1

Cognitive Radio Research@WINLAB

Narayan B. Mandayam

WINLABRutgers University

May 20, 2008

narayan@winlab.rutgers.eduwww.winlab.rutgers.edu

2

Cognitive Radio Research@WINLAB A Multidimensional Activity

Spectrum Policy Economics Regulation Legal Business

Theory and Algorithms Fundamental Limits Information & Coding Theory Cooperative Communications Game Theory & Microeconomics

Hardware/Software Platforms & Prototyping Programmable agile radios GNU platforms Cognitive Radio Network

Testbed

3

How it all started -The Spectrum Debate & Cognitive Radio

4

The Spectrum Debate Triumph of Technology vs. Triumph of

Economics

Open Access (Commons) [Noam, Benkler, Shepard, Reed …]

Triumph of Technology Agile wideband radios will dynamically share a commons Success of 802.11 vs 3G

Spectrum Property Rights [Coase, Hazlett, Faulhaber+Farber]

Triumph of Economics Owners can buy/sell/trade spectrum Flexible use, flexible technology, flexible divisibility, transferability A spectrum market will (by the force of economics) yield an efficient

solution

What everyone agreed on (~ 10 years ago): Spectrum use is inefficient FCC licensing has yielded false scarcity

5

Arguments against Triumph of Technology

Partially developed theory Information theoretic relay channel Information theoretic interference channel Ad hoc network capacity, with/without mobility

Technology Panacea Spread spectrum, UWB, MIMO, OFDM Short range communications Ad hoc multi-hop mesh networks

Infant technology UWB, MIMO antenna arrays Transmitter agility

Technology not separable from user assumptions Capabilities of technology vary with cooperation

6

Source: FCC website

Spectrum Management: In fairness to the FCC, Frequency allocation is complex…

7

Maximum Amplitudes

Frequency (MHz)

Am

pli

du

e (d

Bm

)

Heavy Use

Sparse Use

Heavy Use

Medium Use

Less than 6% OccupancyLess than 6% Occupancy

Spectrum Management: Then again, poor utilization in most bands

FCC measurement shows that occupancy of approximately 700 MHz of spectrum below 1 GHz is less than 6~10%

~ 13% spectrum opportunities utilized in New York City during 2004 Political Convention to nominate U.S. Presidential Candidate

Atlanta

NewOrleans

SanDiego

Frequency

Time

8

The Spectrum Debate & Cognitive Radio

What everyone agrees on now: Spectrum use is inefficient FCC licensing has yielded false scarcity

Possible middle ground? Dynamic spectrum access Short-term property rights Spectrum use driven by both technology and market forces

Cognitive Radios with ability to incorporate market forces? Microeconomics based approaches to spectrum sharing

“Dynamic Spectrum Access Models: Towards an Engineering Perspective in the Spectrum Debate” by Ileri & Mandayam, To appear in IEEE Communications Magazine 2007

9

Develop engineering models for shaping spectrum policy

Three aspects we consider: Dynamic Spectrum Access: Short term dedication of

spectrum resources Temporary Exclusive Usage: Parties do not suffer

interference Market Based Allocation: Supply and demand

determines who gets how much bandwidth

We introduce two spectrum access models

D-Pass (Dynamic Property-Rights Spectrum Access) D-CPass (Dynamic-Commons Property-Rights Spectrum Access)

Dynamic Spectrum Access Models via Cognitive Radio

10

D-Pass Model (flavor of property rights)

SPS

Operator 1 Operator 2

User 1 User 2 User 3 User 4 User N

W1 W2

A

WW

WWW

O

21

,.bj max

21

Vectoraloffers to users

Allocation based charges, competition reminiscent of simultaneous ascending auctions

1W 2W

11

D-CPass Model (flavor of open access)

Operator 2Operator 1

SPS

Per user operator competition

N

nAn WWW

1W

.,Obj max

User 1 User 2 User N

1W 2W NW

TNWWW ...1

Usage based charges, competition reminiscent of English auctions

1W 2W NW

12

Static allocation of spectrum is inefficient Slow, expensive process that cannot keep up with technology

Spectrum allocation rules that encourage innovation & efficiency Free markets for spectrum, more unlicensed bands, new services,

etc.

