1. Dr. Charles Graff, US Army RDECOM CERDEC-STCD Ft. Monmouth NJ 07703
Network Design 1 May 2008
Dr. Charles Graff,
US Army RDECOM
CERDEC-STCD
Ft. Monmouth NJ 07703

2. Briefing Outline Background on Networks Network Design Issues
STCD Network Design Program
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3. Distribution Statement
This briefing has been previously cleared for public release.
The comments recommendations and conclusions are solely those of the author, and not of the Army or DOD.
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4. Background on Networks

5. Mobile Ad Hoc Networks (MANETs) vs Commercial Networks
Mobile Ad Hoc Networks (MANETs) do not have a fixed infrastructure or backbone
MANET connectivity is determined by node location, mobility, and RF propagation characteristics.
Commercial Networks ( Digital Cellular ) have fixed infrastructure backbone of Base Stations and Cell Towers that are interconnected thru high bandwidth Fiber Optic cable.
In Digital Cellular, only the “last hop” is wireless.
Other Commercial wireless networks ( IEEE 802.xx ) are usually static in nature.
The MANET model of networks, with extensions for multi-tier operation, is most applicable to Army Networks such as FCS and WIN-T.
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6. MANET Networks
User Traffic and RF connectivity are in fact probabilistic and stochastic quantities
User node mobility is also a stochastic quantity
Node interconnection, as determined by RF propagation, leads to a time dependent, stochastic topology graph
Classic Graph Theory has limited application for MANETS due to the inability of represent link to link coupling and node mobility explicitly.
Hence the mathematics of networks must include both a stochastic nature as well as a combinatoric nature
In WIRELESS MANETS, both the Network and the Traffic loads are stochastic in nature
In WIRED Networks typically only the Traffic is stochastic
Hence MANET Network Design is a HARD problem!!
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7. Network Analysis vs. Network Design (Synthesis)
The Analysis Problem:
Given a network solution perform an performance analysis typically for
User Requirements for throughput, delay, reliability
Network survivability
Network connectivity
The Design Problem:
Design is the Synthesis Problem
Given what you want ( as given requirements), how do you go about creating the network solution that meets (or exceeds the requirements?
This is a creative and inventive process
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8. Network Design Issues

9. Specific Network Design Issues
Design metrics for MANETs
Connectivity ( in mobile ad hoc environment )
ETE User Requirements
Survivability ( in mobile ad hoc environment )
“Optimality” or goodness of design to be used in comparisons of different designs
RF Connectivity representation ( including mobility )
Steady state vs transients in network operation
Closed loop vs open loop for adaptive/dynamic algorithms
Scalability ( to arbitrary network sizes )
Multi-tiered Operation
Widely differing RF characteristics link highly dispersed users
Design solution should be “insensitive” to traffic loads, operational scenarios
Typically requirements are uncertain and/or subject to change
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10. Specific Network Design Issues (Concluded)
Security
“Optimality” in the face of uncertain requirements, scenarios, and RF environments
RF Waveform Design for Jamming Environment
“Validation” issues
Does the design meet/exceed requirements
Typically requires extensive testing
“Verification” Issues
Does the design have “good properties” that are common to all designs
COST, COST, COST
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11. Network Analysis / Design Techniques
Analytic Modeling
Define/develop mathematical equations for network behavior – connectivity, capacity, thruput/goodput, delay, survivability, etc
Very difficult to get good closed form solutions
A 6.1 and academic focus
Simulation
Develop Discrete Time Event Driven simulation for the Network
Execute simulation over range of traffic loads, topologies, mobility patterns, failure/destruction patterns
Requires a large number of runs to get solutions
OPNET, Qualnet, etc
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12. Network Analysis/Design Techniques ( Concluded )
Prototyping
Build a solution, make measurements on it.
