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\def \CapstoneTeamName{Automated Fenceless Grazing}
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\def \GroupMemberOne{Ryan Alder}
\def \GroupMemberTwo{Danila Fedorin}
\def \GroupMemberThree{Matthew Sessions}
\def \CapstoneProjectName{Automated Fenceless Grazing}
\def \CapstoneSponsorCompany{Oregon State University}
\def \CapstoneSponsorPerson{Bechir Hamdaoui}
\def \DocType{Project Archive Master Document}

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\begin{document}

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        \begin{abstract}
            The Fenceless Grazing System aims to reduce the amount of work
            needed by farmers to keep and manage herds of grazing animals. The project
            implements a network of devices using the LoRa wireless communication protocol to allow
            for long-range interaction between animal-worn collars and a gateway.
            An HTTP-based JSON API server is hosted on the same hardware as the gateway, and applies configuration
            changes to collars through an application built for Android mobile devices.
            A MariaDB SQL database management system is used to store the data
            received from the collar for viewing and analysis. This document
	    contains all information and major documents gathered and created during
	    the process of implementing the Fenceless Grazing System.
        \end{abstract}     
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\end{titlepage}

\pagebreak
\section*{Table of Contents}
\input{generated.toc}

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\section{Project Introduction}
The Fenceless Grazing System was designed and implemented as requested by OSU professor Bechir Hamdaoui,
who also served as the project's client.
Prof. Hamdaoui believes that there is an emerging market for smart collar systems, and wanted to explore
the implementation of such a system using the LoRa \textbf{Lo}ng \textbf{Ra}nge communication protocol.
The project is meant as a proof of concept of an automated animal tracking and monitoring system.

The team consisted of three members:

\begin{itemize}
    \item \textbf{Ryan Alder} served as the liaison with Prof. Hamdaoui, and as a LoRa expert. Being the
        most familiar of the three team members with the LoRa protocol, he oversaw and aided the
        implementation of the collar and gateway, each of which used the protocol to communicate with
        the other. Ryan also tested the system in practice, measuring the effective range of the
        components and verifying that data can be transmitted back and forth.
    \item \textbf{Danila Fedorin} worked on the "high level" components of the system, namely
        the Android application and the API server, as well on the overall system architecture.
        He additionally designed the database schema and part of the data encoding protocol
        used by the system.
    \item \textbf{Matthew Sessions} worked on the "low level" components of the system, namely
        the collar and gateway. Having strong background in embedded development, Matthew
        was the source of truth in the implementation of hardware components, as well
        as their troubleshooting.
\end{itemize}

Prof. Hamdaoui was largely not involved in the overall design of the system. Though he requested
the use of the LoRa protocol, and suggested an overall architecture for the project, the same
overall architecture was independently developed by the team. In meetings, scheduled approximately
once a month, Prof. Hamdaoui provided guidance on which component of the system to prioritize next,
but gave the team the freedom to decide how to implement said component.

The COVID-19 pandemic led the development to slow down slightly, since team meetings and interaction
became more difficult. While previously, hardware components were distributed among some team members
(Ryan and Matthew), this became impractical due to the requirement of social distancing. However, this
did meaningfully affect the project deliverables, as the team was able to set up a system for remotely
modifying the physical components' firmware.

\subsection*{Next Steps}
To pick the project up, we recommend that different hardware be used for the collar devices. We have run
into some serious limitations while using the LoRa-enabled ATMega controllers; the combination of LoRa,
GPS, and Protobuf firmware was too large to fit in device memory. This led us to develop our own, stripped
down versions of a lot of the software. Yet even with this, the firmware on the collar ends up running
in a very constrained environment, and small changes that increase stack usage even by a small amount
can lead to crashes.

At present, the collar is capable of sending its position, retrieving valid grazing areas, and verifying
that it is in a valid position. The next step on the collar side, then (after the hardware changes suggested
above) would be to add to the collar a physical indication of whether or not it is in a valid grazing
boundary (see the Design Document below for an idea of what that is). This physical indication can be
an LED light, but the final goal would be to have that indication be a buzzer or an electric shock.

The gateway software is capable of receiving and configuring a single collar device (See Appendix 4
for an explanation of why only a single device is currently supported). The next step for the 
gateway software, then, is to add support for multiple collar devices. Additionally, the gateway
software currently uses the Things Network to decode and process LoRaWAN packets. This is less
than ideal since it requires a connection to the Internet (rather than allowing the system
to work on a LAN), and because it uses a third-party service to process animal data. To avoid
this, the best course of action would be to use a local LoRaWAN implementation on the gateway.
At the time of writing such software is not easily available, and was deemed too difficult to
create. Finally, we recommend that a mechanism be added to the gateway software to verify
that a collar has received an updated grazing boundary.

The Android application is, in our view, the lowest priority at present. It is capable
of authenticating and working with the API server, viewing animal locations,
and of modifying collar grazing boundaries. Next steps could include the display of additional
statistics (this would also require changes to the API server).

Please refer to Appendix 4 for a description of all things that need to be done.

\end{document}