© 2007 IBM Corporation IBM T J Watson Research Center Slide 1 Invited talk at KAIST, 4/30/2007 Networking Research in the International Technology Alliance.

© 2007 IBM Corporation

IBM T J Watson Research Center

Slide 1 Invited talk at KAIST, 4/30/2007

Networking Research in the International Technology Alliance- Topology control and data dissemination in wireless networks

Kang-Won Lee

IBM T. J. Watson Research Center

Research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defense and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defense or the U.K. Government.


© 2007 IBM CorporationSlide 2 Invited talk at KAIST, 4/30/2007

CreditsCollaborators

V. Pappas, A. Tantawi, A. Beygelzemer (IBM)

S. Seshan (CMU)

P. Lio, J. Crowcroft (Cambridge)

M. Gerla (UCLA)

A. Swami (ARL), T. McCutcheon (DSTL)

Slide credits

A. Tantawi, V. Pappas (IBM)

U. Lee, M. Gerla (UCLA)



What is ITA?

International Technology Alliance for Network and Information Sciences

Large scale long-term research program supported by US ARL and UK MOD

10 years, 24 institutions in US and UK

Four main technical areas (TAs)

network theory, security of a system of systems, sensor information processing, and coalition planning



The ITA Vision

A US/UK Alliance conducting an open collaborative research focused on network science by:

Creating an international collaborative research culture– Academia, Industry, Government in US and UK

– Innovative multidisciplinary approaches

Developing ground-breaking fundamental sciences– Making an impact on coalition military effectiveness

– Develop understanding the fundamentals of military networks – not just computer networks, but also logical and social networks

Jointly address major research challenges– Networking & Security & Sensor Processing & Decision making

5

U.S.Gov.

Industry

Academia

U.K.Gov.

INDUSTRY9. BBNT Solutions LLC

10.The Boeing Corporation

11.Honeywell Aerospace Electronic Systems

12. IBM Research

13.Klein Associates

ACADEMIA1. Carnegie Mellon University

2. City University of New York

3. Columbia University

4. Pennsylvania State University

5. Rensselaer Polytechnic Institute

6. University of California Los Angeles

7. University of Maryland

8. University of Massachusetts

INDUSTRY 8. IBM UK

9. LogicalCMG

10.Roke Manor Research Ltd.

11.Systems Engineering& Assessment Ltd.

ACADEMIA1. Cranfield University, Royal Military

College of Science, Shrivenham

2. Imperial College, London

3. Royal Holloway University of London

4. University of Aberdeen

5. University of Cambridge

6. University of Southampton

7. University of York

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853

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Team Overview

6

International Technology Alliance in International Technology Alliance in Network and Information SciencesNetwork and Information Sciences

Collaborative Alliance Managers/Consortium Managers Jay Gowens (ARL) Jack Lemon (MoD) Dinesh Verma (IBM) Dave Watson (IBM-UK)



Security Across a Security Across a System-of-SystemsSystem-of-Systems

Trevor Benjamin (Dstl)Trevor Benjamin (Dstl)Greg Cirincione (ARL)Greg Cirincione (ARL)

John McDermid (York U)John McDermid (York U)Dakshi Agrawal (IBM)Dakshi Agrawal (IBM)




Network TheoryNetwork Theory

Ananthram Swami (ARL)Ananthram Swami (ARL)Tom McCutcheon (Dstl)Tom McCutcheon (Dstl)Don Towsley (U Mass)Don Towsley (U Mass)Kang-Won Lee (IBM)Kang-Won Lee (IBM)



Sensor Information Sensor Information ProcessingProcessing

Tien Pham (ARL)Tien Pham (ARL)Gavin Pearson (Dstl)Gavin Pearson (Dstl)

Thomas La Porta (PSU)Thomas La Porta (PSU)Vic Thomas (Honeywell)Vic Thomas (Honeywell)




Distributed Coalition Distributed Coalition PlanningPlanning

Jitu Patel (Dstl)Jitu Patel (Dstl)Mike Strub (ARL)Mike Strub (ARL)