Collect a large amount of data and then use data driven modeling techniques to define necessary relationships
Rutgers WIN-LAB ORBIT is good example
Emulation
Build a scaled or abstracted version of the network solution
make measurements and develop data driven relationships
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13. Mathematics Potentially Applicable to Network Design and Analysis
Graph Theory
Topology representation and analysis – but not sufficient for MANET
Closed Loop control theory
Assumes a “network operating point” that is to be maintained
Need to add delay/ loss model to control model
Stochastic Processes
“Random” or statistical nature of traffic load, node mobility, and RF channel, Discrete Time Markov Processes, Fractals, Jackson Networks. BCMP Networks
Discrete Event Transition Models
Finite State machines, Petri-Nets, etc
To represent control interaction with system state
Optimization Theory as applied to Network Design
Primal/Dual, hill climbing, annealing, Genetic Algorithms, Robust Optimization, Stochastic Optimization
VERY MESSY INDEED
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14. Overall MANET Network Design Elements
The protocol/stack design
individual protocols
cross layer issues
protocol parameters/configuration
The “node” design
Radio hardware design
Antenna Modifications
Waveform Modifications
The Recommended Network Architecture
Relay function for multi-hop networks
Addition relay (like UAV or UGV) capability may be needed to achieve network goals of connectivity, capacity, and survivability
Domain sizing and organization
Interconnection points
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15. Cross Layer Design Issues (Goldsmith, et. al.)
Multiple Antennas
Coding/Modulation
Power Control
Adaptive Link Techniques for Power Control, Scheduling, and link selection
Neighbor selection / maintenance
Delay/energy constrained routing
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16. Network Layer Design Issues
Network Initialization Time (Cold Start)
Node Join Time To Existing Network
Group Node Join Time
Node Leave Time From Existing Network
Group Node Leave Time
Network Recovery Time ( after node / link failure )
Network Overhead ( packets/sec )
Processing Resource Requirements ( CPU cycles/memory)
QOS/Data Handling
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17. Application Layer Issues
ETE delay, throughput/goodput, packet loss
Reliability/Connection Management
QOS/priority
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18. STCD Network Design Program

19. Background on Engineering Network Design Tools
Design Tools exist for wired backbone networks, such as Digital Cellular and POTS.
Some design tools exist for IEEE802.xx type networks, but are typically limited only to connectivity determination and node placement.
Some design Tools exist for Satellite Networks but are limited primarily to up/down link design.
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20. Why a Network Design Toolset?
Due to network complexity, many relationships may be required to accurately describe network behavior
These relationships will need to be coupled and use in a coordinated fashion to produce a complete network design
Tools/Toolsets have be used successfully in many other areas:
Cell phone network design
Design of Boeing 777
VLSI circuit design
Layout of internal spaces on submarines
But not ad hoc network design ( yet!)
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21. Network Design vs Network Planning
Used by Military user in Operational environment to plan deployment of existing network assets to meet operational needs in theater.
Assumes that Network hardware/software is fixed and designed.
Network Design
Used by Engineering community
protocol/stack design, node design, and network architecture
provides rapid design/trade offs of design options
validated thru detailed OPNET ( or equivalent ) simulations, prototyping, or experimentation
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22. Approaches to Network Design
Analytic Approach
Human must create, discover or invent mathematical relationships/heuristics
Many assumptions required for mathematical tractability
Typically, one function at a time is all that can be represented mathematically ; hence many coupled relationships may be required to completely describe ad hoc networks
In many areas, these analytic relationships do not exist
Discrete Time Event Driven Simulation Approach
The human must do the design, and the simulation does the computation; The human does the analysis of simulation results
Very detailed, fine grained simulations are possible
Specific solutions to specific input sets (a big calculator)
Many runs needed to vary parameters and get statistically valid results
Little to no insight as to network behavior or “what effects what”
Prototyping / experimental testing Approach
Must have a network built first
Instrumentation issues to get accurate measurements
Much data can be collected --> challenging data analysis
SHOULD BE DONE TOGETHER IN CONSISTENT FASHION to achieve good network design
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23. Network Engineering Design Analytic Toolset (NEDAT)
Network output - What can the network do?
Network input goals/ranges.
NEDAT INPUTS
NEDAT OUTPUTS
NUMBER OF NET NODES
LOCATION OF NET NODES
RANGE OF XMIT POWER
RF REPRESENTATIONS
RANGE OF RF LINK RATES
NET CONNECTIVITY
NET END TO END CAPACITY
NET SURVIVABILITY
GENERIC PROTOCOLS
MOBILITY PATTERNS
NEDAT
Development Approach “Build a little/test a little”
Start with small numbers of nodes (~15)
Increase number of nodes (~100 , ~500, ~1000)
Add more refined behavior as Network Science results become available.
ADDITIONAL NODES AT LOCATIONS NEEDED??