Anecdotal evidence of WLAN spectrum congestion Unlicensed systems need to scale and manage user “QoS”

Density of wireless devices will continue to increase ~10x with home gadgets, ~100x with sensors/pervasive computing

Interoperability between proliferating radio standards Programmable radios that can form cooperating networks across

multiple PHY’s

Motivation for Dynamic Spectrum and Cognitive Radio Techniques:

13

Towards Cognitive Radio Networks

Research themes that have emerged from mobile ad hoc and/or sensor networks research:

Hierarchical Network Architecture wins Capacity scaling, energy efficiency, increases lifetimes, facilitates

discovery

Cooperation wins Achievable rates via information theoretic relay and broadcast

channels

“Global” awareness and coordination wins Space, time and frequency awareness and coordination beyond

local measurements

Efficient operation requires radios that can: Cooperate Collaborate Discover Self-Organize into hierarchical networks

14

Selected Cognitive Radio Research @WINLAB

15

Cognitive Radio - Theory & Algorithms

Fundamental research and algorithms – based on foundations of: Information and Coding Theory

Relay cooperation, User Cooperation, Coding techniques for cooperation, Collaborative MIMO techniques

Signal Processing Collaborative signal processing, Signal design for spectrum

sharing, Interference avoidance, Distributed sensing algorithms Game Theory

Spectrum warfare, Microeconomics and pricing based schemes for spectrum sharing, negotiation and coexistence, Coalition formation, Incentive mechanisms for cooperation

MAC and Networking Algorithms Discovery protocols, Etiquette protocols, Self-organization

protocols, Multihop routing

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Information Theoretic Approaches Various types of relay cooperation and user cooperation models

Cooperation – nodes share power and bandwidth to mutually enhance their transmissions

Can achieve spatial diversity – similar to multiple antennas Fundamental limits are known in limited cases Primary focus on achievable rates, outage and various cooperative

coding schemes, e.g. Decode-and-Forward Compress-and-Forward Amplify-and-Forward

Wired Backbone

SN

SN

SN SN

SN

SN

SN

SN

SN SN

SN

SNSN

SN

SN

SN

SN

SN

SN

SN

AP

APFN

FN

FN

FN

AP

FN

FN

FN

SN

SN

Relay Cooperatio

n

17

SN

Wired Backbone

SN

SN

SN

SNSN

SN

SN

SN

SN

SN

SN

SN SN

SN

SN

SN

SN

SNSN

SN

SN

SN

SN

SN

AP

AP

AP

SN

SN

SN

SN

SN

Information Theoretic Approaches Various types of relay cooperation and user cooperation models

Cooperation – nodes share power and bandwidth to mutually enhance their transmissions

Can achieve spatial diversity – similar to multiple antennas Fundamental limits are known in limited cases Primary focus on achievable rates, outage and various cooperative

coding schemes, e.g. Decode-and-Forward Compress-and-Forward Amplify-and-Forward

User Cooperatio

n

18

E.g. Relay Cooperation vs User Cooperation (Sankar-Kramer-Mandayam)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

x-coordinate

y-c

oo

rdin

ate

User 1

User 2Destination

Relay

Sector of circle of radius 1 and destination at circle center.

Uniform node distribution.

Average rate and outage over 100 locations.

Path-loss exp. = 4.

Transmit SNR for all users = P1.

Processing cost factor

Compare Outage Probability?

How much better than TDMA?

What is the effect of processing costs?

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Amplify-and-Forward Cooperation = 0.01

Relay and User Cooperation schemes gain over TDMA Relay achieves gains relative to user-cooperation

-10 -5 0 5 10

10-4

10-3

10-2

10-1

100

10-5

Transmit SNR P1 (dB)

Out

age

Pro

babi

lity

Pou

t

Sub-plot 1

-10 -5 0 5 10 15

10-4

10-3

10-2

10-1

100

10-5

Total (transmit+proc.) SNR Ptot

(dB)

Out

age

Pro

babi

lity

Pou

t

Sub-plot 2

Coop. 2-hop

Relay Pr=0.5P

1

Relay Pr=P

1

TD-MAC

Coop. 2-hop

Relay Pr=0.5P

1

Relay Pr=P

1

TD-MAC

K = 2Rate R = .25

K = 2Rate R = .25 = .01

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Amplify-and-Forward Cooperation = 0.5, 1

Even with relay achieves gains relative user cooperation

-5 0 5 10 15

10-4

10-3

10-2

10-1

100

10-5

Total (transmit+proc.) SNR Ptot

(dB)