Nigel Shadbolt (SHamp)Nigel Shadbolt (SHamp)Graham Bent (IBM)Graham Bent (IBM)




7



























Policy Based Security Management

Calo, IBMCalo, IBM

Policy Based Security Management

Calo, IBMCalo, IBM

Energy Efficient Security

Architectures and Infrastructures

Paterson, Royal Paterson, Royal HollowayHolloway

Energy Efficient Security

Architectures and Infrastructures

Paterson, Royal Paterson, Royal HollowayHolloway

Trust and Risk Management in

Dynamic Coalition Environments

McDermid, YorkMcDermid, York

Trust and Risk Management in

Dynamic Coalition Environments

McDermid, YorkMcDermid, York

Theoretical Foundations for

Analysis/Design of Wireless and Sensor

Networks

Towsley, U MassTowsley, U Mass

Theoretical Foundations for

Analysis/Design of Wireless and Sensor

Networks

Towsley, U MassTowsley, U Mass

Interoperability of Wireless Networks

and Systems

Lee, IBMLee, IBMHancock, RMRHancock, RMR

Interoperability of Wireless Networks

and Systems

Lee, IBMLee, IBMHancock, RMRHancock, RMR

Biologically-Inspired Self-Organization in

Networks

Lio, CambridgeLio, CambridgePappas, IBMPappas, IBM

Biologically-Inspired Self-Organization in

Networks

Lio, CambridgeLio, CambridgePappas, IBMPappas, IBM

Quality of Information of Sensor Data

Bisdikian, IBMBisdikian, IBM

Quality of Information of Sensor Data

Bisdikian, IBMBisdikian, IBM

Task-Oriented Deployment of Sensor Data

Infrastructures

La Porta, Penn StateLa Porta, Penn State

Task-Oriented Deployment of Sensor Data

Infrastructures

La Porta, Penn StateLa Porta, Penn State

Complexity Management of

Sensor Data Infrastructures

Szymanski, RPISzymanski, RPI

Complexity Management of

Sensor Data Infrastructures

Szymanski, RPISzymanski, RPI

Mission Adaptive Collaborations

Poltrock, BoeingPoltrock, Boeing

Mission Adaptive Collaborations

Poltrock, BoeingPoltrock, Boeing

Command Process Transformation and

Analysis

Sieck, Klein AssocSieck, Klein Assoc

Command Process Transformation and

Analysis

Sieck, Klein AssocSieck, Klein Assoc

Shared Situational Awareness and the

Semantic Battlespace Infosphere

Shadbolt, SouthhamptonShadbolt, SouthhamptonWagget, IBMWagget, IBM

Shared Situational Awareness and the

Semantic Battlespace Infosphere

Shadbolt, SouthhamptonShadbolt, SouthhamptonWagget, IBMWagget, IBM



Network Theory (Towsley U. Mass, Lee IBM) Fundamental underpinnings for adaptive networking

to support complex system-of-systems

P1 Theoretical foundations for design of wireless and sensor networks (Towsley, U. Mass)

P2 Interoperability of wireless networks and systems (Lee IBM-US/Hancock, RMR)

P3 Biologically-inspired self-organization in networks (Lio Cambridge/Pappas IBM-US)

Strategies for delivering traffic in duty-cycling networks Power reduction by cooperative transmission



Biologically-inspired Networking

Why do computer scientists (who work in wireless networking) look for biological inspirations?

At high level, there is a parallel between the two, e.g.

Topology and spatial characteristics

Dynamics and mobility vs. ants or insects foraging

Data diffusion vs. disease spreading

Robust design vs. self healing systems



Holy Grail

Develop simple algorithms that uses only local knowledge, which result in desirable global properties

Some network algorithms are like that (not necessarily biological)

TCP congestion control [Jacobson88]

Randomized duty cycling [Godfrey04]

Coloring-based resource allocation [Ko05]

Time synchronization of nodes [Degesys07]



However,

Wireless networks are not graphs

They are even different from conventional networks

Physical characteristics

Medium access (resource sharing)

Routing

Dynamics and mobility

There is no single kind of wireless networks

Cellular, MANET, wireless mesh, sensors, aquatic, etc.



