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Trust-based Security for Mobile Ad-hoc Networks using Small World Phenomenon. Summary of Tasks done. Network simulations using AODV,DSDV and DSR Comparison of above protocols Develop Android application (partially) Algorithm to uniquely identify a node

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Presentation Transcript
summary of tasks done
Summary of Tasks done
  • Network simulations using AODV,DSDV and DSR
  • Comparison of above protocols
  • Develop Android application (partially)
  • Algorithm to uniquely identify a node
  • Algorithm to create a weighted scoring model to develop a ranking for each device
current challenges of the network
Current challenges of the network
  • No existing implemented network
  • Started creating ad-hoc network from the scratch
  • Existing advance protocols creates huge network traffic
    • AODV-UU
    • ECBRP
  • CBRP protocol does not work in simulator environment
implementation concepts
Implementation concepts
  • Create a rating value for the phone(as we proposed)
  • Use existing packet formats to distribute security information
  • Using basic versions of famous routing protocols
    • AODV, DSDV, DSR
reasons to go back to basics
Reasons to go back to basics
  • Implementations are simple and clear
  • Easy to edit
    • Existing packet formats
    • Create new packet formats if needed
  • Most favourable protocol is DSR and AODV
    • Less amount of packets are dropped in DSR
    • AODV is simple and does not create huge network overhead
normalized routing load for 15 nodes
Normalized routing load for 15 nodes

An Extensive Performance Analysis of CBRP, DSR and AODV Protocols for Dense and Sparse Topologies in MANET - Kondaiah et al

packet delivery ratio for 15 nodes
Packet delivery ratio for 15 nodes

An Extensive Performance Analysis of CBRP, DSR and AODV Protocols for Dense and Sparse Topologies in MANET - Kondaiah et al

average delay for 15 nodes
Average delay for 15 nodes

An Extensive Performance Analysis of CBRP, DSR and AODV Protocols for Dense and Sparse Topologies in MANET - Kondaiah et al

android development
Android Development
  • Scan the local Android based phone to check what resources are currently being used in the operation
  • Give a rating based on the statistics acquired using the scan. For this scan Mobile data connectivity, GPS availability, currently installed apps in the phone are being considered
unique id generation algorithm
Unique ID generation algorithm
  • SIM ID: 12345678901234567890
  • IMEI : 123456789012345
  • Step1: Get the sum of all numbers in

‘a’ à m

a

b

c

d

unique id generation algorithm1
Unique ID generation algorithm…
  • Step2: Do the same for block ‘b’ and ‘c’

block ‘b’ àn block ‘c’ à o

  • Step3: Mod the even places of block ‘b’

with the numbers in block ‘d’

then get the sum of the result

2

4

6

8

0

%

%

%

%

%

1

2

3

4

5

p

à

x

x

x

x

x

unique id generation algorithm2
Unique ID generation algorithm…
  • Step 4: Do the same odd places of

block ‘b’ àq

So far ‘m n o p q’ are created

  • Step 5: If the rank is x.y (Eg: 7.8)

Divide mnopq by x and get the

sum of the resulting numbers àr

unique id generation algorithm3
Unique ID generation algorithm…
  • Step 6: Do the same for mnopq

with y às

So far ‘m n o p q r s’ are created

  • Step 7: Get the sum of all numbers in

the above number àt

x

m

n

o

p

q

r

s

t

y

device rating
Device rating
  • Data
    • HSDPA -> 10
    • EDGE -> 6.7
    • GPRS -> 3.4
    • Weight is 2
    • A = Rate x Weight
slide15

Unused ports

    • B = (Unused Ports)/(Total Ports) x 10 x 4
  • Harmful apps
    • C = (Total Apps – Harmful Apps)/(Total Apps) x 10 x 3
  • Nodes connected
    • D = (Connected Nodes)/10 x 2
slide16

GPS

    • Available = 10
    • No = 10
    • Weight = 1
    • E = [0|10]
  • Rating = (A+B+C+D+E)/12
  • Good if Rating > 7.5
  • Fair if 7.5 > Rating > 5.0
tasks to be done
Tasks to be done
  • Develop MANET
  • Obtain network parameters such as no. of connected nodes
  • Implementing Trust features