MOBILE AD-HOC NETWORKINGPerformance Metrics Evaluation and

Commercial Availability

Supervisor: Grant Wigley

By Tom Moscon

THESIS BACKGROUND

Mobile Ad-Hoc Networks (MANETs) being researched by the Defence Force (DSTO).

Reason: Infrastructure-less Rapid-deployment

Scenario: Near Dial-up speeds Over 1000 nodes Combat Net Radios (VHF/UHF Band) Harsh and mountainous terrain

Need for a solid tactical mobile network

NETWORK FUNDAMENTALS

Generic LAN/WAN networks based on a 3 layer hierarchical structure

Ad-hoc means ‘for this purpose’, in our case tactical warfare

Infrastructure-less Every soldier is a router No underlying routers/switches/access points Every node forwards packets

http://www.mednet.sk/figures/hierarchicalW.png (top)

Hierarchical

MANET

http://www.it.uu.se/research/group/mobility/adhoc/ad_hoc_net.jpg (bottom)

NETWORKING/MANET CHALLENGES

- Infrastructure-less design adds difficulty in fault detection and management

- Dynamic topology results in route changes and packet loss

- Scalability is still unsolved, challenges include addressing, routing, configuration management, interoperability, etc.

Chlamtac, I, 2003. Mobile ad hoc networking: imperatives and challenges. Ad Hoc Networks, Volume 1, Issue 1, Pages 13-64.

NETWORKING/MANET CHALLENGES

- Varied link/node capabilities cause variable processing capabilities

- Energy Constraints limit processing power, MANETs rely on each node being a router

METRICS

Used to describe a parameter of network performance

Can be captured at multiple layers

How to realize important Metrics e.g Downloading large files – Throughput Playing games/VOIP – Delay/Latency

COMMERCIAL OFF THE SHELF (COTS)

Products publicly available, bought under contract by government

Advantages: Reduced development time Lower cost/maintenance time Standardized approach Array of support

COMMERCIAL AVAILABILITY Bluetronix (Bluestar)- R&D for Government Military MANET solutions- SWARM Intelligent Routing

Trellisware - Military MANET development, Robust Hardware- Converged physical/network layer waveform- Tactical Scalable MANET (TSM) Waveform

Harris- Another Military hardware provider- Joint Tactical Radio System (JTRS) certified products

THESIS QUESTION

What are the key metrics and optimal parameters for evaluating performance in Military MANETs?

METHODOLOGY

Research (Phase 1):- > Compare previous simulations-- > Rank metrics based on data collected-- > Quantitative only, no QualitativeSimulation (Phase 2):--- > Prepare a ‘Baseline’ network topology---- > Change available parameters of the

networkAnalysis (Phase 3):----- > Analyze effect on each key metric------ > Propose optimal Implementation

METHODOLOGY Measurement Techniques - They are only applied to real systems/prototypes- Very few test beds found in literature- Uppsala University discovered “communication grey

zones” in specific geographic areas

Model Approach- Study of system behavior by varying it’s parameters- Scenario based, not full spectrum- Large number of simulation models have been

developed- Mobility models allow analysis of the effects of

mobility on the network, though limited

CONSTRAINTS

Simulators will not work accurately over the hundreds range size.

Radio Waves suffer from diffraction (bent around sharp edges), refraction(direction) and scattering(path deviation)

PERFORMANCE METRICS

METRICS (VALUE) - IMPORTANCE

Packet Delivery Ratio (%) – Very HighTotal Number of Packets Received / Total Number of Packets Sent

Average Hop Count (n) – Very HighAverage number of nodes a single packet passes through

Route Discovery Time (ms) – Very HighAverage time in Milliseconds for a route to be discovered from source to destination

Overhead (%) - HighTotal Number of routing packets sent / Total Number of Packets Sent

Jitter - HighDeviation of packet delay over a period of time.

Average End-to-End Delay (ms) - HighAverage Time in Milliseconds for packets to travel from source to destination

Average Throughput (kbp/s) - LowAverage speed of packets through the network

SIMULATIONPARAMETERS

SIMULATORS

Name Granularity

Ns-2 Finest

Glomosim Fine

OPNet Medium

QualNet Fine

GTNets Fine

OMNet++ Medium

DIANEmu Application-Level

PACKET SIZE

Increased packet size may lead to increased throughput.

Risks of higher packet corruption and network contention.

