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ProjectsGeek

Streetlight Monitoring System IOT Project

February 11, 2018 by ProjectsGeek 2 Comments

Streetlight Monitoring System IOT Project

 

Objective

To create a smart street light monitoring system based on Internet of Things (IoT) using Raspberry-pi to make street lighting more efficient, safe and affordable.

Project Overview

Street light is the important part and cautious need for any institution, city, municipality during night times. The unfortunate thing is that, everybody able to utilize the outcome of the streetlight, but too much cost and maintenance makes the traditional street light system as not efficient. From traditional system, nowadays we are moving towards a smart street light system, where it is connected through the internet. This new system increases the efficiency, safety and low wastage of energy.

Existing System

Existing street light monitoring and controlling system focuses on reducing power consumption and fault detections without considering the traffic rate. It considers daily power usage, faulty things happening in the environment. The existing system architecture is shown in below figure.

Streetlight Monitoring System

Working Principle

  • In the street light environment, regular routine works will be happen. Additional to routine regular works, the sensor integrated with street light will detect the power level and faulty things.
  • All the activities that happening will be transferred to the web server through internet. This digital data is stored in the database for future analysis.
  • The normal range power level will be stored in the database. If incoming digital power level data is mismatched from the normal power level, then this data will be used to control the power level in the street light.
  • Similarly faulty activities also notified in the user interface for the further action by the responsible persons.

Advantages

  • Reduce the energy wastage by monitoring the power level.
  • Faulty activities avoided and rectified quickly.

Disadvantages

  • It does not consider the traffic level.
  • It does not consider the latest mobile application need and usage level by common people.

Proposed System

The proposed system smart street light monitoring and controlling system focus on reducing power consumption and fault detection’s with considering the traffic rate in that environment. All the reporting is connected to the mobile application. The proposed system architecture is shown in following figure.

Working Principle

  • In the smart street light environment, additional to regular routine works, 3 sensors fitted for the purpose of traffic detection, power level detection and faulty activity detector.
  • In additional to an existing system, traffic level detector is used to monitor the traffic level in the environment and based on that level it controls the energy level.
  • All the activities are converted to report format and this information is displayed in the mobile application as graphical format.

Advantages

  • Controls and monitors the light energy based on traffic level.
  • Graphical reporting in mobile application access, allows efficient monitoring and controlling.

Components / Software /Hardware’s Required:

  • Python
  • Web Server
  • MySQL Database
  • GUI, User Interface
  • Raspberry-pi
  • Street Light
  • Traffic Monitoring Sensor
  • Android

Download Abstract

Other Projects to Try:

  1. Traffic Signal Monitoring & Controlling System IOT Project
  2. Garbage Monitoring System Internet of Things Project
  3. Smart Gas Pipe Leakage Detector System IOT Project
  4. Smart Toll Booth Management System IOT Project
  5. Smart Voting System Project IOT Project

Filed Under: Internet of Things Project Tagged With: Internet of Things Project

Smart Gas Pipe Leakage Detector System IOT Project

February 9, 2018 by ProjectsGeek Leave a Comment

Smart Gas Pipe Leakage Detector System IOT Project

 

Overview

Gas pipe leakage causes some unwanted negative effects in home and industry. It leads to losses and it is a bigger threat in terms of fire accident.  Usage of sensors at every section will lead to higher budget system. So it is important that the system is efficient in all ways. This system concentrates on managing the toll booth system in a smart way using Internet of Things(IoT).

Existing System

The current era gas pipe leakage system in most places nowadays is based on  more human intervention process. Many existing system projects concentrate on security issues, leakage and fire accidents. The system sends an SMS to the mobile and generate alarms.

Advantages

  • Existing system creates alarm to avoid losses.
  • Compared to the traditional manual checking of leakage, this system is efficient.

Disadvantages

  • At every step of the process, some types of human work are needed.
  • It does not consider the room temperature.

