploit user's ambient environment and location context with wearable sensing andetooth beacons.
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Associating Internet Usage with Depressive Behavior Among College Students
TL;DR: The author report their findings from a month-long experiment conducted at Missouri University of Science and Technology on studying depressive symptoms among college students who use the Internet using real campus Internet data collected continuously, unobtrusively, and while preserving privacy.
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HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing
TL;DR: Experimental results demonstrate that the HuMAn system can detect 21 complex at-home activities with high degree of accuracy, and a novel two-level structured classification algorithm that improves accuracy by leveraging sensors in multiple body positions.