Issues at Various Layers

Physical layer

Antenna technologies – directional, MIMO, cooperative

Power control – also an issue at MAC, network layers

MAC layer in wireless

Hidden terminal problem, exposed terminal problem

Fairness in MAC

Network layer

Myriads of ad hoc routing protocols

– Proactive, reactive, geographical, hierarchical, hybrid

Multicasting and broadcasting issues

Store-and-forward



Bio-inspiration is Not Bio-emulation

X

O



Topic of Today: Two Ongoing Research Activities

MANET/sensor net topology control

IBM / CMU

Urban sensing and data diffusion

IBM / UCLA / Cambridge



MANET Topology Control

Problem Definition

How to configure low-level device parameters in order to achieve a network structure with a set of desirable characteristics?

Characteristics

Connectivity

Network capacity

Energy consumption

Path latency

Robustness/Resilience



Configurable Parameters

Transmission power

Carrier sense threshold

MAC, packet transmission

Radio channel allocation

Multiple channel / multiple interface

Antenna characteristics:

Multiple-input multiple-output (MIMO)

Directional, onmi-directional



Example: Transmission Power



Topology Properties Pictures from CBTC paper (ToN 05)

Densely Connected Sparsely Connected

Small Hop-Count

High Power Consumption

Robust to Node Failures

Transmission Interference

Large Hop-Count

Low Power Consumption

Prone to Node Failures

Low Interference

What makes a good topology ?

Small Hop-Count

Low Power Consumption

Robust to Node Failures

Low Interference



Good Topology: Application-Driven

Application-driven topology control:

Application-specific metrics

Placement of services

Compatibility matrix

Dense

Sparse

Real-TimeMessaging Dense Sparse Long Short

Application-Type Traffic-Matrix Mission-DurationTopology



How to Build Good Topologies (1)

Can we get insights from wired networks?

How about biological insights – neural networks, galleries of insect colony?

[Li04]

Preferential Attachment HOT Model




What do we need to consider in MANET topology?MANET: spatial constrains

Technological Constrains: shared medium, energy consumption

Node Mobility

Time Scale

As a result:Internet: scale-free

MANET: RGG? Clustered?




Current Approaches:Minimize transmission power

– NP-hard problem (for 2D and up)

Minimize interference with channel allocation– NP-hard problem

Minimize energy stretch of a path– Relative Neighbor Graphs, Gabriel Graph, Yao Graph



Duty Cycling in Wireless Networks

Power saving longevity of mission lifetime

Impacts the performance

Sensor coverage

Connectivity

Routing delay

Mathematical modeling to provide insights for management

How to control the fraction of active nodes

Localized duty cycling decision predictable global behavior



Related Work

SPAN (Chen01)

Makes local randomized decision to join a forwarding backbone based on the estimate how much it will benefit the neighbors

GAF (Xu01)

Sets up a virtual grid based on location information, and only one node in a grid becomes active

STEM (Schurgers02)

Nodes awaken sleeping neighbors when they need to forward data using beacons on a dedicated signaling channel

NAPS

Local randomized algorithm based on number of neighbors with an aim to achieve global connectivity



Modeling Duty Cycling Networks

Consider two states: active, sleeping

Each node makes local decision based on:

Its own probability to become active

States of immediate neighbors: pulling or pushing

We are interested in the steady state

Model as a spatial process



Modeling Duty Cycling Networks – Spatial Process

n = (n1;n2;¢¢¢;nJ )

J sites

nj attribute (state) of site j 2 f1;2;¢¢¢;J g , nj 2 N j

S state space, S = N1 £ N2 £ ¢¢¢£ N J

¼probability distribution ¼: S ! (0;1) andP

n2S ¼(n) = 1

n is called a random ¯eld



Connectivity Model

G connectivity graph of sites (J ;E )

