Published October 26, 2019

Indoor Temperature Monitoring System

Internet of Things based temperature monitor with predictive capabilities & anomaly detection using ML algorithms.

AdvancedFull instructions provided808

Things used in this project

Hardware components

Bolt IoT Bolt WiFi Module

LM35 Temperature Sensor

Jumper wires (generic)

Breadboard (generic)

Micro-USB to USB Cable (Generic)

Portable Power Bank

Software apps and online services

Bolt IoT Bolt Cloud

Bolt IoT Android App

Mailgun

Ubuntu Core

Story

Temperature monitoring systems are incredibly useful tools to monitor and manage the heat levels at remote telecom sites. While "too much heat" is the most common problem when dealing with computer systems, "too cold" is also a very real problem in some climates.

A temperature monitoring system will help you to avoid heat buildup at your telecom locations, like huts and other network nodes. Temperature monitoring is also important at less industrial "IT" locations like server rooms and data centers. The right temperature monitoring system will enable you to keep track of critical temperatures at all of your sites that contain important computer gear.

I made a light-weight IOT-based temperature monitoring system which predicts the temperature of refrigerator & sets boundary to the temperature limit. Further, it senses the change in temperature whenever someone opens the door and alert by means of Email. Here I have used 'refrigerator' just to have a temperature-measuring atmosphere, resembling cooling chamber in most industries.

Working:

1.Prediction Data:-

I have used Polynomial Regression Algorithm of Machine learning, which helps fit a non-linear curve to a given data-set. The trend can be then used to understand where other data points might lie.

Recording Temperature readings for 2 hours inside refrigerator.

Temperature Prediction

It outputs the coefficients of the function of the form:

which most closely resembles the trend in the input data. This number tells the Polynomial Visualizer how many elements should be present in the function.

2.Setting Boundaries:-

From the observations, maximum & minimum temperature limits are set.

code for setting boundaries.

LM35 is an integrated analog temperature sensor whose electrical output is proportional to Degree Centigrade.Just divide the analog pin output by 10.24 to get a value in degree centigrade. This algorithm checks whether this reading is in range or otherwise it sends an alert message through EMAIL for early actions.

3. Anomaly Detection:-

Another Feature is it detects an anomaly, i.e., whenever someone opens fridge door an alert email, saying 'Someone has opened the door' is sent through Mailgun. This is done by using Z-score analysis algorithm of machine learning.It basically works to detect any sudden change in the sensor value.

code for anomaly detection

We calculate the Z score (Zn) for the data and use it to calculate the upper and lower threshold bounds required to check if a new data point is normal or anomalous. by formula,

where Mn is taken as mean Vi is variance and Zn as Z-score. Frame-size & Multiplication factor are assigned values in separate.py file, accompanied by other credentials required for mailgun.

All repeats again and again with a gap of 10 secs.

A short video of working of project:

THANK YOU:)

import conf, json, time, math, statistics
from boltiot import Email, Bolt

max_lim = 299
min_lim = 10

def compute_bounds(history_data,frame_size,factor):
    if len(history_data)<int(frame_size) :
        return None

    if len(history_data)>int(frame_size) :
        del history_data[0:len(history_data)-int(frame_size)]
    Mn=statistics.mean(history_data)
    Variance=0
    for data in history_data :
        Variance += math.pow((data-Mn),2)
    Zn = int(factor) * math.sqrt(int(Variance) / int(frame_size))
    High_bound = history_data[int(frame_size)-1]+Zn
    Low_bound = history_data[int(frame_size)-1]-Zn
    return [High_bound,Low_bound]
    
tempo = Bolt(conf.API, conf.DEVICE_ID)
mailer = Email(conf.MAILGUN, conf.SANDBOX, conf.SENDER_EMAIL, conf.RECIPIENT_EMAIL)
history_data=[]

while True:
    print("Collecting sensor value")
    response = tempo.analogRead('A0')
    data = json.loads(response)
    print("Sensor value " +str(data['value']))
    try:
        sensor_value = int(data['value'])
        if sensor_value>max_lim or sensor_value<min_lim:
           print("Making request to Mailgun to send an email")
           respo = mailer.send_email("Alert: Temprature exceeded thesold limit.")
           response_text = json.loads(respo.text)
           print("Response received from Mailgun is: " +str(response_text['message']))
    except Exception as e:
        print ("Error occured: Below are the details")
        print (e)
    time.sleep(10)

    bound = compute_bounds(history_data,conf.FRAME_SIZE,conf.MUL_FACTOR)
    if not bound:
        required_data_count=int(conf.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 ("Someone has opened the door. Sending an Email")
             response1 = mailer.send_email("Alert","Someone opened fridge door")
             print("This is the response ",response1)
        history_data.append(sensor_value);
    except Exception as e:
          print ("Error",e)
    time.sleep(10)

Credits

Divnoor Singh

1 project • 0 followers

Indoor Temperature Monitoring System

Things used in this project

Hardware components

Software apps and online services

Story

Working:

1.Prediction Data:-

2.Setting Boundaries:-

3. Anomaly Detection:-

Code

Temperature-monitor

Prediction

Credits

Divnoor Singh

Comments

Embed the widget on your own site

Indoor Temperature Monitoring System

Indoor Temperature Monitoring System

Things used in this project

Hardware components

Software apps and online services

Story

Working:

1.Prediction Data:-

2.Setting Boundaries:-

3. Anomaly Detection:-

Code

Temperature-monitor

Prediction

Credits

Divnoor Singh

Comments

Related channels and tags