Title: Machine Learning (ML) and AI to Provide Actionable Intelligence for IoT Systems Addressing Climate Change
Conference: Forward Research & Innovation Summit | December 10, 2021 | Puerto Rico
Author: Kamil Agi, Ph.D. | President & CEO | SensorComm Technologies, Inc.

Abstract: Big data analytics (combined with IoT) has implications across all industries. The opportunity to leverage machine learning (ML) and artificial intelligence (AI) algorithms is not only in how the data is collected (sensors), but also in how that data is converted to information, and then to actionable intelligence.

Part I of this talk will discuss a new IoT-based methane sensor system being developed in conjunction with the University of New Mexico. The output sensor electronics leverage ML/AI algorithms to identify/differentiate multiple sources of methane emission (anthropogenic vs. agricultural vs. wetland) using SensorComm’s smart IoT platform to significantly simplify the challenge of mitigation.

Part II of this talk will discuss how pollution from the transportation sector is one of the largest contributors to the existential threat of climate change. Currently, individual polluters (i.e. vehicles driven) are not being identified. As a result, mitigation solutions (e.g. from car/truck manufacturers) intended to address lower emission regulations remain relatively ineffective. To address this challenge, SensorComm has developed Wi-NOx™ – a smart IoT mobile pollution monitoring system. Installed at the tailpipe, Wi-NOx™ provides key stakeholders with real-time business intelligence by capturing the NOx footprint of individual vehicles (and drivers) to deliver fuel, GHG and air quality diagnostics. The analytics on the time series data from individual vehicles helps prioritize repairs, replacements, and/or transitions to renewable energy sources (e.g. electric) enabling governments, cities and transit authorities to accelerate planning and infrastructure changeover.

This presentation will discuss the application of ML/AI to not only data collection (e.g. sensor systems), but also to the conversion of that data to actionable intelligence as applied to climate change.