The document describes a proposed system called FogDrive Disaster Backup as a Service that uses fog computing to provide backup for cloud server data. It aims to provide an easy-to-use and secure backup system. The system would use cloud and encryption techniques to backup and store data on a FogDrive server located within the local network. It would include OpenPGP encryption of backups and allow users to manage their encrypted backups through a dashboard. The system architecture was presented, including modules for the cloud provider, FogDrive server, and backup software service.

1. FogDrive Disaster Backup as a Service for
Cloud Server using Fog Computing
Batch Members:
Keerthana K (712818104019)
Rohit Prakash (712818104032)
Sharmila S M (712818104037)
Guided By,
Prof. D. Suganthi M.E,(Ph.D.),
Department of CSE

2. Objective
The purpose of online backup is simple and
straightforward: to protect the information –
whether it's business data or personal – from the
risk of loss associated with user error, hacking,
or any other kind of technological disaster.

3. Problem Identified
Data loss can result from user error, a malicious
attack, unauthorized access or breach, incomplete
data sync, incompatible software, power outages,
or disasters like extreme weather, flooding, and
fire.

4. Existing System
• TrustyDrive is a dmizes the storage needs using the sec-
cs data structure for deduplication of flat contents.
• ExpanStor is another Multi-Cloud storage system with
dynamic data distribution. It applies a Client-Server
architecture instead of a pure Client-Based
implementation.

5. Proposed System
• Proposed FogDrive to provide an easy-to-use, highly
secure, and reliable backup system using state-of-the-art
Cloud and encryption techniques.
• The FogDrive BDaaS provider manages the entire backup
process, providing the client with a user-friendly dashboard
to manage their data in the cloud and FogDrive.
• OpenPGP(Pretty Good Privacy (PGP)) Scheme based
Encrypted backups allow you personal control over your
data by locking it up with passwords meaning only your
authorized users can access it.

6. Hardware specification
• Processors: Intel® Core™ i5 processor 4300M at
2.60 GHz or 2.59 GHz (1 socket, 2 cores, 2
threads per core), 8 GB of DRAM.
• Disk space: 320 GB.
• Operating systems: Windows® 10, macOS*, and
Linux*.

7. Software Specification
• Server Side : Python 3.7.4(64-bit) or (32-bit)
• Client Side : HTML, CSS, Bootstrap
• IDE : Flask 1.1.1
• Back end: MySQL 5.
• Server : Wamp server 2i
• DL DLL : TensorFlow, Pandas, SiKit Learn

8. System Architecture – Fog Drive Data
Backup
Login
Upload Data
Encrypt Data
Generate ISO
File
Compress Data
Received Data
Backup Data
Update
FogDrive
Store Data
Fog Drive
Open PGP
DBaaS Backup
Data Owner
Cloud Front End Interface
UI
Cloud
Backend
Server

9. System Architecture – DBaaS Data
Recovery
Forward Req toFD
Server Status
Received Req
Check Ser.Avai
Decompress ISO
Request Data
Check SStorage
Fog Drive
Response Data
Request Data
DBaaS Web Interface
Retrieve Req.Data
Response Data
Decrypt Data
Data Owner
Data User

10. Modules
1. Cloud Service Provider
2. FogDrive
3. DBaaS Software Service
3.1. PGP Module
3.2. FD ISO Backup
3.3. Backup Recovery

11. 1. Cloud Service Provider
• Data owner or user are able to upload, download, or
modify their data interactively and very fast.
• Its integrated with the FogDrive for Cloud Disaster
Backup

12. 2. FogDrive
• The FogDrive is a client side storage server.
• Edge or client devices and the FogDrive are in
the same Local Area Network (LAN)

13. 3. DBaas Software Service
• This is the backup system that runs on the
FogDrive.
• The DBaas offers a simple but safe backup
interface to the edge nodes, whereas completely
protecting the data on a distributed Cloud storage.

14. 3.1. Open PGP
• The data delta coming from the cloud is
encrypted using openPGP scheme.
• Pretty Good Privacy (PGP) is an encryption
system used for both encrypting and
decrypting sensitive files.

15. 3.2. FD ISO Backup
● Backup the data in compressed manner and store
it in FogDrive with minimal storage space

16. 3.3. Disaster Backup Recovery
• Cloud data retrieval and recovery of the entire
data can be conveniently performed using DBaaS
from Fog Drive.
• Then, DBaaS can search for the metadata on all
the configured CSPs and construct the backup
chain.

17. 4. Performance Evaluation
• Time taken to retrieve and restore the user
data to FogDrive at the time of cloud disaster.
• Time taken to compress and decompress the
data.

