110608 baras comin labs keynote

8/3/2019 110608 Baras Comin Labs Keynote

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Copyright John S. Baras 2010Copyright John S. Baras 2010 1

Challenges and Opportunities for

Future Broadband Networks:

From Physical to Services to Social

John S. BarasInstitute for Systems Research

Electrical and Computer Engineering Department

Fischell Bioengineering Department

Applied Mathematics, Statistics and Scientific Computation ProgramUniversity of Maryland, USA

LABEX COMIN Kick-off MeetingJune 8, 2011

INRIA Rennes Bretagne Atlantic

Maryland Hybrid NetworksCenter (HyNet)


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Outline

Broadband Communication Infrastructures andtheir Significance

Component-Based Wireless Network Protocol

Design Dynamic Network Trust

Trusted Routing

Cross Layer and Compositional Security

Constrained Coalitional Games

An Integrated Model and FoundationalProblems


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AT&T; Cisco Visual Networking Index:

Approaching the ZettaByte Era


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Copyright John S. Baras 2010

Shaping Society and Civilization

Social significance of broadband access impacton civilized societies:

Digital divide

Information - knowledge society

Health care

Education

Economic development

Environment and habitats

4


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Next Generation Data Centers

1000x gain in performance

Exascale: Dramatically moreefficient data centers designed

across components,interconnects, power & cooling,virtualization, management, andsoftware delivery

Photonics: Replace copperwith light to transmit data


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Sustainable Data Center

Reduce data center costs on the bottom line and the environment

Reduce total cost of operationof a data center by 50% and

carbon footprint by 75%, whilemeeting Quality of Service goals

Real-time management of

data center environmentReal-time management ofservice application instances

Data center modeling, synthesisand optimization

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7/69Copyright John S. Baras 2010

OpenFlow OpenNet

Programmable networks

Open, flexible, wired and wirelessnetwork platform to enable rapid

introduction of new functionality

End-to-end quality of service,reliability, security, mobilityand management

Scalable and energy-efficientdata center networks

7



Green Communications:

Some Statistics & Facts

2 % of global energy consumption To rise to 10 % by 2020

Energy was never an issue in design & operation

Green: source & expenditure

Modes of consumption: transmission, processing,

on-status Hot spots: data centers, base stations

Our focus

Internet Wireless

8



Saving Margins

Elementary computation task: C-bit

Current technology: ~ 10-8 10-9 Joules/C-bit

Potential bound: 10-21 10-23 Joules/C-bit(from thermodynamics)

Quantum-limit: zero!(Reversible computation) (At infinite delay cost)

Can run networks worldwide 10,000 more energy efficiently--set target at 1,000

Means: run them for three years with the same energy it takes today for a day!9



Broadband Wireless :

A glimpse into the future

LTE, WiMax technologies and beyond: multiple MBps

to the mobile user

Increasing to pervasive appearance of

infrastructureless networks Self-configurable networks

Self-monitoring

Distributed dynamic content depositories

Distributed security

New technologies and materials for miniaturization

10



Broadband Wireless:

Shaping Societies and Civilization

11



While the last 50 years have been dominated by a march to ever

more complex computers, the next few decades will see the rise

of simple sensors -- by the billions. Business Week

Wireless Sensor Networks (WSN)

Sensor and Sensor networks are becoming ubiquitous

Embed numerous distributed devices to monitor andinteract with physical world

Exploit spatially and temporally dense, in situ, sensingand actuation

Networkthese devices so that they coordinate toperform higher-level identification and tasks.

Distributed & large-scale like the Internet - but, physicalinstead of virtual, resource constrained, and with real-time constraints

12
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Energy Efficient

and Intelligent Buildings

DigitalVideo surveillance Access control

Intrusiondetection

Fire alarm

Alarms management

Energy /Inventory .Management
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The Global Health Care Crisis The current system is unsustainable

Many economic, social, medical reports support thisconclusion

NAE-NIM Report (2005): Engineering andtechnology can help

IVA workshop (2007): Technology, Economics andHealthcare

IT can play a role towards the desired goal: High quality healthcare for all at low cost

Patient participation a must Preventive medicine a must

14

Health IT and Wireless Networksand Devices



HEALTH IT Components

Broadband Hybrid Communication Networkswith widely available access

Universal patient records and dissemination Universal logistics support (insurance,databases, accounting, case management)