ACTUAL XMIT POWER
ACTUAL DATA RATES
PROTOCOL STACK REQUIRED PARAMETERS
Tool Development Approach - “Build a little/test a little”
NEDAT will produce a network design
Start with small numbers of nodes (~15)
Increase number of nodes (~100 , ~500, ~1000) and possibly add more refined behavior as Network Science result become available.
6.1 RESEARCH NETWORK DESIGN
EQUATIONS/RELATIONSHIPS
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24. Network Engineering Design Analytic Toolset (NEDAT)
Detailed OPNET Simulation used to “validate” Network Design Produced by NEDAT
Scenario/Traffic Load
Connectivity, Capacity,
Survivability
Discrete Time Event driven OPNET Simulation
Network Design
High Level Node Representation
NEDAT
Goals Met? No
Design Equations
Heuristics From Network Science
Ideas for Refinement
Yes
Recommend Lab/Field Experimentation
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25. What is Cognitive Networking (CN)?
Cognitive Networks (CN) are characterized by advanced hardware and software that
Interacts proactively with the environment (RF, traffic load, mobility, mission profile, etc)
Uses learning, knowledge representation, estimation, predictive and optimization techniques for (near) real time network control (i.e. protocols), and network and spectrum management
Provides enhanced performance (user thruput, delay, loss, survivability) and spectral efficiency over “conventional” techniques
Conventional techniques use fixed algorithms with variable parameters, while Cognitive Networks use variable algorithms with variable parameters in stable, non-oscillatory, adaptive fashion
(MILITARY) Cognitive Networking is the application of the above techniques to the MANET Technology
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26. Cognitive Networking vs “Traditional” Networking
Cognitive Networking will solve the well known MANET networking problems in a “different” and better way when compared to “traditional” approaches
Interact, learn, and respond to networking environment
Algorithms change their computation logic (based on the environment) as well as usual algorithm parameters
Provide less overhead thru the use of advanced techniques (i.e. compute, don’t communicate) such as predictive, estimation, local reasoning, etc.
The Networking problems to be addressed remain the same
Network Ops, Control/Management
Protocols / Cross layer stack design
Security
Multi-tiered Network Transport
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27. Why Cognitive Networking Design (CND) is hard?
Mobile Ad Hoc Networking design is hard in general; the addition of cognitive capabilities increase the design space/options
Many options and alternatives need to be examined and explored
Traditional simulation (i.e. OPNET) approaches have limitations regarding model development, fidelity, scalability and are actually analysis and not design tools
Many of learning, estimation, reasoning, optimization and other techniques have not been applied to the highly mobile, large scale, distributed, dynamic ad hoc networking for the hostile battlefield environment.
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28. Why a CN Design Tool?
MANETs in general are hard to design due to the large design space
Using Cognitive Networking technology only increases the design space options and adds complexity to the process with the expectation of enhanced performance and efficiency of operation.
Using a modeling approach to present both cognitive and non-cognitive functions in an engineering tool environment will allow rapid and effective exploration of a large number of design options and alternatives.
Using a modeling approach, critical issues such as scalability, performance, and behaviors may be explored and investigated at minimal cost without committing to large amounts of physical hardware or expensive testing
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29. Cognitive Network Design (CND) Considerations
Design metrics for MANETs
Connectivity ( in mobile ad hoc environment )
End-to-end User Requirements
Survivability ( in mobile ad hoc environment )
“Optimality” or goodness of design to be used in comparisons of different designs
Knowledge oriented representation of RF connectivity (including mobility), network operation/behaviors
Effectiveness of learning/prediction techniques in dynamic environment
Steady state vs. transients in network operation (stability issues)
Design solution should be “insensitive” to traffic loads, operational scenarios/requirements and environments
Typically these are uncertain and/or subject to change
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30. CN Design Tool Software Functional Architecture
Knowledge
Database
Prediction/Estimation
Learning
Functional Model
Repository
Analytic Tool-box
Input
Module
Design Manager
Output
Module
???
DES
Adaptor
External
DES
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31. The Payoff of Cognitive Networking
The ability to say, with a high level of confidence that the “network” will work in the military dynamic environment
A potential to perform cost-performance trade offs for various cognitive network design through analysis for large military ad-hoc networks
A new capability to “optimize the design” for performance functionality, and capabilities of mobile ad-hoc networks
A better understanding of complex network behaviors
New capability to optimize engineering design of large, multi-tiered, highly mobile, ad hoc networks
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