Out

age

Pro

babi

lity

Pou

t

Sub-plot 1

-5 0 5 10 15

10-4

10-3

10-2

10-1

100

10-5

Total (transmit+proc.) SNR Ptot

(dB)

Out

age

Pro

babi

lity

Pou

t

Sub-plot 2

Coop. 2-hop

Relay Pr=0.5P

1

Relay Pr=P

1

TD-MAC

Coop. 2-hop

Relay Pr=0.5P

1

Relay Pr=P

1

TD-MAC

K = 2Rate R = .25 = 1

K = 2Rate R = .25 = .5

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Game Theory Approaches Negotiation strategies for mediation

Pricing and microeconomic strategies to promote cooperation in spectrum sharing

Reimbursing costs in cooperation – energy costs, delay costs

Domination strategies for situations of conflict Spectrum warfare with agile waveforms and competition for spectrum

Coalition formation strategies for cooperation Coalitional games for receiver and transmitter coalitions in spectrum sharing

Approaches result in algorithms that specify:

• Power control • Rate control• Channel selection• Cooperation techniques• Route selection

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Each node•Enjoys its own data reaching the access point.•Pays for its outgoing throughput to adjacent devices.•Gets reimbursed for data it relays only if the data reaches theaccess point.

E.g. Inducing Forwarding through Reimbursement (Ileri-Mandayam)

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Horizontal Trajectory

Radio tower

5 m 10 m

-5 m

-

USER 2(POTENTIAL

FORWARDER)

USER 1(NON-FORWARDER)

ACCESS POINT

5 m POSITIONS OFUSER 1 IN

HORIZONTALGEOMETRY

• Incentives for forwarding works well when nodes tend to “cluster”

• The aggregate bits/Joule in network is higher

• Network revenue is also higher

0 1 2 3 4 5 6 710

14

1015

1016

1017

1018

1019

Aggregate bits/JouleAggregate bits/Joule, no reimbursement.

E.g. Inducing Forwarding through Reimbursement

user 2 distance

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Cognitive Radio: Reactive Schemes Reactive (autonomous) methods used to avoid interference via:

Frequency agility: dynamic channel allocation by scanning Power control: power control by interference detection and scanning Time scheduling: MAC packet re-scheduling based on observed activity Waveform agility: dynamism in signal space

Reactive schemes (without explicit coordination protocols) have limitations: Interference is a receiver property!

Coverage

area of D

Y

A

B

C

D

A cannot hear DA’s agile radio waveform

D’s agile radio waveform

without coordination protocol

with coordination

Hidden Terminal Problem

Coverage area of A

Coverage

area of D

Y

A

B

C

D

A cannot hear DA’s agile radio waveform

D’s agile radio waveform

without coordination protocol

with coordination

Hidden Terminal Problem

25

Cognitive Approaches: Outlook Cognitive radio networks require a large of amount

of network (and channel) state information to enable efficient Discovery Self-organization Cooperation Techniques

AA

BB

D

C

D

E

F

Bootstrapped PHY &control link

End-to-end routed pathFrom A to F

PHY A

PHY B

PHY C

Control(e.g. CSCC)

Multi-mode radio PHYAd-Hoc Discovery

& Routing Capability

Functionality can be quitechallenging!

26

Broad range of technology & related policy options for spectrum

Hardware Complexity

Protocol Complexity(degree of

coordination)

ReactiveRate/Power

Control

ReactiveRate/Power

Control

AgileWideband

Radios

AgileWideband

Radios

Unlicensed Band

with DCA (e.g. 802.11x)

Unlicensed Band

with DCA (e.g. 802.11x)

InternetServer-based

SpectrumEtiquette

InternetServer-based

SpectrumEtiquette

Ad-hoc,Multi-hop

Collaboration

Ad-hoc,Multi-hop

Collaboration

Radio-levelSpectrumEtiquetteProtocol

Radio-levelSpectrumEtiquetteProtocol

StaticAssignment

StaticAssignment

InternetSpectrumLeasing

InternetSpectrumLeasing

“cognitive radio”schemes

UWB,Spread

Spectrum

UWB,Spread

Spectrum

“Open Access”+ smart radios

Unlicensed band +simple coord protocols

Cognitive Radio: Design Space

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Cognitive Radios need help too!