Proposed System

The proposed system, focuses on building the completely automatic system. Internet of Things (IoT) and mobile application integrated with existing system architecture to get the best outcome for this system. The proposed system architecture is shown in below architecture:

Gas Pipe Leakage Detector System

In the proposed system we have designed smart gas pipe leakage system using IoT. This system concentrates on detection of fuel level, gas and alerting the people in that environmentwith buzzer. The features of the proposed system are,

  • Sensing the environment temperature, to avoid the fire accidents.
  • Automatic booking of cylinders by automatic monitoring of current level of the cylinder.
  • Alerting the user through SMS.
  • Notification is sent to the user when gas level reaches user threshold value.
  • Notification with buzzer is created during the excess temperature than usual one in the surrounding environment.

 Advantages

  • The automated system leads to less human intervention.
  • Tracking of gas pipe leakage is more efficient with this new system.

 Disadvantages

  • During the setup of the first time process, the cost will be somewhat higher. But after that only only maintenance needed.So no further investment required.

 System Requirements

  • Arduino boards
  • GSM Module
  • Buzzer
  • Power Supply
  • Gas Sensor
  • Mobile application

Other Projects to Try:

  1. Garbage Monitoring System Internet of Things Project
  2. Smart Toll Booth Management System IOT Project
  3. Smart Voting System Project IOT Project
  4. Streetlight Monitoring System IOT Project
  5. Vehicle Parking System IOT Project

Filed Under: Internet of Things Project Tagged With: Internet of Things Project

Twitter Data Sentimental Analysis Using Hadoop Project

February 7, 2018 by ProjectsGeek Leave a Comment

Twitter Data Sentimental Analysis Using Hadoop

 

Objective

To analyze the sentiments of people as positive, negative or neutral using Hadoop for the Demonetization data to extract interesting patterns.

Project Overview

The Twitter Data Sentimental Analysis hadoop project is to analyse the sentiment by gathering tweets from different people and to check whether the people happy with the government scheme or not. Twitter Sentiment Analysis is the process of determining Tweets is positive, negative or neutral.It is known as opinion mining.

The data set is collected from tweets of citizens from twitter. Obviously data are in an unstructured format. Also a huge amount of tweets is generated. So here the big data come into action. The big data concepts like, Hadoop, MapReduce, Hadoop Distributed File System widely used for this type of applications. 

Proposed System

The proposed Twitter Data Sentimental Analysis hadoop project system concentrates on sentiment analysis of the noteban data using hadoop. The sentiments collected from the twitter are classified as positive, negative, neutral. Positive opinion words are used to express desired states for the government scheme while negative opinion words are used to express undesired states for the government scheme. The proposed system architecture is shown in the figure.Twitter Data Sentimental Analysis

Step 1: Twitter API

Twitter API is used as an authentication API to extract the tweets related noteban data.

Step 2: Data Preparation

The data are collected from twitter using Hadoop through twitter API for Indian government announcement noteban. Punctuation, stop words, special characters are removed using data preprocessing techniques.

  • Tokenization:
  • Lexical Dictionary
  • Acronym Dictionary
  • Emoticon Dictionary
  • Stop Words Dictionary

Tokenization

Tweets extracted from twitter are divided into into tokens. This is known as tokenization process. For example, ‘In the short run it took many life & shattered many household’is divided down into ‘In’ , ‘the’, ‘short’, ‘run’, ‘it’, ‘took’, ‘many’, ‘life’, ‘&’, ‘shattered’, ‘many’, ‘household’.

Lexical Dictionary:It is used to match the words in the tweet.

Acronym Dictionary:It is used to expand the abbreviations and acronyms. This dictionary will create words which are used for further analysis.

Emoticon Dictionary: it is used to convey the meaning for emoticon.

