Analytics in Extracting Intelligence from IoT-Based Sensors in Transportation, Healthcare and Natural Gas Detection
Authors: Agi Kamil , Ian Robert , Smith James , Tsui Lok-kun , Halley Sleight , Garzon Fernando H , Jordan Ramiro 
Published: July 2021

Networking of sensors and the development of sensor systems have become routine technological endeavors. The advent of Internet-of-Things (IoT) technologies has enabled an expanding matrix of applications for integration of specialized sensor systems. The focus on innovation is now on driving costs down, while increasing performance. Additive manufacturing techniques and topological improvements are key drivers of innovation for sensors.

As sensors and sensor systems transform their capabilities from (strictly) data generation and gathering, the focus now shifts on how to increase the utility of the sensor and sensor system.

SensorComm defines the utility of a sensor as its ability to provide
intelligence. Intelligence comes from the collection of data. Through data-analytics, information can be extracted. The final step of interpreting the information is through the generation of intelligence. Off-the-shelf tools are available that include machine learning, artificial intelligence, and even toolboxes in platforms like MATLAB, all of which have changed the way we obtain information. The next step is obtaining the intelligence.

SensorComm has applied this methodology of intelligence extraction in three areas: 1) transportation 2) healthcare and now 3) natural gas monitoring/detection.

In transportation, we will describe how intelligence is obtained from SensorComm’s IoT-based mobile pollution monitoring system. Installed at the tailpipe, Wi-NOx™ captures the real-time pollution footprint of vehicles. The system extracts, analyzes and transforms NOx emissions data into real-time information providing transportation managers with business intelligence that leads to operational efficiencies and measurable savings. Wi-NOx™ is the cornerstone of a global pollution mitigation strategy that enables fuel savings, emissions reduction, vehicle optimization and extended asset life (to responsibly delay significant infrastructure investments like electrification).

The Wi-NOx™ system was developed to monitor emissions from vehicles. Recently, we have modified it to remotely monitor patients in healthcare applications, and now, to monitor natural gas for oil & gas applications.

In healthcare, Sensorcomm has developed a wrist-worn temperature monitoring system for COVID-19 detection using the Data-Centric Care™ model. Data-Centric Care™ focuses on data to provide the necessary intelligence to correctly assess the situation. The system uses (near) continuous temperature monitoring of individuals to identify at-risk persons who
may
or
may not
be symptomatic (for COVID-19 and beyond). The system uses a smart wearable device (wrist-worn) that monitors temperature as a trigger for second-tier screening from which a corresponding response is implemented (e.g. return to daily activity, isolate, elevated care). The continuous monitoring allows for the development of individualized thresholds (average temperature and fever threshold) rather than using population thresholds. The system identifies at-risk individuals (people with anomalies in their daily temperature profiles) for additional screening, prior to entering a facility.

The most recent development at SensorComm is the partnership with the Center for Micro-Engineered Materials at the University of New Mexico, who is developing mixed-potential methane sensors using additive manufacturing. Mitigating environmental effects and financial costs of methane leaks requires a robust, widely deployable, low-cost sensing technology to provide continuous monitoring of natural gas infrastructure. Since methane emissions originate from multiple sources (agricultural or wetlands sites), sensors need to distinguish between natural gas or one of these other sources. Strategies for gathering the data, extracting the information and providing intelligence will be discussed.

This talk will discuss the different techniques utilized in the data analytics and will provide key examples of how intelligence is extracted in the various applications.

This material is based upon work supported by the U.S. Department of Energy, National Energy Technology Office program under Award Number DE-FOA-0031864, and partially by the National Science Foundation under Grant No. 1632498.

Figure 1