Infrastructure that can facilitate cognitive radio networks

Examples of coordination mechanisms:

Information aids “Spectrum Coordination Channel” to enable spectrum sharing

Network architectures “Spectrum Servers” to advise/mediate sharing

28

Cognitive Radio: Common Spectrum Coordination Channel (CSCC) (Jing-

Raychaudhuri) Common spectrum coordination channel (CSCC) Common spectrum coordination channel (CSCC) can be used

to coordinate radios with different PHY Requires a standardized out-of-band etiquette channel & protocol Periodic tx of radio parameters on CSCC, higher power to reach

hidden nodes Local contentions resolved via etiquette policies (independent of

protocol) Also supports ad-hoc multi-hop routing associations

Frequency

CH#N

CH#N-1

CH#N-2

CH#2

CH#1

CSCC

::

Ad-hocnet B Ad-hoc

net A

Ad-hocPiconet

MasterNode

CSCCRX range

for X

CSCCRX range

for Y

Y

X

29

802.11 & 16 Co-Existence Scenario

802.16a 802.11b

Traffic Type UBR (Poisson arrival), UDP packet, 512 Bytes datagram

MAC protocol TDMA IEEE 802.11 BSS mode

Channel Model AWGN, two ray ground propagation model, no fading

Bandwidth/channels 20 MHz / 4 non-overlapping chs 22MHz / 11 overlapping chs

Bit Rate 13Mbps 2Mbps

Radio parameters OFDM (256-FFT, QPSK) DSSS (QPSK)

Background Noise -174 dBm/Hz

Rx Noise Figure 9 dB 9 dB

Receiver Sensitivity -80dBm (@BER 10^-6) -82dBm (@BER 10^-5)

Antenna Height BS 15m, SS 1.5m 1.5m

Tx Power/Max range 33dBm / 3.2Km 20dBm / 500m

Default channel Channel 1 : centered at 2412GHz Channel 1 : centered at 2412GHz

Available channels 4 (non-overlap) 12 (overlapping)

30

CSCC frequency adaptation when DSS-AP =200m and traffic load 2Mbps

AP1km

100m

802.11b Hotspot

802.16a Cell

BSSS

DSS-AP

802.16 DL 802.11 link0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Ave

rage

Lin

k T

hrou

ghpu

t (M

bps)

802.16 DL and 802.11 link

No Coordination CSCC frequency adaptation

0 200 400 600 800 10000.0

0.2

0.4

0.6

0.8

1.0

1.2

Ave

rag

e L

ink

Th

rou

gh

pu

t (M

bp

s)

Distance between 802.16 SS and 802.11 hotspot (meters)

802.16a DL 802.16a DL with CSCC 802.11 link 802.11 link with CSCC Average No Coordination Average with CSCC

Throughputs vs. DSS-AP by using CSCC power adaptation and traffic load 2Mbps

802.11 & 16 Co-Existence: Reactive vs. CSCC-based Power Control (Jing-

Raychaudhuri)

Single 802.11 Hot Spot Case

31

Cognitive Radio: Spectrum Policy Server Internet-based Spectrum Policy ServerInternet-based Spectrum Policy Server can help to coordinate wireless networks (a “Google for spectrum”)

Needs connection to Internet even under congested conditions (...low bit-rate OK) Some level of position determination needed (..coarse location OK?) Spectrum coordination achieved via etiquette protocol centralized at server

InternetInternet

Access Point(AP2)

AP1

WLANoperator A

WLANoperator B

Ad-hocBluetoothPiconet

Wide-areaCellular data

service

SpectrumPolicy Server

www.spectrum.net

AP1: type, loc, freq, pwrAP2: type, loc, freq, pwrBT MN: type, loc, freq, pwr

MasterNode

EtiquetteProtocol

32

What can a Spectrum Policy Server do?

Spectrum Server

rate4rate1

rate2 rate3

Spectrum Server facilitates co-existence of heterogeneous set of radios by advising them on several possible issues:

Spectrum policy Interference informationScheduling and coexistenceLocation specific servicesContext

Mobility Management AddressingAuthentication SecurityContent

33

(( ))

Cross-layer scheduling of end-to-end flows

Wireless network

• Physical links - achievable rates depend on the PHY layer transmission schemes, signal processing at receiver etc.

• MAC – distributed/centralized schemes to avoid/control interference

• Routing – decision made based on metric specified by application running on the network

Flow 1 Flow 2

AP

Overarching design principles for wireless networks employing a variety of physical layer strategies?

