Routing & scheduling for mobile ad hoc networks using

an EINR model

Harshit Arora Mentor:IIT Kanpur Dr. Harlan Russell

Mobile ad-hoc network:

A self-configuring network. Does not require any

infrastructure. Can have any arbitrary

topology at a time. Can operate in a standalone

fashion and thus can be helpful in disaster management and military conflicts.

EINR model: EINR is energy to interference+noise ratio. At a node:

Received energy and received interference at the receiver are estimated by using a propagation model .

No is the thermal energy of the noise at the receiver.

all other Txin the network

o

received EnergyEINR =

N + received interference

Motivation behind the EINR approach: Transmission range model. A wants to sent to B, C wants to send to D

Using transmission range model Using the EINR model EINR at B

If EINR at B and D is greater than the EINR threshold (β)

then both transmisions are possible.

bc

abα = 3.5

dd

A B

C

D

EINR at D ad

cdα = 3.1

dd

Protocols: A convention for data transfer in a network. Protocols can be divided into two subcategories:1. Channel access protocols2. Routing protocols

Channel access protocol deals with the question “Who can transmit when ?”

8 time slots. A particular time slot is selected if the following three

conditions hold:1. The time slot is available to both Tx and Rx.2. Rx satisfies the EINR criterion.3. All other transmissions continue to maintain acceptable

EINR.

Protocols..

Slot 1 Slot 2 Slot 3

4

1

53

6

1. Transmit data from 1 to 4.2. Transmit data from 4 to 5.3. Transmit data from 5 to 3.4. Transmit data from 2 to 6.

14 45 53

26

Assume that each node has 3 time slots.

2

Protocols.. Routing protocol addresses the question of finding a

path between the source and the destination. In my simulations Dijkstra’s algorithm has been used as

the routing protocol. Links must be assigned weights. Links are assigned weights using the ENR ( Energy to

Noise ratio) criterion. At any node:

1. Received energy at a node is estimated using the propagation model.

2. No is the thermal energy of noise.

Received energyENR=

oN

Protocols.. Suppose we have to assign weight to the link

between nodes ‘4’ and ‘8’. No node other than ‘8’ is assumed to

transmit. At node 4:

ENR criterion: If ENR > threshold, weight[4,8] =+ve

otherwise 0.1. If threshold = β Problem!!!

2. threshold = β*η is a better choice.

η is called the interference margin.

received energy

o ENR=

N

8

12

5

9

3

7

4

6

Tx Rx

Protocols.. Many Routing approaches are possible. Min. hop routing metric: If ENR > β*η link weight = 1 otherwise 0

Disadvantage If β*η=3.0 , both the links are assigned ‘1’ Although link(1,3) is far better than

link(2,4) min. hop approach doesn’t bring out the difference.

Need to come up with a new routing metric approach.

1

42

3

ENR =3.1

ENR =10Weight =1

Weight =1

Protocols.. Distance metric approach: If ENR > β*η link weight =

otherwise 0.

Proposed metric approach:If ENR > β*η link weight = otherwise 0.

( , )dist i j

1

,Min ENR

Description of simulation model:

A randomly generated network topology of N nodes, whose location is randomly decided, is considered in a square region.

Links are assigned weights. The network is checked for

connectivity. A source and a destination pair is

randomly chosen. A route between the source and the

destination is obtained. The ‘network diameter’ is the number

of links in the longest min-hop route.

8

12

5

9

3

7

4

6

Description of Simulation model.. Slots are allocated to each link in the route. If slot allotment is successful for all links, the route is

termed a success. The total number of such successful pairs is determined

and is called ‘network capacity’.

Different network topologies have been analyzed for different values of β, η and for different routing metric approaches to come up with a set which ensures best

network performance.

Simulation results:

For a fixed β=4, η=2 gives the best nework performance.

Network capacity Vs Side of sq.(m)

9

10

11

12

13

14

15

16

17

18

500 1000 1500 2000

Side of sq.(m)

Netw

ork

cap

aci

ty

Average diameter Vs Side of sq.(m)

0

2

4

6

8

10

12

14

16

0 500 1000 1500 2000 2500

Side of sq.(m)

Ave

rag

e d

iam

ete

r

η=4

η=2

η=1.5η=1

η=4η=2

η=1.5

η=1

β=4N=100

Simulation results..

Network capacity Vs Side of sq.(m)

0

20

40

60

80

100

120

0 1000 2000 3000 4000 5000 6000

Side of sq.(m)

Net

wo

rk c

apac

ity

Average diameter Vs Side of sq.(m)

0

2

4

6

8

10

12

14

16

0 1000 2000 3000 4000 5000 6000

Side of sq.(m)

Ave

rag

e d

iam

eter

η=2

β=0.01

β=0.01β=1.0β=1.0

β=4

β=4

Simulation results..Network capacity Vs Side of sq.(m)

9

10

11

12

13

14

15

16

17

18

19

500 1000 1500 2000

Side of sq.(m)

Netw

ork

capacit

y

The proposed approach performs better than the min. hop and the distance metric approach.

Proposed approach

Min hop approach

Distance metric

Conclusion: Analysis of different Network topologies show that a low

value of β reduces the network dependence on interference while a high value of β makes the network more susceptible to interference.

A low value of β increases the network capacity for a fairly large value of average network diameter.

The proposed routing metric protocol promises an improvement in network performance parameters i.e. network capacity and average diameter.

Acknowledgements: Dr. Harlan Russell, academic supervisor. Dr. Noneaker and Dr. Xu, SURE program directors. Josh, Steven,Tomy and Rahul for their guidance. All my fellow SURE participants for making this

experience so special and so much more fun. SURE program and the Clemson University.