Title

The World in 0’s and 1’s: Internet of Things Data Fusion and Sensor Interpretation

Poster Number

06B

Lead Author Major

Computer Engineering

Lead Author Status

Sophomore

Second Author Major

Computer Engineering

Second Author Status

Junior

Third Author Major

Computer Engineering

Third Author Status

Junior

Fourth Author Major

Electrical Engineering

Fourth Author Status

Junior

Format

Poster Presentation

Faculty Mentor Name

Fadi Muheidat

Faculty Mentor Email

fmuheidat@pacific.edu

Faculty Mentor Department

Electrical and Computer Engineering

Abstract/Artist Statement

As technology inevitably continues to progress, and the interconnection between devices only growing in parallel scale, information acquisition and accumulation are notions that must be taken into consideration. Our research into data fusion investigates a more efficient, expansive, economical, and secure method of information compilation, thus making it so that such data will become assets for users and society. Data fusion, itself, is a means of integrating various data sources in order to deliver information that is more comprehensive, coherent, and complete, than if provided by any individual source. To put this concept into perspective, imagine the human body. A human body, with its five senses, possesses the capacity to collect bits and snippets of information about the world around it. Only when this mass of information is properly examined and evaluated by the brain can conclusions be made, and subsequent decisions determined. To this objective, though, therein lies a multitude of obstacles that will impede progress (e.g. data imperfections and inconsistencies, misled data association, data fusion portability, machine independence, security vulnerabilities, etc.). In order to amend such issues, while keeping our research in mind, our current efforts are turned towards the creation of an Arduino board fitted with a variety of sensors working in tandem with a Raspberry Pi appended with a wifi module that allows for their communication over the internet. Upon the completion of our modular device and the setup of our server environment, we hope to turn towards solidifying our design by integrating CORAL machine learning to assist us in resolving said issues. In doing so, we will be able to better utilize various data fusion models in our research such as: probabilistic, artificial intelligence, and evidence based mathematical theories such as Dempster-Shafer, which will be covered further during our board presentation.

Location

DeRosa University Center Ballroom

Start Date

27-4-2018 12:30 PM

End Date

27-4-2018 2:30 PM

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Apr 27th, 12:30 PM Apr 27th, 2:30 PM

The World in 0’s and 1’s: Internet of Things Data Fusion and Sensor Interpretation

DeRosa University Center Ballroom

As technology inevitably continues to progress, and the interconnection between devices only growing in parallel scale, information acquisition and accumulation are notions that must be taken into consideration. Our research into data fusion investigates a more efficient, expansive, economical, and secure method of information compilation, thus making it so that such data will become assets for users and society. Data fusion, itself, is a means of integrating various data sources in order to deliver information that is more comprehensive, coherent, and complete, than if provided by any individual source. To put this concept into perspective, imagine the human body. A human body, with its five senses, possesses the capacity to collect bits and snippets of information about the world around it. Only when this mass of information is properly examined and evaluated by the brain can conclusions be made, and subsequent decisions determined. To this objective, though, therein lies a multitude of obstacles that will impede progress (e.g. data imperfections and inconsistencies, misled data association, data fusion portability, machine independence, security vulnerabilities, etc.). In order to amend such issues, while keeping our research in mind, our current efforts are turned towards the creation of an Arduino board fitted with a variety of sensors working in tandem with a Raspberry Pi appended with a wifi module that allows for their communication over the internet. Upon the completion of our modular device and the setup of our server environment, we hope to turn towards solidifying our design by integrating CORAL machine learning to assist us in resolving said issues. In doing so, we will be able to better utilize various data fusion models in our research such as: probabilistic, artificial intelligence, and evidence based mathematical theories such as Dempster-Shafer, which will be covered further during our board presentation.