STEP 1: Creating an account on the IoT cloud
STEP 2: Hardware Connection
STEP 3: Creating and linking the product to the device
STEP 4: Create a Twilio account
STEP 5: Write the code

Published April 29, 2020

Light Monitoring System with Message Alert using Bolt IOT

This project monitors the light of the room continuously and informs the user whenever the light is turned on or turned off with SMS.

IntermediateFull instructions provided5 hours1,001

Light Monitoring System with Message Alert using Bolt IOT

Things used in this project

Hardware components

LDR, 5 Mohm

Resistor 10k ohm

Bolt IoT Bolt WiFi Module

Breadboard (generic)

Jumper wires (generic)

USB-A to Mini-USB Cable

Software apps and online services

Bolt IoT Bolt Cloud

Twilio SMS Messaging API

Story

Let's say you are a person who works for a secret organization. You are given an important and highly confidential file. Since many people are after that file, you decide to keep it at your place. But you want to monitor the place such that whenever anyone enters the room and switch on the light, you will be notified.

In project uses "Z-Score Analysis" to predict the turn on and turn off of the light.

Let's dive in to learn how to create such a system.

STEP 1: Creating an account on the IoT cloud

Go to www.cloud.boltiot.com.
Enter your details and create an account.
After creating login into your account.

STEP 2: Hardware Connection

Connect one lead of LDR to the 3v3 pin of the Bolt IoT module.
Connect another lead of LDR to the A0 pin of the Bolt module.
Connect one leg of 10K ohm resistor to the GND pin.
Connect another leg of the resistor to the A0 pin.
Make sure all connections are properly made without any loose contacts.

STEP 3: Creating and linking the product to the device

Login in to your Blt Cloud account.
Click on the product tab on the left side and click "Build".
Give a suitable Product icon and product name.
Select "Input device", as LDR is an input device.
Select "GPIO" to collect data and then click "Done".
Next click "Configure this product" (looks like tools) on the right side.
You will get a "Product Setup" page which has "Hardware" and "Code" tabs.
In the Hardware tab, select A0 and a variable name.
In the Code tab, write the code to create a chart in.js extension and click "Save".
Link your product with your device by clicking the "Link" icon.

STEP 4: Create a Twilio account

Go to "https://www.twilio.com/" and create a free account.
After creating the account make sure you note down the Twilio SID, AuthToken, ToNumber, etc..

STEP 5: Write the code

Open the "Digital Ocean" or any other platform where you write python code. You can even use "Pycharm" or other IDE but make sure you install necessary packages.

NOTE: Use "pip install boltiot" in Pycharm to get the bolt packages.

Create a configuration file. I've named it "confy.py".

confy.py

The first four parameters are Twilio details and the next two are your Bolt device details which you can get in your Bolt Cloud.

Frame Size decides the threshold value ie., it is the number of data required for deciding the threshold value fo the analysis. I've kept frame size as 3 for quick calculation but you can keep more for better analysis.

The algorithm for the main code is as follows.

1) Fetch the latest sensor value from the Bolt device.

2) Store the sensor value in a list, that will be used for computing z-score.

3) Compute the z-score and upper and lower threshold bounds for normal and anomalous readings.

4) Check if the sensor reading is within the range for normal readings.

5) If it is not in range, send the SMS.

6) Wait for 10 seconds.

7) Repeat from step 1.

The full code for the above algorithm is given under the "Code" section and the screenshot is given here.

1 / 2

The output of the above code is

OUTPUT

Twilio SMS alert output

SMS Alert

Code

import confy, json, time, math, statistics
from boltiot import Sms, Bolt
def compute_bounds(history_data,frame_size,factor):
    if len(history_data)<frame_size :
        return None

    if len(history_data)>frame_size :
        del history_data[0:len(history_data)-frame_size]
    Mn=statistics.mean(history_data)
    Variance=0
    for data in history_data :
        Variance += math.pow((data-Mn),2)
    Zn = factor * math.sqrt(Variance / frame_size)
    High_bound = history_data[frame_size-1]+Zn
    Low_bound = history_data[frame_size-1]-Zn
    return [High_bound,Low_bound]

mybolt = Bolt(confy.API_KEY, confy.DEVICE_ID)
sms = Sms(confy.SSID, confy.AUTH_TOKEN, confy.TO_NUMBER, confy.FROM_NUMBER)
history_data=[]

while True:
    response = mybolt.analogRead('A0')
    data = json.loads(response)
    if data['success'] != 1:
        print("There was an error while retriving the data.")
        print("This is the error:"+data['value'])
        time.sleep(10)
        continue

    print ("This is the value "+data['value'])
    sensor_value=0
    try:
        sensor_value = int(data['value'])
    except e:
        print("There was an error while parsing the response: ",e)
        continue

    bound = compute_bounds(history_data,confy.FRAME_SIZE,confy.MUL_FACTOR)
    if not bound:
        required_data_count=confy.FRAME_SIZE-len(history_data)
        print("Not enough data to compute Z-score. Need ",required_data_count," more data points")
        history_data.append(int(data['value']))
        time.sleep(10)
        continue

    try:
        if sensor_value > bound[0] :
            print ("The light level increased suddenly. Sending an SMS.")
            response = sms.send_sms("Someone turned on the lights")
            print("This is the response ",response)
        elif sensor_value < bound[1]:
            print ("The light level decreased suddenly. Sending an SMS.")
            response = sms.send_sms("Someone turned off the lights")
            print("This is the response ",response)
        history_data.append(sensor_value);
    except Exception as e:
        print ("Error",e)
    time.sleep(10)

Credits

Sandhya Mohan

1 project • 0 followers

Light Monitoring System with Message Alert using Bolt IOT