34

network with 4 links

1 4

3

2

Transmission mode [1 0 1 0]

1

3

Rate matrix C =

[6.6 0 0.01 0 0.56 0 0.01 0 2.05[ 0 6.6 0.06 0 0 1.86 0.06 0 0[ 0 0 0 6.6 1.0 1.86 0.83 0 0[ 0 0 0 0 0 0 0 6.65 0.32

0 0.01 0 0.49 0 0.01]0.97 0.06 0 0 0.77 0.06 ]0 0 0.04 0.04 0.04 0.04 ]0.05 0.04 0.40 0.19 0.05 0.04]

Glk = link gain from Tx k to Rx l

Scheduling with a Spectrum Server(Raman-Yates-Mandayam)

Spectrum server specifies xi = fraction of time mode i is ON

Average rate in link l is rl = i

cli xi

The rate matrix C includes the achievable physical layer rates for a wide rate of physical layer transmission techniques

35

c11

cli

cL1

cM1. . . x1

xM

.

.

.

>

1

1

Universal cross-layer scheduling framework – centralized approach

PHY rates

MAC schedule

NETWORK routes

Physical layer rates

1

0 0…

0

1

…

f1

fM

.

.

.

Higher layer flow

requirements

Maximize User utilities such that:

x1

x1

x2

x3

x4

f1

f2

Utilities: Max throughput, max-min fairness, proportional fairness, energy efficiency

TRANSPORT flows

36

Hardware & Software Platforms

37

WINLAB Cognitive RadioWINLAB’s “network centric” concept

for cognitive radio prototype (..under development in collaboration with GA

Tech & Lucent Bell Labs)

Requirements include: ~Ghz spectrum scanning, Etiquette policy processing PHY layer adaptation (per pkt) Ad-hoc network discovery Multi-hop routing ~100 Mbps+

38

Cognitive Radio Network Experiments Hardware/Software Platforms@WINLAB

Urban

300 meters

500 meters

Suburban

20 meters

ORBIT Radio Grid

Office

30 meters

Radio Mapping Concept for ORBIT Emulator

400-node Radio Grid Facility at WINLAB Tech Center

ProgrammableORBIT radio node

URSP2CR board

Planned upgrade(2007-08)

Current ORBIT sandbox with GNU radio

ORBIT radio grid testbed currently supports ~10/USRP GNU radios, 100 low-cost spectrum sensors, WARP platforms, WINLAB Cognitive platforms and GNU/USRP2

Cross-layer design and experimentation

39

Wireless usage scenarios that will impact future internet design

Mobile data applicationsMultihop Mesh networks Sensor and vehicular networks

Wireless and the Future Internet “Wireless” is overtaking “Wired” as the primary mode of

connectivity to the internet

INTERNETINTERNET

WirelessEdge

Network

WirelessEdge

Network

INTERNETINTERNET

~500M server/PC’s, ~100M laptops/PDA’s

~750M servers/PC’s, >1B laptops, PDA’s, cell phones, sensors

2005

WirelessEdge

Network

WirelessEdge

Network

2010

40

Wireless/Mobile/Sensor Scenarios and the Future Internet

Some architectural and protocol implications for the future Internet... Integrated support for dynamic end-user mobility Wireless/mobile devices as routers (mesh networks, etc.) Network topology changes more rapidly than in today’s wired Internet Significant increase in network scale (10B sensors in 2020!) New ad hoc network service concepts: sensors, P2P, P2M, M2M,… Addressing architecture issues – name vs. routable address Integrating geographic location into routing/addressing Integrating cross-layer and cognitive radio protocol stacks Data/content driven networking for sensors and mobile data Pervasive network functionality vs. broadband streaming Power efficiency considerations and computing constraints for sensors Many new security considerations for wireless/mobile Economic incentives, e.g. for forwarding and network formation

41

Advanced TechnologyDemonstrator (spectrum)

LocationService

NSF RadioTestbeds

Emulation &Simulation

Protocol & ScalingStudies

5 3

4

12

Other services

SensorNetworks

Embedded wireless,Real-world applications

“Open” InternetConcepts forCellular devices

BroadbandServices,Mobile Computing

NSF GENI Implementation Wireless Sub-Networks Overview

Ad-HocMesh

Network

EmergingTechnologies

(cognitive radio)

GENIInfrastructure

Open APIWide-AreaNetworks

42

Cognitive Radio in NSF’s GENI Project

Propose to build advanced technology demonstrator of cognitive radio networks for reliable wide-area services (over a ~50 Km**2 coverage area) with spectrum sharing, adaptive networking, etc.