18. Result Screenshot

25. Coding
Testing Phase
from flask import Flask
from flask import Flask, render_template, Response, redirect,
request, session, abort, url_for
from cameTesting Phase
from flask import Flask
from flask import Flask, render_template, Response, redirect,
request, session, abort, url_for
from camera import VideoCamera
@app.route('/verify_face2',methods=['POST','GET'])
def verify_face2():
msg=""
ss=""
uname=""
act=""
if request.method=='GET':

26. act = request.args.get('act')
#if 'username' in session:
# uname = session['username']
ff2=open("un.txt","r")
uname=ff2.read()
ff2.close()
cursor = mydb.cursor()
cursor.execute('SELECT * FROM register WHERE card = %s',
(uname, ))
account = cursor.fetchone()
name=account[1]
mobile=account[3]
print(mobile)
email=account[4]
vid=account[0]
shutil.copy('faces/f1.jpg', 'faces/s1.jpg')
cutoff=15
img="v"+str(vid)+".jpg"
cursor.execute('SELECT * FROM vt_face WHERE vid = %s', (vid, ))
dt = cursor.fetchall()

27. hash0=imagehash.average_hash(Image.open("static/frame/"+rr[2]))
hash1 = imagehash.average_hash(Image.open("faces/s1.jpg"))
cc1=hash0 - hash1
print("cc="+str(cc1))
if cc1<=cutoff:
ss="ok"
break
else:
ss="no"
if ss=="ok":
act="2"
msg="Face Verified"
print("correct person")
return redirect(url_for('login', msg=msg))
else:
act="1"

28. msg="Face not Verified"
print("wrong person")
mess="Someone Access your account"
url2="http://localhost/atmpin/img.txt"
ur = urlopen(url2)#open url
data1 = ur.read().decode('utf-8')
idd=int(data1)
url="http://iotcloud.co.in/testsms/sms.php?sms=linkatmpin&name
="+name+"&mess="+mess+"&mobile="+str(mobile)+"&id="+str(id
d)
print(url)
webbrowser.open_new(url)
return render_template('verify_face2.html',msg=msg,act=act)

29. def DCNN_process(self):
test = train_data_preprocess.flow_from_directory(
'dataset/test',
target_size = (128,128),
batch_size = 32,
class_mode = 'binary')
## Initialize the Convolutional Neural Net
# Initialising the CNN
cnn = Sequential()
# Step 1 - Convolution
# Step 2 - Pooling
cnn.add(Conv2D(32, (3, 3), input_shape = (128, 128, 3), activation
= 'relu'))
cnn.add(MaxPooling2D(pool_size = (2, 2)))

30. # Step 3 - Flattening
cnn.add(Flatten())
# Step 4 - Full connection
cnn.add(Dense(units = 128, activation = 'relu'))
cnn.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy',
metrics = ['accuracy'])
history = cnn.fit_generator(train,
steps_per_epoch = 250,
epochs = 25,
validation_data = test,
validation_steps = 2000)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')

31. plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
test_image = image.load_img('dataset', target_size=(128,128))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
result = cnn.predict(test_image)
print(result)
if result[0][0] == 1:
print('feature extracted and classified')
else:

32. Conclusion
• As important network infrastructures to support data storage and
service delivery for worldwide users, cloud data centers are
facing great threaten by frequent disasters around the world and
thus the survivability of cloud data centers becomes a critical
issue.
• This project introduces FogStore -Disaster Backup as a Service
and FogDrive, a new data backup system based on Cloud and
Fog Computing.
• overcomes the problems of multi-Cloud using the Fog
Computing paradigm.
• System users can easily and securely backup, restore, and modify
their data without caring about the sophisticated operations to
protect and secure the data on Temporary-Cloud storage

33. References
1. J. Fu, Y. Liu, H. Chao, B.K. Bhargava, Z. Zhang, Secure data
storage and searching for industrial IoT by integrating fog
computing and cloud computing, IEEE Trans. Ind. Inf. 14 (10)
(2018) 4519–4528.
2. S. K. Monga, S. K. Ramachandra, and Y. Simmhan, ``ElfStore: A
resilient data storage service for federated edge and fog resources,''
in Proc. IEEE Int. Conf. Web Services (ICWS), Jul. 2019, pp. 336-
345.
3. R. Mayer, H. Gupta, E. Saurez, and U. Ramachandran, ``FogStore:
Toward a distributed data store for fog computing,'' in Proc. IEEE
Fog World Congr. (FWC), Oct. 2017, pp. 1- 6.
4. O. A. Nasr, Y. Amer, and M. AboBakr, ``The, `droplet': A new
personal device to enable fog computing,'' in Proc. 3rd Int. Conf.
Fog Mobile Edge Comput. (FMEC), Apr. 2018, pp. 93-99.
5. OpenPGP. Accessed: Nov. 15, 2020. [Online]. Available:
https://www.openpgp.org