9 citations · 13 across the 7 of their papers we have counts for
7 papers
The Case for Hierarchical Deep Learning Inference at the Network Edge
Ghina Al-Atat, Andrea Fresa, Adarsh Prasad Behera +3
Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of t…
Performance of 802.11be Wi-Fi 7 with Multi-Link Operation on AR Applications
Molham Alsakati, Charlie Pettersson, Sebastian Max +2
Since its first release in the late 1990s, Wi-Fi has been updated to keep up with evolving user needs. Recently, Wi-Fi and other radio access technologies have been pushed to their…
Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge
Vishnu Narayanan Moothedath, Jaya Prakash Champati, James Gross
We consider a resource-constrained Edge Device (ED), such as an IoT sensor or a microcontroller unit, embedded with a small-size ML model (S-ML) for a generic classification applic…
Energy-Optimal Sampling for Edge Computing Feedback Systems: Aperiodic Case
Vishnu Narayanan Moothedath
We study the problem of optimal sampling in an edge-based video analytics system (VAS), where sensor samples collected at a terminal device are offloaded to a back-end server that…
Realistic Modeling of Human Timings for Wearable Cognitive Assistance
Manuel O. J. Olguín Muñoz, Vishnu N. Moothedath, Jaya Prakash Champati +3
Wearable Cognitive Assistance (WCA) applications present a challenge to benchmark and characterize due to their human-in-the-loop nature. Employing user testing to optimize system…
Ainur: A Framework for Repeatable End-to-End Wireless Edge Computing Testbed Research
Manuel Olguín Muñoz, Seyed Samie Mostafavi, Vishnu N. Moothedath +1
Experimental research on wireless networking in combination with edge and cloud computing has been the subject of explosive interest in the last decade. This development has been d…
Energy Efficient Sampling Policies for Edge Computing Feedback Systems
Vishnu Narayanan Moothedath, Jaya Prakash Champati, James Gross
We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and genera…