Published July 16, 2020 © GPL3+

Covnent: Community Driven Map Platform for COVID-19

Covnent is a community solution, where users collect and publish data onto public maps to better inform their communities.

BeginnerFull instructions provided3 hours169

Covnent: Community Driven Map Platform for COVID-19

Things used in this project

Hardware components

Raspberry Pi Zero

Silicon Labs Thunderboard™ Sense 2 Sensor-to-Cloud Advanced IoT Development Kit

Software apps and online services

Amazon Web Services

ThingSpeak API

Google Maps

Twilio SMS Messaging API

Hand tools and fabrication machines

Laser cutter (generic)

For this project you have the option to choose between a laser cutter and a 3D printer. Both are options which allow for fast prototyping.

3D Printer (generic)

For this project you have the option to choose between a laser cutter and a 3D printer. Both are options which allow for fast prototyping.

Story

Introduction

With COVID-19's implications on transport across the world, many people are traversing across to work (and recreational regions) by walking and cycling. Many research studies show exposure to UV-light, certain air pressures and temperatures, as well as air quality, can all affect the transmission. By using the low-cost sensors embedded in the Thunderboard sense 2, the solution proposed using this technology aims to track and record the environmental data for both local uses to view later and cloud usage. This can help to plan better routes to these locations to minimise transmission. This can also help communities with the use of the cloud where the users can submit routes to help their local community to find better transport routes etc. Whilst also hosting environmental data, Convent helps users to map out outbreaks within their communities and also add custom roads and list any local shops to help re-build the community. The design includes an AI Chatbot which can be accessed via SMS ! This helps to spread awareness for COVID -19 with a universal mode of communication.

Function

Flowchart

Step 1: Configuring Thunderboard Sense 2

When you first open the thunderboard sense 2, download the android or ios app and connect to your device. This will automatically stream data from the device to the phone. This will help verify all aspects of your device is working. Also 3D Print the casing attached in the files.

Step 2: Libraries for Raspberry Pi

I) Update libraries on your raspberry pi by typing

sudo apt update
sudo apt upgrade

ii) Install Python 3

sudo apt-get install python3-pip

III) For BLE connectivity install bluepy (licensed under GNU Public V2) using:

sudo apt-get install python-pip libglib2.0-dev
sudo pip install bluepy

If you are not running a version of Debian follow the instructions here: https://github.com/IanHarvey/bluepy

While everything is downloading feel free to move onto the next step

Step 3: Setting up ThingSpeak

I) Log on/Create an account and then set up a new channel

II) Add 5 Fields in this order: Battery, Temperature, Humidity, UV Index, Carbon Dioxide

II) Find your Write API Key by navigating to API Keys section. It should look something like this with each x being a number or letter: "xxxxxxxxxxxxxxxx"

Step 4:Setting up Thunderboard Sense 2

I) Download the Bluetooth Sniffer App

II) Power your thunderboard and find it in the sniffer app and click connect

III) You should be able to see your device's ID/Address please note this as you will need it in the program

Step 5: Programming Raspberry Pi

Download the program attached/below and change:

i) The API Key

ii) Device Address

Then add it to the raspberry pi and open up the terminal and run the program

sudo python REPLACE WITH FILE ADDRESS

Please note you MUST be in sudo for this to work properly. If it does not connect unplug the thunderboard sense 2, re-plug it wait 3 seconds and promptly run the program

Step 6: Data Collection

If you look onto thingspeak you will the data is being updated live

Step 7: Google Maps

The next part of the project is run on google maps. Create an account and and navigate to my maps section and create a map. This will host the community platform for changes etc. Each community will have a shared platform. There they can add

i) Businesses

ii)Roads

ii) Bike and Walking Paths

iii) Areas to Avoid

iv) General Environmental Trends

v) Much More...

Step 8: Amazon Web Services Chatbot

The entire system will be hosted on Amazon web services for ease of use and reliability. To add to the function we will add a chatbot for users to communicate questions to help improve information and awareness.

i) Sign up for AWS Services or Log In

ii) Create a new Amazon Lex Project and create a custom bot

iii) Create your first intent and begin to add utterances, which are example questions, and the answers. Use a variety of different utterances to help the neural network to better understand the questions.

iv) Create your responses and save the intent. Create multiple intents following the same steps until all question types are completed.

v) Build and Publish Project, then proceed to head over to channels

Step 9: Twilio Setup

On the Twilio SMS page, provide the following information:

Project Name
Choose "aws/lex" from KMS key.
For Alias, choose the bot alias you provided in the project
Type the AUTH TOKEN from your Twilio account.
Type the ACCOUNT SID from your Twilio account.
Activate and copy the endpoin
Go to the twilio website and to your phone number (twilio) add this as an endpoint

Conclusion

Overall, Convent is a community measure which helps to educate community members about COVID-19 through the widely accessible SMS platform ; help map out outbreaks and restrictions; helps businesses get back on the road and set up an online presence ; helps community members to plan out better routes and collect environmental data to help curb the spread. Convent helps to detect COVID-19 transmissions through heatmaps and community data and helps to protect through public alerts, whilst simultaneously helping the community to re-build businesses and plan routes.

