Protei_Rpi

Protei, is a modular, shape-shifting sailing robot. Protei_Rpi is an attempt to prototype an IoT network architecture for the collection, analysis and broadcast of ocean data in an efficient manner.

Goal

By increasing the amount of available ocean data and incorporating easy to use technologies to access the data, the project would enable scientists, sailors and fishermen to analyse the conditions on the ocean at different locations, thus strengthening the community even further. The huge amount of data could also be used to improve existing oceanic models, such as HKU Project Waterman.

Usage

The users would access the data in the form of a map with parameters such as the water surface temperature, pressure, humidity, magnetometer, wind speed and acidity (pH) based on the geolocation of the sensors on board Protei.

System Architecture

• Each individual unit comprises of Protei, a Raspberry Pi and Sense HAT. The Sense HAT measures the temperature, humidity, pressure and orientation The GPS and GSM data has been simulated for this version.

• In addition to the above, one of the units is converted into a Network Router and a MQTT broker to act as a data sink for aggregation.

• Since the boats are at sea, battery usage has to be minimized. Communication, mainly in the form of GSM, needs to be controlled appropriately, keeping in mind its financial cost and battery usage per message. This leads to a financial cost (frequency of update) vs battery usage trade-off.

• Two different Pi networks are implemented so that we are able to obtain more data while we save energy in the following ways:

1. If the Router Pi (Cental Boat) is not nearby, we switch off the Wi-Fi and send the data directly to the server using the simulated GSM.

2. If a Central Boat is nearby, the Pi connects to it using Wi-Fi. It then sends the data to the the central boat, which aggregates the data from multiple boats and sends it to the server.

Thus, The network architecture for communication between the server and Proteis are based on the relative proximities of the boats.

Code

connect_pi.sh

Shell Script to manage Wi-Fi connection
- Takes in the coordinates as two arguments 
- Wi-Fi ESSID hardcoded
- Checks if Wi-Fi with a specific ESSID is available
- If yes, connect to it and send the data to broker Pi
- If no, connect to mobile data and send data to the server

send_data.py

Python Script for sending out the Data
- Takes x_coordinate,y_coordinate and an Int as an argument
- Int decides where to send out the data [ Server (1) / Router (2)]
- Data: Coordinates, Device ID, Time, Temperature, Pressure, Humidity, Magnetometer
- Computes the MAC of the data and sends it along

broker_pi.py

Python Script for the broker Pi
First Client:
- Listens on a specific MQTT channel
- Computes MAC, checks it against the data
- Stores the data from three Rpis on log.json
- After receiving data from three Rpis, AGGREGATES DATA
    - computes average of temperature, pressure, humidity
- Sends it off to the server on a different channel
- Truncates log.json, resets counter
Second Client:
- Receives data in the form of an integer from the server
- Updates the threshold value of the number of JSON strings to be aggregated and sent back to the server

geo.py

Python Script for the Server [AWS]
- Listens to feed on two MQTT channels
    - For data directly sent to the server from non-router Pis
    - For aggregated data sent from router Pis
- Checks for data integrity by computing the MAC and verifying against sent MAC
- Stores the data in protei.geojson or neighborhoods.geojson according to where the data is received from

update_threshold.py

Python Script to update the threshold value on the broker Pi
- Takes int as argument
- Updates the specific feed for the broker to listen to

geojs.html

HTML file for plotting GeoJSON data using Leaflet
- Displays data from 'protei.geojson' as Markers
- Displays data from 'neighborhoods.geojson' as Polygon (Choropleth Map)
- leaflet.markercluster.js, MarkerCluster.css for aesthetics

Future

The two Pi networks were built keeping in mind the cost vs battery trade-off which exists when the boats are at sea. For the purpose of data aggregation on board the Central Boat, instead of using the Wi-Fi bandwidth, LoRa Technology can be utilized to further lower the power consumption and boost up the range to 10-15kms. One of the existing LoRa IoT solutions is the Seeduino LoRaWAN with GPS