Web-based services Mini-clinics and inexpensive tests and consultations Social, behavioral aspects Hospital information and management systems Multimedia systems, robotics, tele-surgery, new operating rooms

Health care management systems Security, trust, authentication and privacy

15

I would like more Systems Engineering principles for Health Care

Harvey V. Fineberg, President of the Institute of MedicineInnovation in Medical Technology, Whiting-Turner Lecture 04/21/09



Forthcoming

16



Forthcoming

Cell-phone Microscopy

17



Convergence = New Home

Health Platform

Digital home entertainment infrastructure can be usedfor health

Everyday health through everyday devices

Personalized, proactive health info/reminders/agents

INTEL 18



Broadband Wireless

Benefits to Society

Health Care

Much higher quality health care at lower cost and

much wider availability

Essential for preventive maintenance basedhealthcare

Essential for health care in rural and underdeveloped

areas (almost 95% of the current earths population

and locations)

Patient education and awareness

Physician, nurse and hospital training

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Outline


Component-Based Wireless Network ProtocolDesign

Dynamic Network Trust

Trusted Routing



An Integrated Model and FoundationalProblems


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Most Promising Technologies

Cross-layer optimization Key challenge: automated ways to dynamically coordinate layers for best

QoS

Dynamic network topologies, exploiting radio capabilities and environmentinformation

Multiple networks optimized for different loads (stream, data, broadcast,unicast, etc.)

Adaptive MAC (spectrum, MIMO, beamforming)

New physical layer concepts and designs interference mitigation

Each node has multiple networks available

Key challenge: automated ways to dynamically connect to networks forbest QoS

Multiple routes (robustness and availability)

Dynamic spectrum based on probing feedback

Diversity in frequency

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DYNAMIC INTERCONNECTION AND

INTEROPERABILITY

Broadband wireless networks capable formultiple dynamic interface points

Any node can serve as

interface/gateway

Fixed orhybridbroadband

Key challenge:component - basednetworking

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COMPONENT- BASED NETWORKING

How to synthesize resilient, robust, adaptive network protocols?

Component-Based Networking (CBN)

Components: modularity, cost reduction, re - usability,

adaptability to goals, new technology insertion, validation and

verification

Interfaces: richer functionality intelligent/cognitive networks

Theory and Practice of Component-Based Networking

Heterogeneous components and compositionality

Performance of components and of their compositions

Back and forth from performance - optimization domain to correctness

and timing analysis domain and have composition theory preserving

component properties as you try to satisfy specs in both domains

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COMPONENT-BASED NETWORKING

ExecutableModels

PerformanceModels

FormalModels

Each Block hasComponents

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MODEL-BASED DESIGN TOOL

Inputs, components, design parameters, sensitivity analysis, optimization.

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Copyright John S. Baras 20106/8/2011

MAC AND ROUTING COMPONENTS

Routing Components routing protocols like OLSR [Baras08]

Neighbor Discovery Component (NDC)

Selector of Topology Information to Disseminate Component (STIDC)

Topology dissemination Component (TDC)

Route Selection Component (RSC)

MAC Components based on CSMA-CA MAC protocols like IEEE802.11 [Baras08], and on schedules based MAC (USAP) [Baras09]

Scheduler

MAC

Objective Design MANET adaptable to missions with predictable performance

Approach Break traditional layers to components! Develop component-based

models MANET that considers cross-layer dependency to improve theperformance

Study the effect of each component on the overall MANET performance

Copyright John S. Baras 2010 26


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STIDC Benefits and Approach

STIDC selects a subset of links to be broadcasted

STIDC is a local pruning method for link selection

STIDC reduces the broadcast storm problem of TDC OLSR uses set cover methods for MPR selection

There are metrics that capture the stability of the MANET links

Stable Path Topology Control (SPTC) that accounts for stabilitymetrics in link selection

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h

Traditional Link-State Routing

Neighbor Discovery

Component (NDC)

Topology Dissemination

Component (TDC)

h

28

C d Li k St t R ti


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hh

Compressed Link-State Routing

Topology Control

Neighbor Discovery

Component (NDC)

Topology Dissemination

Component (TDC)

Selector of Topology

Information to

Disseminate (STIDC)

h

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Local View and Global View

- local view is a subgraph of Ginduced by the k-hop neighbors of k,excluding the arcs of the strict k-hopneighbors.

