The Fall 2020 sensor miniproject uses simulated internet-connected sensors. Simulated sensors are used to test proposed designs against impairments including delayed, missing or incorrect data. The IoT simulator consists of a Websockets server that emits data packets with random timing to the backend client.

Prototyping across STEM disciplines is typically done with Python. If you don't already have Python, Miniconda is a small but powerful Python distribution for MacOS, Windows and Linux. The Microsoft Windows Store also has Python.

Websockets are used for this task as they are lightweight and suited to irregular data streams. Python websockets provide server / client Websockets API.

Many web interfaces (in any code language) such as the Python websockets package use coroutines to allow low overhead handling of large numbers of clients. Python asynchronous routines require specific syntax and call practices or they may fail to execute (hang forever) or crash. async def indicates an asynchronous function instead of def for an plain Python function.

This assignment is done in two-person teams, where each student should run the simulation and work together on the analysis and turn in one joint report. The maximum number of points possible for this assignment is 100.

This simulation was tested to work on standard computers such as a laptop or Raspberry Pi. If you try to run it in a virtual machine or browser-based cloud resource, the code may not work or require additional system configuration. Typically sensor prototypes are worked on with a laptop, so that is the suggested use case for this assignment.

(15 points total for this section)

Please "fork" this GitHub repository and place all your assignment responses and results into Report.md in your repository. By forking this repo, you will be able to more easily integrate any updates I might make to this repo as bugs are discovered or other enhancements are made. Report.md is a Markdown formatted text file with links to or inline figures of the plots generated in PNG format.

This package is "cloned" to your computer after forking this repo like:

git clone https://github.com/username/2020-sensor-miniproject

Your project can be synced to updates from the repo by setting this repo as upstream.

The program is setup (including necessary prerequisites) by:

python -m pip install -e 2020-sensor-miniproject

The "-e" option makes the install in development mode, so that any changes made in this directory are used upon next import sp_iotsim. Change to directory "2020-sensor-miniproject" to edit and run the code.

A simple example is run using two Terminal windows. In the first Terminal run:

python -m sp_iotsim.server

then open another Terminal / Command Prompt and type:

python -m sp_iotsim.client

(15 points for this section)

• setup and run the Python code as described above on your computer
• What is the greeting string issued by the server to the client upon first connecting?

Python standard library includes JSON, which is a common serialization format used to interchange data among networked devices. Normally we would store the retrieved data into a database. However here since we're just testing for a fixed amount of time and this is a short assignment, we just store the JSON data to a file as-is, line by line as they come in.

Python file operations are generally done via pathlib. Example snippet:

from pathlib import Path

# other code; argparse sets P.log value from command line

filename = Path(P.log).expanduser()

# other code

file = filename.open("a")

# other code

file.write(txt + "\n")
file.flush()

# other code

file.close()

While file I/O streaming and many other options exist, for this assignment we can simply write within the main for or while loop.

(20 points total for this section)

• Add Python code to Websockets client that saves the JSON data to a text file as it comes in (message by message)

Run the server and client and collect data. Run them for at least 20 minutes so that you get hundreds of data values. The data will be stored to a text file that you implemented in Task #1 (be sure this file is actually saving data incrementally before you let it run for a while).

Typically when analyzing data, whether simulated or from real sensors, we use an external library as directly using native Python types can be too slow and cumbersome for numerical analysis. Popular numeric libraries for Python include:

• Numpy (underlies most engineering and science Python packages)
• Pandas (data science with large heterogeneous 1D and 2D data)
• Xarray (N-dimensional data science)

To keep things simple, for this miniproject I think Pandas is adequate as using Xarray assumes one is already experienced with Pandas. I don't think Xarray is necessary for this miniproject.

(20 points total for this section)

• what are the median and variance observed from the temperature data (at least 100 values) [3 points]
• what are the median and variance observed from the occupancy data (at least 100 values) [3 points]
• plot the probability distribution function for each sensor type? [6 points]
• What is the mean and variance of the time interval of the sensor readings? Please plot its probability distribution function. Does it mimic a well-known distribution for connection intervals in large systems? [8 points]

This coding would take place in a separate script you create. This is to make things simpler since asynchronous programming requires specific syntax and practices that complicate things in a short project like this.

(25 points total for this section)

• implement an algorithm that detects anomalies in temperature sensor data
• Does a persistent change in temperature always indicate a failed sensor?
• What are possible bounds on temperature for each room type?

(20 points total for this section)

Please create Report.md, a Markdown format text file in the top level of your GitHub repository. The report should be concise, the equivalent of about 2 pages. Please compose the report as if it were an informal engineering memo to give to your manager in a workplace.

Some points to think about:

• how is this simulation reflective of the real world?
• how is this simulation deficient? What factors does it fail to account for?
• how is the difficulty of initially using this Python websockets library as compared to a compiled language e.g. C++ websockets
• would it be better to have the server poll the sensors, or the sensors reach out to the server when they have data?

See Python.md for how to switch Python versions.

in create_connection raise OSError('Multiple exceptions: {}'.format( OSError: Multiple exceptions: [Errno 61] Connect call failed ('::1', 8765, 0, 0), [Errno 61] Connect call failed ('127.0.0.1', 8765)

This typically indicates that Websockets server isn't running (or wasn't fully started when client was started). When running, the terminal where you typed

python -m sp_iotsim.server

will print:

IoT server starting:  localhost port 8765