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.  N o is the thermal energy of the noise at the receiver.

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. A B C D EINR at D

Protocols:  A convention for data transfer in a network.  Protocols can be divided into two subcategories: 1.Channel access protocols 2.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 1Slot 2Slot 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. 141445455353 2626 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.N o is the thermal energy of noise.

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 TxTx RxRx

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 ENR =3.1 ENR =10Weight =1

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

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

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. η=4 η=2 η=1.5 η=1 η=4 η=2 η=1.5 η=1 β=4 N=100

Simulation results.. η=2 β=0.01 β=1.0 β=4

Simulation results.. 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.