G ¡ j set of sites in G other than j

gj set of neighbors of site j

n is a Markov ¯eld if P (nj j nG¡ j ) = P (nj j ngj), j 2 f1;2;¢¢¢;J g



Probability Distribution – Product Form

For Markov ¯eld n, ¼has theproduct form

¼(n) = B ¦ C 2C µC (nC ); n 2 C

where

C µ G is a simplex (a set of fully connected edges)

C set of simplices of G

µC (nC ) a function of nC , C 2 C



Geometric Reversible Spatial Process

Suppose that N j = f1;2;¢¢¢;Ng

q(n;Tmj n) = ¸(nj ;m) Á(nj )r Ã(m)r 0

where

Tmj n = (n1;n2;¢¢¢;nj ¡ 1;m;nj +1;¢¢¢;nJ ) an operator which changes the at-

tributeof site j to m

¸(nj ;m) intrinsic tendency of a site to change from nj to m

r(r0) number of sites neighboring j with attributes nj (m)

Á(nj )(Ã(m)) extrinsic tendency of a site to change from nj (to m)



Geometric Reversible Spatial Process

Theequilibrium distribution is

¼(n) = B ¦ Nn=1 ®(n)M (n)

·Ã(n)Á(n)

¸R(n)

where

M (n) number of sites with attributen

R(n) number of edges with both end sites havig attributen

and ®(n) is thenonzero solution to

®(n) ¸(n;m) = ®(m) ¸(m;n)



Example: Simple Duty Cycling

N = f0;1g¸(1;0) = ¸¸(0;1) = ¹®= ¹ =̧Á(0) = Á(1) = 1Ã(0) = ¡Ã(1) = ¢

Then,

¼(n) = B ®M (1) ¡ R (0) ¢ R (1)

Threeparameters: ®, ¡ , and ¢

// 0: sleeping, 1: active



Analytical Result – 1k Node, 10k Edge RG



Impact of Self – Increasing α



Impact of Self – Full Spectrum



Impact of Ψ(0) – Increasing γ



Impact of Ψ(0) – Full Spectrum



Impact of Ψ(1) – Full Spectrum



Impact of Both Ψ(0) and Ψ(1)



Controlling Node Activities



Connectivity Graphs

C

G

F E

D

B

A

H

sample graphlinear graph

3 5421



State Pattern

time

Site

sN

(1)

Connectivity Graph Linearlambda 1

mu 1gamma 1

delta 1

N(1)

0

50

100

150

200

0 1 2 3 4 5

Fre

qu

ency

N(1)

Mean 2.404Standard Error 0.050026Median 2Mode 2Standard Deviation 1.118609Sample Variance 1.251287Kurtosis -0.395192Skewness 0.000384Range 5Minimum 0Maximum 5Sum 1202Count 500



State Pattern

time

Site

sN

(1)


mu 1gamma 2

delta 2

N(1)


N(1)

0

50

100

150

200

0 1 2 3 4 5

Fre

qu

ency



State Pattern

time

Site

sN

(1)


mu 1gamma 0.5

delta 0.5

N(1)

050

100

150200250

0 1 2 3 4 5

Fre

qu

ency

N(1)




Two Ongoing Research Activities

MANET/sensor net topology control

IBM / CMU

Urban sensing and data diffusion

IBM / UCLA / Cambridge



Epidemic Style Data Diffusion in Vehicular Sensor Networks (VSNs)

VSN-enabled vehic le

Inter-vehic lecommunications

Vehic le-to-roadsidecommunications

Roadside base station

Video Chem.

Sensors

Storage

Systems

Proc.