Stop Words Dictionary:The words which do not have any importance for sentiment analysis. So this word is identified and removed. Example: a, an, the, as, etc.,

Step 3: Sentiment Analysis

The sentiments collected from the twitter are classified as positive, negative, neutral. This sentiment analysis is performed statewise.

Example for positive tweet:

New india is born.

Example for negative tweet:

In the short run it took many life & shattered many household.

Step 4: Data Visualization

After the sentiment analysis, the analyzedsentiments are visualized using bar chart.

Software Requirements

  • Linux OS
  • MySQL
  • Hadoop & MapReduce
  • Twitter API Account

Hardware Requirements

  • Hard Disk – 1 TB or Above
  • RAM required – 4 GB or Above
  • Processor – Core i3 or Above

Technology Used

  • Big Data – Hadoop

Other Projects to Try:

  1. Sentiment Analysis on E-Commerce Sites | Data Analytics
  2. Document Level Sentiment Analysis Using Opinion Mining
  3. Climatic Data analysis using Hadoop Project
  4. Facebook Data Analysis Using Hadoop Project
  5. Flight History Analysis Using Hadoop Project

Filed Under: Hadoop Projects Tagged With: Hadoop Projects

Web Page Ranking With Hadoop Project

February 5, 2018 by ProjectsGeek Leave a Comment

Web Page Ranking With Hadoop

 

Objective

The objective of the Web Page Ranking With Hadoop project is ranking the web pages using Hadoop and MapReduce based on the keyword to improve the accuracy of the web page search results for the search query by the user.

Project Overview

The number of web pages in the internet is growing rapidly. So there is a need for analyzing that much of internetdata to get any valuable insight to return the best search results. The large data processing is needed to rank a webpage based on the keywords. Hence Hadoop framework is the best choice for data processing for storing all the web pages and for ranking web pages.Web Page ranking is used to define the relevance of the web page to the user query.

Searching the relevant information using links is one of the difficult tasks. It consumes lot of time and it will not produce exact or accurate results.In order to improve the efficiency in the web page searching and retrieving, improvement in existing system and an efficient algorithm based on keyword is needed to rank the web pages. Hadoop data processing framework is used for storing and retrieving web related data and page rank algorithm is used for ranking web pages.

Existing System

In the traditional web page ranking, web page searching is done based on the hyperlinks in the web page. It provides search result to the user, but it does not return the user expected search result.

Proposed System

The proposed Web Page Ranking With Hadoop project system rank the web pages based on the keywords strength (Number of keywords) in the web page document. MapReduce concept is used here to rank the web pages based on Mapper and Reducer. The web page with highest number of keywords in the document is returned to the user query. This process increases the efficiency of the search result and less time consuming.

The proposed Web Page Ranking With Hadoop project system focuses on creating best page ranking algorithm for Web pages using Hadoop. The proposed system architecture is shown in the figure.Web Page Ranking With Hadoop

Module 1: Data Preparation

Document data & Hadoop large data processing: Web page data are stored in the text format. Large numbers of text files are stored and processed using Hadoop framework.

Module 2: MapReduce

MapReduceconsists of 4 tasks, loading, parsing, transforming and filtering to rank the web pages.

Module 3: Page Ranking Algorithm

This algorithm focuses on ranking the web pages based on the keyword strength.

Module 4: Results Page

The final web page result is displayed in the user interface with the top level web page results to the user based on the query requested.

Web Page Ranking With Hadoop Benefits

  • Fast and accurate web page results
  • Less time consuming

Software Requirements

  • Ubuntu OS
  • MySQL
  • Hadoop&MapReduce
  • JDK

Hardware Requirements

  • Hard Disk – 1 TB or Above
  • RAM required – 8 GB or Above
  • Processor – Core i3 or Above

Technology Used

  • Big Data – Hadoop

Other Projects to Try:

  1. Search Engine using Python Project
  2. Wiki Page Ranking With Hadoop Project
  3. Aadhar Based Analysis using Hadoop Projects
  4. Big Data Hadoop Projects Ideas
  5. Airline On-Time Performance Hadoop Project

Filed Under: Hadoop Projects Tagged With: Hadoop Projects

Flight History Analysis Using Hadoop Project

February 4, 2018 by ProjectsGeek Leave a Comment

Flight History Analysis Using Hadoop

 

Objective

  • To analyze flight history data, which provides the reasons for flight delays, negative reviews by passengers.