Ghlocal

Ghglobal Gh

local Gbroadcast

Every host vertex h broadcastsa selective subset of the out-arcs. This forms a broadcastgraph

.Gbroadcast j3

j2

j6

j4

j5

j10

j9j12

j11

j8

j7

j13

j14

j15 j16

j17

j18

j19

j20

j21

j23 j24

j22 j1

h

30

Global view

T l C t l f Q S


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Topology Control for QoS

Rule-Based Routing

Does preserve the QoS optimal

paths for routing from h to every

destination?

Ghglobal

31

- local view is a subgraph of Ginduced by the k-hop neighbors of k,excluding the arcs of the strict k-hopneighbors.

Ghlocal

Ghglobal Gh

local Gbroadcast

Every host vertex h broadcastsa selective subset of the out-arcs. This forms a broadcastgraph

.Gbroadcast j3

j2

j6

j4

j5

j10

j9j12

j11

j8

j7

j13

j14

j15 j16

j17

j18

j19

j20

j21

j23 j24

j22 j1

h

Global view


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ETX Link Stability Metric

df forward delivery ratio

dr reverse delivery ratio

32

u v

df

dr

ETX(u, v) 1

df dr


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OLSR-ETX

OLSR-ETX uses the ETX metric to select the

pruned edge set, .

33

hpruned

The best ETX metric for a two hop neighbor jminiNh

1ETX(h,i) ETX(i, j)

OLSR-ETX

o chooses a minimal subset of one-hop neighbors

o such that all two-hop neighbors are reachable by theirbest ETX metric path

Note: best ETX path is of the form (h,i,j), i is a one-hop neighbor!

QoS (Path Stability) Preserving


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QoS (Path Stability) Preserving

Topology Control

xijG minpPijG wp

o The optimal path stability is

o Path stability metric of a path p in G is the additivecomposition of its link stability metrics, :auv ETX(u, v)

wp

auv

(u,v)p

34

o Does OLSR-ETX pruning preserve the optimally stablepath?


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3 Platoon Mobility Scenario

35

OLSR-ETX SPTC-ETX

Saturation

CL

~ 2 Mbps ~ 2 Mbps

TC message

rate

923 kbps 890 kbps

Long connection from 20 to 0 (platoonheads)

Type Connection Offered-load

Intra-

platoon

(1,3),(2,9),(4,6),(7,5),(20,

29),

(14,17),(16,11),(17,18),(19,12),

(21,22),(23,27),(23,28)

12 kpbs

Inter-

platoon

(1,18)

(20,11),(20,0)

(10,1),(21,10)

2.4 kbps

6 kbps

12 kbps


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Outline




Trusted Routing


Constrained Coalitional Games An Integrated Model and Foundational

Problems

Distributed Trust Management in Wireless


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Distributed Trust Management in Wireless

Autonomic Networks

Distributed trust in autonomic networks Trust document distribution

Trust (and Mistrust) spreading and dynamics

Effects of topology on convergence (small world graphs)

Trust as incentive for collaboration link with economic,social and biological network analysis

Trust evaluation: direct and indirect ways; reputations,

profiles

Trust, reputation, recommender systems in web-based

social networks and services

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Trust Credential Distribution

No centralized trusted party Trust credentials are scattered in the network

Problems: Where and how to find all needed credentials?

Where and how to store credentials so that the searching is efficient?

AB

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Related Work

The problem of trust credential distribution shares manycharacteristics with P2P file sharing systems Freenet based credential distribution scheme [Eschenauer,

Gligor and Baras, 2002]

Network coding based file sharing has been shown to beefficient and based on local information only [Gkantsidis andRodriguez, 2005]

A B

G

F

E

DC

A sends outrequest for

certificates of R

G has certificate

G --> R

E has certificate

H --> R

1

7

2

4

3

5

6

Request

Reply w ith certificate

Reply f or no certifcate

Uses hashed keywordrouting, instead of flooding

Replication of credentialwhere needed via caching

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Network Coding Based Scheme

Main idea: Each user only communicates with a small subset of users

(neighbors)

A user frequently checks with its neighbors for new credentials

Whenever a user forwards trust credentials, it produces a linearcombination of all the credentials it currently stores and thecombined documents it has received from its neighbors

For mdistinct documents, a user can recover them after receivingmcombined documents for which the coefficient vectors arelinearly independent

Advantage: Only local interactions -- all operations are decentralized

No request-response procedure

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Operation Diagram

Coefficient vector

transmitted to user D

combDoc2

User C

Cred4

Cred1

Cred2

User A

User B

combDoc1

a1 a

2

combDoc3User D

b1

b2

a1

a2

1 2 3 1 1 2 2[ , , ] [ ' , ' ,0] [0,0,1]c c c b a a b

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Effectiveness

Key question: how effective the credential distributionscheme is?