Basic building block is a cognitive radio platform, to be selected from competing research projects now in progress and/or future proposals

Requires enhanced software interfaces for control of radio PHY, discovery and bootstrapping, adaptive network protocols …….. suitable for protocol virtualization

FCC experimental license for new cognitive radio band

Cognitive Radio Network Node

Cognitive Radio Client

Cognitive Radio Network Node

Cognitive Radio Client

Connections to GENIInfrastructure

Spectrum MonitorsSpectrum Server

Research Focus:1. New technology validation of cognitive

radio2. Protocols for adaptive PHY radio

networks3. Efficient spectrum sharing methods4. Interference avoidance and spectrum

etiquette5. Dynamic spectrum measurement6. Hardware platform performance studies

43

CSCC Spectrum Etiquette Protocol CSCC( Common Spectrum Coordination Channel) can

enable mutual observation between neighboring radio devices by periodically broadcasting spectrum usage information

Edge-of-bandcoordination channel

Service channels

44

CSCC: Proof-of-Concept Experiments

x

d0.5meters

0.5meters

d

Bluetooth2 movesin the arc far from

WLAN2

d

WLAN1WLAN2

Bluetooth1

Bluetooth2

Bluetooth2

d

WLAN-BT Scenario

Different devices with dual mode radios running CSCC

d=4 meters are kept constant

Priority-based etiquette policy

45

CSCC Results: Throughput Traces

0 20 40 60 80 100 120 140 160 180 200 220 240

3.4

3.6

3.8

4.0

4.2

4.4

4.6

4.8

WLA

N T

hro

ugh

put (

Mbp

s)

Time (Seconds)

CSCC on CSCC off

WLAN session with BT2 in initial position

WLAN = high priority

0 50 100 150 200 250 30030

35

40

45

50

55

60

65

Blu

eto

oth

Th

rou

gh

pu

t (K

bp

s)

Time (Seconds)

CSCC on CSCC off

BT session with BT2 in initial position

Bluetooth = high priority

Observations: WLAN session throughput can improve ~35% by CSCC coordination BT session throughput can improve ~25% by CSCC coordination

46

Cognitive Radio Networks: “CogNet” Architecture

New NSF FIND project called “CogNet” aimed at development of prototype cognitive radio stack within GNU framework

Joint effort between Rutgers, U Kansas, CMU and Blossom Inc

47

Cognitive Radio Networks: “CogNet” Protocol Stack

Global Control Plane (GCP) Common framework for spectrum allocation, PHY/MAC

bootstrap, topology discovery and cross-layer routing

Data plane Dynamically linked PHY, MAC, Network modules and

parameters as specified by control plane protocol

Control PHY

Control MAC

Boot-strap

Discovery

PHY

MAC

Network

Transport

Application

Control Plane Data Plane

Global Control Plane

Data Plane

Control Signalling

Data Path

Establishment

Naming&

Addressing

48

Cognitive Radio Network Experiments Hardware/Software

Platforms@WINLAB

Urban

300 meters

500 meters

Suburban

20 meters

ORBIT Radio Grid

Office

30 meters

Radio Mapping Concept for ORBIT Emulator

400-node Radio Grid Facility at WINLAB Tech Center

ProgrammableORBIT radio node

URSP2CR board

Planned upgrade(2007-08)

Current ORBIT sandbox with GNU radio

ORBIT radio grid testbed currently supports ~10/USRP GNU radios, 100 low-cost spectrum sensors, WARP platforms, WINLAB Cognitive platforms and GNU/USRP2

Each platform will include baseline CogNet stack

49

Wireless/Mobile/Sensor Scenarios and the Future Internet – NSF’s GENI

Project Some architectural and protocol implications for the future Internet...

Integrated support for dynamic end-user mobility Wireless/mobile devices as routers (mesh networks, etc.) Network topology changes more rapidly than in today’s wired Internet Significant increase in network scale (10B sensors in 2020!) New ad hoc network service concepts: sensors, P2P, P2M, M2M,… Addressing architecture issues – name vs. routable address Integrating geographic location into routing/addressing Integrating cross-layer and cognitive radio protocol stacks Data/content driven networking for sensors and mobile data Pervasive network functionality vs. broadband streaming Power efficiency considerations and computing constraints for sensors Many new security considerations for wireless/mobile Economic incentives, e.g. for forwarding and network formation