Project Overview

Flight delays are a important issue in the flight industry, because it will lead to financial crisis in the business. This project identifies the factors influence the occurrence of flight delays. Research survey indicates that every year about 20% of flights are delayed or cancelled. It costs in very big way for both travelers and airlines.

The project is to analyze flight data history by gathering data from official web portal. The data that’s maintained in web portal is big in size and it is increasing everyday. So obviously big data analytics are the best way to analyze the data and extract the useful knowledge from the data set. Hadoop, MapReduce, Hadoop Distributed File System (HDFS) and HIVEare used here in this project as a big data concepts. 

Proposed System

The proposed Flight History Analysis Using Hadoop system concentrates on analyzing flight data history to identify the reasons for negative feedback from users and reasons for flight delays. The proposed system architecture is shown in the figure.Flight History Analysis Using Hadoop

                                                                                             Figure: Proposed System Architecture

Flight History Analysis Using Hadoop Queries

  • Reasons for flight delay
  • Reasons for negative feedback
  • How to improve the business model?

 

Module 1:Data Collection

The required data set is collected from the https://www.kaggle.com/open-flights/flight-route-database. The attributes of the data set are year, month, day, day of the week, airline name, origin airport, destination airport, scheduled departure, scheduled arrival, departure time, arrival time, departure delay, arrival delay and distance.

Module 2: Data Preparation

The collected raw data set is loaded into HDFS directory. This raw data is vulnerable to impurity data like inconsistent and noisy. So before applying machine learning techniques, first data cleaning methods are applied to the missing data and noisy data.

Module 3: Machine Learning

The prepossessed data set is divided into a training set and test set. Here, the training set is used to create models, while test set is used to test the accuracy of the machine learning algorithm. If the accuracy is acceptable, then this applies to the future data.

Machine Learning Classification identifies

  • Which attributes impact the flight delay?
  • What are the main reasons for negative feedback from passengers?
  • Is there any relation between variables that causes the flight delay?
  • What kind of offers can be provided for particular segmentation of passengers?
  • What kind of things need to be introduced to attract the new customers?

Module 4: Data Visualization

The extracted knowledge and patterns are visualized using Tableau – Business Intelligence tool.

Flight History Analysis Using Hadoop Benefits

  • This project will give the exact reason for the flight delay, which will be the important factor in the business.
  • Major financial losses can be avoided, with the usage of this project in real time.

Software Requirements

  • Linux OS
  • MySQL
  • Hadoop & MapReduce
  • Tableau

Hardware Requirements

  • Hard Disk – 500 GB or Above
  • RAM required – 4 GB or Above
  • Processor – Core i3 or Above

Technology Used

  • Big Data – Hadoop
  • Business Intelligence

Other Projects to Try:

  1. Airline On-Time Performance Hadoop Project
  2. Big Data Hadoop Projects Ideas
  3. Climatic Data analysis using Hadoop Project
  4. Facebook Data Analysis Using Hadoop Project
  5. Twitter Data Sentimental Analysis Using Hadoop Project

Filed Under: Hadoop Projects Tagged With: Hadoop Projects

Facebook Data Analysis Using Hadoop Project

February 1, 2018 by ProjectsGeek Leave a Comment

Facebook Data Analysis Using Hadoop

Objective

To analyze the Facebook data using Hadoop for the purpose of better decision making in the business.