50 credentials in thenetwork

Results: 60 combineddocuments areenough to recover all

50 credentials

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Simulation

Compare network coding based scheme and Freenet-basedscheme

Time to finish document distribution

Number of users who obtained documentsneeded vs simulation time

Network coding basedscheme is more efficient indistributing credentials,

i.e., smaller finish time

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Trust evaluation, trust and mistrust dynamics

Spin glasses (from statistical physics), phase transitions

Indirect trust; reputations, profiles; Trust computation via lineariterations in ordered semirings

Direct trust: Iterated pairwise games on graphs with players of many

types

( 1) , ( ) |i ji j is k f J s k j N

2 31a b

ba

2007 IEEE Leonard Abraham PrizeNew Book , 2010, Path Problemsin Networks

44

O li


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Outline




Trusted Routing



Problems

Pareto Optimal


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Two objectives for routing Two classical

problems

Delay shortest path problem (min,+)

Dual Trust spanning tree problem (min,max)

minpPij

f1(p) minpPij

d(u, v)(u,v)p

minpPij

f2 (p) minpPij

max(u,v)p

t(u, v)

Pareto Optimal

Trusted Routing

46

Haimes Method for


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minParetopPij

d(u, v)(u,v)pmax

(u,v)pt(u, v)

?

minpPij

d(u, v)(u,v)p

subject to max(u,v)p

t(u, v)

minpPij

max(u,v)p

t(u, v)

subject to d (u, v)(u,v)p

Haimes Method for

Trusted Routing (cont.)

47

Haimes Method for


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Constraint

minpPij

d(u, v)(u,v)p

subject to max(u,v)p

t(u, v)

max(u,v)p

t(u, v)

(u, v) p, t(u, v) EdgeExclusion

Haimes Method for

Trusted Routing (cont.)

48

Haimes Method


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Haimes Method

Two Stage Recipe

G (V,E)

Source

1. G reduced graph O(|E|) 2. GSP SP on reducedgraph

O(|V|.|E|)

49

Examples of Idempotent


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Semiring Algebraic Path Problem

Max likelihood

Shortest Path

Widest Path

Most Reliable Path

Shared link attributes

Shared path attributes

Examples of Idempotent

Semirings

( , min, )

_

( , max, min)

([0,1],max,)

(2W, U, I )

(2W, I , U)

([0,1],max,)

50

O tli


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Outline




Trusted Routing



Problems

Security Authentication Trust


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Security, Authentication, Trust

Universally Composable Security when possible?

Software components and interfaces -- Design interfaces carefullyand robustly major doors of attacks

Utilize to advantage the physical layer (vastly ignored todate)

Wave form, RF and hardware peculiarities lead to unshakeable

fingerprints

Authenticate the device to the network and then the user to the

device reduces attack risk (fewer times through the net)

Distribute assurance function across software and hardware (increasesdifficulty to attacker immensely)

Trusted platform module (TPM) architecture modifications to allowmultiple sources input (including biometrics) open

TPM chip add on to portable devices (TCG, TCN)

Chip authentication

Distributed communal trust monitoring : Know thy neighbors well, butwatch them maintain assured neighborhood information

52

S it A th ti ti T t ( t )


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Security, Authentication, Trust (cont.)

Cross-layer trust computation across the network

Distributed, self-checking, trust dynamics, topology effects

Include trust in routing via path metrics

Distributed control around compromised neighborhoodscontainment

New distributed hybrid systems methods for IA and trustevaluation, combine logic and statistics Combining distributed model checking and theorem proving techniques

Use natural randomness and other signatures for ID-based keying

Design of distributed dynamic recommender and reputationsystems

Using TPM, TCN, to implement specification-based policies andtesting of policies

Trusted platforms in social networks

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Physical Layer Security:


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Instead of multiplexing

the authentication

We superimpose it

And write

s the message and t the authentication tag

Current research: Extensions to Multicarrier LTE and WiMAX


Perturbed Modulation

t

s

54

E i l V lid i


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Experimental Validation

Demonstrated Very Low Power Authentication is Feasible

55

E i t l R lt


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Experimental Results

56

Extension to Multicarrier

LTE and WiMAX



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y y y

Trusted Computing

Trusted Platform Module (TPM)

