I-Corps: Artificial Intelligence (AI)-Based Sensing and Data Efficient Sampling, Transmission, Storage, Analysis and Cloud Computing

Project: Research project

Project Details


The broader impact/commercial potential of this I-Corps project is to improve data from sensors that have become ubiquitous in daily life, including personal devices, smart homes and infrastructure, industrial machines and factories, autonomous machines, and the natural environment. Sensor-based ‘big data’ offers real-time visibility into a variety of systems. However, the volume, speed, and variety of this data will soon overwhelm the ability to ingest, refine, and analyze all the data. This project seeks to develop modeling methodologies that will improve the way data is collected, processed, and analyzed in a variety of industries involving embedded systems such as Internet of Things (IoT), space exploration, and biotechnology. Remote monitoring of industrial machines and distributed sensing in the oceans and the biosphere are some application areas.This I-Corps project is based on the development of a novel sensor data modeling methodology. Sensor-based data generation is about to reach 73+ trillion gigabytes by 2025. Contemporary data collection and analysis pipelines are inefficient and uneconomical for this scale of data. This technology investigates undersampling for several orders of magnitude savings in data collection, transmission, storage, cloud computing, and analytics. Some application areas include the Industrial Internet of Things (IIoT), environmental and ocean monitoring, and autonomous drones and vehicles. The novel methodology in the post-Nyquist era information extraction pipeline centered around undersampling, low-dimensional latent representations, and machine learning, with several orders of savings in the data and engineering complexity of IIoT systems. The insight on undersampling-based learning of low-dimensional latent representations and using them to solve different downstream tasks is the intellectual contribution of this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Effective start/end date8/15/227/31/24


  • National Science Foundation: $50,000.00


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