Project Overview

Smart phone without social media usage in daily lifestyle of people is unthinkable. That much effect has been created in the lifestyle of people by smartphone and social media. There are many social media such as Facebook, Twitter, etc., As per 2017 statistics, nearly 1.37 billion daily active users for Facebook. Every user contributes some type of data to in structured or semi-structured or unstructured data format. Business  owners utilize this data to understand customer need and their behavior to make profit in their business. Facebook data analysis is the process of collecting, analyzing Facebook data and visualizing extracted results to the end user.

The user data is collected from Facebook based on their activities. User behavior, number of likes, number of posts, type of posts, their comments, etc. are stored by the database server. Comments by the user in unstructured formats, while other data in structured and semi-structured format. Petabytes of data is generated by Facebook users. So Hadoop, MapReduce and related big data concepts used in this project to analyze the data.

Proposed System

The proposed system focuses on analyzing sentiments of Facebook users using Hadoop. The user sentiments collected are categorized into positive, negative, neutral.The proposed system architecture is shown in the figure.

Facebook Data Analysis Using Hadoop

                                                Figure: Proposed System Architecture

 

Module 1: Facebook API

Facebook API is used as an authentication API to extract the user contents related to the query requested.

Module 2: Data Pre-Processing

Data Collection: The data are collected from Facebook using Hadoop through the Facebook API based on the requested query.

Data Preparation: The collected data consists of different emotions, stop words, acronyms, etc. But during analysis this type of data needs to be converted into the proper format to extract sentiments from the user behavior.

  • Tokenization
  • Various Dictionaries
    • Acronym Dictionary
    • Stop Words Dictionary
  • Emoticon

Consider one of the Facebook posts regarding new mobile features. Users opinion about the new phone might be positive or negative or neutral.

Example for Positive Sentiment

Looks are awesome.Battery backup is excellent. Camera is good.The display light quality is good.

Example for Neutral Sentiment

Although this is good mobile, looks good, but Problem is that it doesn’t provide separate Space for dual SIM & memory card together.

Example for Negative Sentiment

Not good one as expected. Camera quality very poor.

Tokenization

Comments extracted from Facebook are divided into tokens. This is known as tokenization process. For example, ‘Looks are awesome. Battery backup is excellent. Camera is good. The display light quality is good.’is divided down into ‘Looks’, ‘are’, ‘awesome’, ‘.’, ‘Battery’, ‘backup’, ‘is’, ‘excellent’, ‘.’, ‘Camera’, ‘is’, ‘good’, ‘.’, ‘The’, ‘display’, ‘light’, ‘quality’, ‘is’, ‘good’, ‘.’

 Acronym Dictionary: It is used to give the required acronym for the words, if needed.

Stop Words Dictionary: It is used to remove the unrelated words in the sentiment analysis process. Example: A, An, The, Has, Are, Is.

Emoticon:This is used to detect the emoticons for the purpose of classifying the comment as positive or negative or neutral.

 Module 3: Sentiment Analysis

The user sentiments collected from the Facebook are categorized into positive, negative, neutral. This sentiment analysis can be performed for different purposes based on the business objectives.

Module 4: Data Visualization

After the Facebook sentiment analysis, the extracted and analyzed sentiments are visualized using Tableau.

Software Requirements

  • Linux OS
  • Hadoop & MapReduce
  • Facebook API
  • HIVE
  • Tableau

Hardware Requirements

  • Hard Disk – 1 TB or Above
  • RAM required – 4 GB or Above
  • Processor – Core i3 or Above

Technology Used

  • Big Data – Hadoop

Other Projects to Try:

  1. Sentiment Analysis on E-Commerce Sites | Data Analytics
  2. Document Level Sentiment Analysis Using Opinion Mining
  3. Climatic Data analysis using Hadoop Project
  4. Twitter Data Sentimental Analysis Using Hadoop Project
  5. Flight History Analysis Using Hadoop Project

Filed Under: Hadoop Projects Tagged With: Hadoop Projects

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