Protects the integrity and confidentiality of datawith hardware support

Performs integrity measurements and reports them,thus attesting for the software running in the device

Source: TCG Architecture Overview, http://www.trustedcomputinggroup.org

57Copyright John S. Baras 2009

Outline


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Outline




Trusted Routing



Problems

A k i


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A Network is

A collection of nodes, agents,

that collaborate to accomplish actions, gains,

that cannot be accomplished with out such

collaboration

Most significant concept for autonomic

networks

59

Th F d l T d ff


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The Fundamental Trade-off

The nodes gain from collaborating

But collaboration has costs (e.g. communications)

Trade-off: gain from collaboration vs cost of

collaboration

Vector metrics involved typically


60

Example 1: Network Formation -- Effects on Topology

Example 2: Collaborative robotics, communications

Example 3: Web-based social networks and services

Example 4: Groups of cancer tumor or virus cells

G i


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Gain

Each node potentially offers benefits V per time unit toother nodes: e.g. Vis the number of bits per time unit.

Potential benefit V is reduced during transmissions due to

transmission failures and delay

Jackson-Wolingsky connections model, gain of node i

rij is # of hops in the shortest path between i and j

is the communication depreciation rate

1

( ) ijr

ij g

w G V

0 1

if there is no path between andijr i j

61

C


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Cost

Activating links is costly

Example cost is the energy consumption for sending data

Like wireless propagation model, cost cijof link ijas a

function of link length dij:

P is a parameter depending on the transmission/receiver antennagain and the system loss not related to propagation

is path loss exponent -- depends on specific propagation

environment.

ij ijc Pd

62

P i i G d C


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Pairwise Game and Convergence

Payoffof node i from the network is defined as

Iterated process

Node pair ij is selected with probability pij If link ij is already in the network, the decision is whether to sever it,

and otherwise the decision is whether to activate the link

The nodes act myopically, activating the link if it makes each at least as

well off and one strictly better off, and deleting the link if it makeseither player better off

End: if after some time, no additional links are formed or severed

With random mutations , the game converges to a unique Pareto

equilibrium (underlying Markov chain states )

( ) gain cost ( ) ( )i i i

v G w G c G

63

G

Coalition Formation at the


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Stable State

The cost depends on the physical locations of nodes

Random network where nodes are placed according to a uniform

Poisson point process on the [0,1] x [0,1] square.

Theorem: The coalition formation at the stable state for n

Given is a

sharp threshold for establishing thegrand coalition ( number ofcoalitions = 1).

For , the threshold is

less than

2

0

ln,

nV P

n

0 1 2

ln.

nP

n

n = 20

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Topologies Formed


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Topologies Formed

65

Outline


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Outline




Trusted Routing



Problems

Dynamic Integrated Networks:


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y g

Humans, Machines, ICT,

Multiple Interacting Multigraphs

Nodes: agents, individuals, groups,organizations

Directed graphs

Links: ties, relationships

Weights on links : value (strength,

significance) of tie Weights on nodes : importance of

node (agent)

Value directed graphs withweighted nodes

Real-life problems: Dynamic,time varying graphs,relations, weights

67

Social/Cognitive

Information

Comms

S

ijw : Sii w

:S

jj w

I

klw:I

kk w :

I

ll w

C

mnw:

C

mm w : Cn

n w

Organizational needs

Network architecture

and operation

Network Complexity:


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Copyright John S. Baras 2010 68

p y

Four Fundamental Challenges

Multiple interacting dynamic multigraphs involved Collaboration multigraph: who collaborates with whom / when

Communication multigraph: who communicates with whom / when

Effects of connectivity topologies:

Find graph topologies with favorable tradeoff between

performance (benefit) vs cost of collaborative behaviors Small word graphs achieve such tradeoff; Expander graphs;

Components, Interfaces, Compositional Synthesis Network protocols component based networking

Compositional Universal Security

Need for different probability models the classical Kolmogorovmodel is not correct Probability models over logics and timed structures

Logic of projections in Hilbert spaces not the Boolean of subsets of a set



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Thank [email protected]

301-405-6606

http://www.isr.umd.edu/~baras

Questions?
http://www.isr.umd.edu/~barashttp://www.isr.umd.edu/~baras

110608 baras comin labs keynote

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