m obile a d -h oc n etworking performance metrics evaluation and commercial availability supervisor:...
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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?