Vehicular Sensor Applications Smart-mob-approach for proactive urban monitoring using

VSNSmart mobs: people with shared interests and goals persuasively and seamlessly cooperate using wireless mobile devices

EnvironmentTraffic congestion monitoring

Urban pollution monitoring

Civic and Homeland securityForensic data for accidents or crime sites

Terrorist alerts



Accident Scenario: Storage and Retrieval

Designated cars: Continuously collect images on the street (store data locally)

Process the data and detect an event

Classify the event as Meta-data (Type, Option, Location, Vehicle ID)

Post it on distributed index

Police (agents) retrieve data from designated cars

CRASH

- Sensing - Processing

Crash Summary Reporting

Summary Harvesting



How to Retrieve the Data?

Upload to nearest AP (Cartel project, MIT)

Epidemic diffusion (our approach)

Mobile nodes periodically broadcast meta-data of events to their neighbors

A mobile agent (e.g. the police) queries nodes and harvests events

Data dropped when stale and/or geographically irrelevant



General Problem

Three phases of urban sensing & harvesting

Meta-data Dissemination

Meta-data Harvesting

Data Access

Bio inspirations:

Pheromone trails (ants foraging)

Chemotaxes (bacterial foraging)

– Motion patterns (called taxes) that the bacteria generates in prescreens of chemical attractants and repellants (nutrition gradient) (e.g., E. Coli)



Communications of Ants

Decoupling of foraging and recruitmentPheromone trail: route to food

Dance and physical contract: recruitment of additional foragers

Types of pheromone trailsNon-volatile, volatile, short-lived repellent

Sound, physical contacts (time-space constraints)Antenna, vibration, displays, dances, waggling, jerking

•Pharaoh’s ants, Monomorium pharaonis, form branching networks of pheromone trails. There the network has been formed on a smoked glass surface to aid visualization

(Image courtesy of Duncan Jackson)

•Pharaoh’s ants, Monomorium pharaonis, form branching networks of pheromone trails. There the network has been formed on a smoked glass surface to aid visualization

(Image courtesy of Duncan Jackson)



Meta-data Dissemination

Meta-data creation

Format: (Location and timestamp, data type, variable size info)

Optional local processing, e.g. Recognizing license plates + vehicle type

Dissemination

Periodically broadcast to neighbors

Can be encrypted for security/privacy issues

Prioritization

Temporal, spatial



Meta-data Harvesting

Gradient-based foragingVehicle density in urban grids is non-uniform

– More vehicles, more information: Agents are attracted via this info gradient

Need to avoid local maxima

Reinforcement learningLearn the mobility patterns over time Data-mining results can provide “feedback” to the foraging algorithm

Multiple agentsHarvesting area should be divided to minimize interferenceFor example, based on contact history (as repellents)



Data Access

Collection by agents

Similar to LER with actual mobility

Factors: physical speed of agents; coordinated swarming of agents

Collection by networks

Multi-hop pulling via Last Encounter Routing (LER)



Evaluation

Simulation Setup

NS-2 simulator

802.11: 11Mbps, 250m tx range

Average speed: 10 m/s

Mobility Models

– Random waypoint (RWP)

– Real-track model (RT) :

• Group mobility model

• merge and split at intersections

• Westwood map



Meta-data Harvesting Delay with RWP

Higher mobility decreases harvesting delay

Time (seconds)

# of

Har

vest

ed S

umm

arie

s V=25m/s

V=5m/s



Harvesting Results with Real Track

Restricted mobility results in larger delay

Time (seconds)

# of

Har

vest

ed S

umm

arie

s V=25m/s

V=5m/s



To sum up

ITA opportunity

International collaborative research on interesting topics

More understanding is required

Biology/physics camp vs. computer networks

– BIOWIRE workshop (Cambridge, UK)

– Network-based modeling, simulation vs. Analysis based on ODE

Mobility model

Questions?

© 2007 IBM Corporation IBM T J Watson Research Center Slide 1 Invited talk at KAIST, 4/30/2007 Networking Research in the International Technology Alliance.

Documents

pappas ibm

ibm corporation slide

beygelzemer ibm

us arl

university of york

networking research

mccutcheon dstl slide

university of cambridge