This was made possible due to generous hardware donations for AWS Credits and Thunderboard Sense 2

Schematics

Code

Raspberry Pi Code

from __future__ import division
import sys
from bluepy.btle import *
import struct
import thread
from time import sleep
import urllib2


PRIVATE_KEY = 'REPLACE WITH KEY'


groundURL = 'https://api.thingspeak.com/update?api_key='

def StreamingSensor():
    scanner = Scanner(0)
    devices = scanner.scan(3)
    for dev in devices:
        print "Device %s (%s), RSSI=%d dB" % (dev.addr, dev.addrType, dev.rssi)

        for (adtype, desc, value) in dev.getScanData():
            print "  %s = %s" % (desc, value)
    num_ble = len(devices)
    print num_ble
    if num_ble==0:
        return None
    ble_service = []
    char_sensor = 0
    non_sensor = 0
    bat_char = Characteristic
    temperature_char = Characteristic
    humidity_char = Characteristic
    uvindex_char = Characteristic
    co2_char = Characteristic
    count = 15
    
    for i in range(num_ble):
        try:
            devices[i].getScanData()
            ble_service.append(Peripheral())
            ble_service[char_sensor].connect('REPLACE WITH BLUETOOTH ADDRESS',devices[i].addrType)
            #ble_service[char_sensor].connect(devices[i].addr, devices[i].addrType)
            char_sensor = char_sensor + 1
            print "Connected %s device with addr %s " % (char_sensor, devices[i].addr)
        except:
            non_sensor = non_sensor + 1
    try:
        for i in range(char_sensor):
            
            services = ble_service[i].getServices()
            characteristics = ble_service[i].getCharacteristics()
            for k in characteristics:
                print k
                if k.uuid=="2a19":
                    print "Battery Level"
                    bat_char = k
                if k.uuid == "2a6e":
                    print "Temperature"
                    temperature_char = k
                if k.uuid == "2a6f":
                    print "Humidity"
                    humidity_char = k
                if k.uuid == "2a76":
                    print "uvindex"
                    uvindex_char = k
                if k.uuid == 'efd658ae-c401-ef33-76e7-91b00019103b':
                    co2_char = k
            
    except:
        return None
    while True:
        bat_data = bat_char.read()
        bat_data_value = ord(bat_data[0])
        
        temperature_data = temperature_char.read()
        temperature_data_value =(ord(temperature_data[1])<<8)+ord(temperature_data[0])
        float_temperature_data_value = (temperature_data_value / 100)
        
        humidity_data = humidity_char.read()
        humidity_data_value =(ord(humidity_data[1])<<8)+ord(humidity_data[0])
        
        uvindex_data = uvindex_char.read()
        uvindex_data_value = ord(uvindex_data[0])
        
        co2_data = co2_char.read()
        co2_data_value = ord(co2_data[0])
        
        

        print "Battery: ", bat_data_value
        print "Temperature: ", float_temperature_data_value
        print "Humidity: ", humidity_data_value
        print "UVIndex: ", uvindex_data_value
        print "CO2: ", co2_data_value
        if count > 14:
            f = urllib2.urlopen(groundURL + PRIVATE_KEY +"&field1=%s&field2=%s&field3=%s&field4=%s&field5=%s" % (bat_data_value, float_temperature_data_value, humidity_data_value,uvindex_data_value,co2_data_value))
            print f.read()
            f.close()
            count = 0
        count = count + 1 
        sleep(1)

while True:
   StreamingSensor()

Credits

Ashwin Sridhar

8 projects • 11 followers

British high school student and inventor. Passionate about STEM in and out of classrooms. Experienced with mechatronics and programming.

Covnent: Community Driven Map Platform for COVID-19

Things used in this project

Hardware components

Software apps and online services

Hand tools and fabrication machines

Story

Introduction

Function

Step 1: Configuring Thunderboard Sense 2

Step 2: Libraries for Raspberry Pi

Step 3: Setting up ThingSpeak

Step 4:Setting up Thunderboard Sense 2

Step 5: Programming Raspberry Pi

Step 6: Data Collection

Step 7: Google Maps

Step 8: Amazon Web Services Chatbot

Step 9: Twilio Setup

Conclusion

Custom parts and enclosures

3D Printed Case

Schematics

Casing

Code

Raspberry Pi Code

Credits

Ashwin Sridhar

Comments

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Covnent: Community Driven Map Platform for COVID-19

Covnent: Community Driven Map Platform for COVID-19

Things used in this project

Hardware components

Software apps and online services

Hand tools and fabrication machines

Story

Introduction

Function

Step 1: Configuring Thunderboard Sense 2

Step 2: Libraries for Raspberry Pi

Step 3: Setting up ThingSpeak

Step 4:Setting up Thunderboard Sense 2

Step 5: Programming Raspberry Pi

Step 6: Data Collection

Step 7: Google Maps

Step 8: Amazon Web Services Chatbot

Step 9: Twilio Setup

Conclusion

Custom parts and enclosures

3D Printed Case

Schematics

Casing

Code

Raspberry Pi Code

Credits

Ashwin Sridhar

Comments

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