Determining a safe packet size relies heavily on the parameters of the network (e.g link loss probability, packet loss, average neighbors).

MOBILITY

Mobility models can be harmful or misleading.

Random Waypoint Vs. Group/Graph/Obstacle Mobility Model.

Using more intense mobility models can reveal the performance of a MANETs repair time.

BEACONING

The delay between broadcasting routing information.

High frequency Vs. Low Frequency.

Higher frequency beaconing causes higher power consumption and overheads yet improves route discovery.

PHYSICAL LAYER

Channel – Wireless Radio Propagation – TwoRayGround Model Interface – Wireless MAC Protocol – 802.11a Antenna Type – Omni Interface Queue Type - DropTail/PriQueue

SCENARIO PARAMETERS

Dimensions x – x Max Queue size (in packets) Routing Protocol (DSR, AODV, DSDV, OLSR) Number of Nodes (1 – 256) Movement Model (Random Waypoint, Gauss-

Markov, Manhatten Grid, Reference Point Group Mobility).

Traffic Model (Type/Speed/Max Connections) Simulation Duration

SIMULATION CONFIGURATIONS

Nodes 16 32 64

Seconds 100 100 100

Width 500 1200 1500

Length 500 1200 1500

Max Connections 32 32 32

Packets/Sec 4 4 4

Que Limit 100 100 100

Beacon Time 30/20/40 30/20/40 30/20/40

Packet Size512/1024/2048 512/1024/2048 512/1024/2048

Movement

Random Waypoint model, Gauss-Markov model, Manhattan Grid modelReference Point Group Mobility model.

Random Waypoint model, Gauss-Markov model, Manhattan Grid modelReference Point Group Mobility model.

Random Waypoint model, Gauss-Markov model, Manhattan Grid modelReference Point Group Mobility model.

PACKET SIZE RESULTS

16 Nodes 32 Nodes 64 Nodes

512 Packet 0.9829 0.5208 0.5009

1024 Packet 0.9792 0.4533 0.3275

2048 Packet 0.9498 0.3179 0.1907

10.00%

30.00%

50.00%

70.00%

90.00%

110.00%

Packet Delivery Ratio (%)

Packet

Delivery

Rati

o (

%)

PACKET DELIVERY RATIO RESULTS

Increased Packet Size has an increasingly devastating effect on the Packet Delivery Ratio with increased node size.

Though a small network size (16) shows little effect. A loss of only 3.31% PDR from 512 - 2048

PACKET SIZE RESULTS

16 Nodes 32 Nodes 64 Nodes

512 Packet 0.43 1.78 4.67

1024 Packet 0.21 0.9892 3.56

2048 Packet 0.15 0.8789 3.74

25%

75%

125%

175%

225%

275%

325%

375%

425%

475%

Routing Overhead (%)

Routi

ng O

verh

ead (

%)

ROUTING OVERHEAD RESULTS

A smaller packet size results in the greatest increase of overhead with increased network size.

Small deviation in overhead for mid to high packet size.

A small network receives the greatest decrease of overhead.

PACKET SIZE RESULTS

16 Nodes 32 Nodes 64 Nodes

512 Packet 8.4 2 4

1024 Packet 20 44 173

2048 Packet 11 280 290

25

75

125

175

225

275

325

End-to-End Delay (ms)

End-t

o-E

nd D

ela

y (

ms)

END-TO-END DELAY RESULTS

Increased packet size shows little effect on small networks.

Any higher network size, delay is too high for VOIP.

PACKET SIZE RESULTS

16 Nodes 32 Nodes 64 Nodes

512 Packet 19 16.5 19

1024 Packet 39 29 24.6

2048 Packet 57 39 21.5

5

15

25

35

45

55

Throughput (kbp/s)

Thro

ughput

(Kbp/s

)

THROUGHPUT RESULTS

Stays relatively stable over all networks sizes with small packet size.

Increased packet size has little effect on large networks.

Increased packet size has greatest effect on small networks.

ANALYSIS PROCESS

What parameters should be configured?

What’s the optimal value for those parameters?

What Metrics do those parameters effect?

Are those Metrics important for MANET VOIP?

CONCLUSION

MANETs have a small network size potential.

Military scenario will not allow for large sizes.

Hierarchical MANET solution is optimal.

Simulating hierarchical MANET solutions is uncommon and extremely hard.

Thank you!

Any Questions?