research in mobile wireless networks -...

Research in Mobile Wireless Networks

Asia School on Future Internet Jeju Korea, Aug 27, 2008

Mario Gerla, CS Dept, UCLA

Wireless Network Projects

Large scale ad hoc nets ‐ Battlefield, disaster recovery, etcVehicular nets

Alarm broadcast, Routing, Content delivery, Urban sensing, security

Personal Area Networks (Bluetooth)Health NetContent distribution (Bluetorrent)

Underwater sensor netsSea Swarms

Topics covered today

Vehicular Communications

Delay Tolerant Geo RoutingGeo Location ServiceCampus Vehicle Testbed

ISVCS 2008, Dublin, July 2008

Pei‐Chun Cheng, Jui‐Ting Weng, Lung‐Chih Tung, Kevin C. Lee, Mario Gerla, Jerome Harri

8/27/2008 ISVCS 2008 4

GeoDTN+Nav: A Hybrid Geographic and DTN Routing with

Navigation Assistance in Urban Vehicular Networks

OutlineBackgroundGeo‐routing Related WorksNavigation‐Assist Model – VNIGeoDTN+NavEvaluationConclusion and Future Work

8/27/2008 ISVCS 2008 5

Georouting ‐ Key IdeaEach node knows its geo‐coordinates (eg, from GPS or Galileo)Source knows destination geo‐coordinates; it stamps them in the packetGreedy geo‐forwarding: at each hop, the packet is forwarded to the neighborclosest to destinationEach node keeps track of neighbor coordinates via beaconing of {MAC ID, geo coordinates}

Got stuck in a max? =>GPSR (Greedy Perimeter Stateless Routing)

Perimeter mode: forward following the right-hand rule on a planar graph. Cross links may cause GPSR to fail.Planarization algorithm can be extremely time consuming..

Planarization examples

GPCR (Greedy Perimeter Coordinator Routing)‐ for urban geo‐routing

Key observation: In urban environment, no planarization is required: urban city map naturally forms a planar graphEach road segment is the edge of a planar graph while nodes at junctions are vertices. Routing decisions are made only at junctions; between junctions, packets are simply forwarded to next junction.

GPCR (cont)When junction nodes are missing, packets will be forwarded across junctions This may cause non planarityproblem is solved by detecting loops and removing cross links

Geo Routing well suited to VANETs

(Vehicle Ad Hoc Networks)

Very robust to motionMotion prediction

Extremely scalable to network size (no link state updates, no flooding); also, no planarizationEasy to support in “Future Internet” mobile devices:

Vehicles, smart phones are GPS equippedGPS accurate enough for routingin GPS deprived areas, alternatives available (Inertial navigation, signal strength+trilateration, virtual coordinates etc)

Geo Location Service can be effectively maintained within the wired infrastructure through overlay (more on this later)

HOWEVER….

VANETs may become disconnected!!!

Conventional routing protocols assume connected networksConnectivity not always guaranteed in VANETIntermittent connectivity caused by:

Low vehicle densityObstaclesIrregular traffic pattern

Evolving networkVehicle movement

8/27/2008 ISVCS 2008 12

Intermittent operation

8/27/2008 ISVCS 2008 13

Packet delivery is by and large decided by connectivity in the vehicular network

Traffic Pattern

Obstacles

Low Density

Enter GeoDTN+Nav routingAims at improving packet delivery in a disconnected VANETInspired by:

DTN (Delay Tolerant Networking)Not all applications require real‐time delivery

Mobility assisted dissemination, eg 7DS, ZebraNet, “data muling”

Basic idea:Exploit vehicles’ mobilityStore/carry/forward packets like in DTNs

Delivers packets across disconnected partitions

8/27/2008 ISVCS 2008 14

Mobility Helps!

8/27/2008 ISVCS 2008 15

OutlineMotivationDTN Related WorkNavigation‐Assist Model – VNIGeoDTN+NavEvaluationConclusion and Future Work

8/27/2008 ISVCS 2008 16

DTN routing schemesGeOpps: Vehicles equipped with navigation system: GPS device, maps, suggested routes and estimated delaysGreedily forward packets to nodes which will be closest to the destination within shortesttime

In example, packet C is chosen

8/27/2008 ISVCS 2008 17

OutlineMotivationGeo‐routing Related WorksNavigation‐Assist Model – VNIGeoDTN+NavEvaluationConclusion and Future Work

8/27/2008 ISVCS 2008 18

GeoDTN+NavBasic idea:

Exploit mobility to help deliver packets across disconnected networks

The problem now is which neighbor to choose?

Blind random choiceMight not helpNeighbors may move even farther away from the destination

Informed choiceUse knowledge of neighbors’ destination and/or path

8/27/2008 ISVCS 2008 19

How to learn about neighbors’ destinations and paths?

Navigation System can HelpInspired by GeOpps:

Exploit navigation systemHarvest neighbors’ dest/path information

AssumptionEvery vehicle has a navigation systemNavigation system is a common accessoryProvides real‐time traffic information

8/27/2008 ISVCS 2008 20

Classify Vehicles’ Mobility

8/27/2008 ISVCS 2008 21

Bus

Taxi

w/ Navigation

w/o Navigation

Classify Vehicles’ MobilityCategories Example

Deterministic(Fixed) Route Big Blue Bus, UCLA Shuttle,

Deterministic(Fixed) Destination Taxi, UCLA Commuter Van

Probabilistic(Expected) Route / Destination Navigation system guided vehicle

Random, unpredictable route Driver without Nav System

8/27/2008 ISVCS 2008 22

We propose a “Virtual Navigation Interface” to generalize these vehicle categories

Virtual Navigation InterfaceAssume VNI is installed on every vehicleA lightweight wrapper interface: interacts with on‐board data sourcesProvides two sets of values:

Route infoDestinationPathDirection

Confidence0% (Random) ~ 100%(Deterministic)

8/27/2008 ISVCS 2008 23

Virtual Navigation Interface

8/27/2008 ISVCS 2008 24

BusVNI : (Path, 100%)

TaxiVNI : (Dest, 100%)

w/ NavigationVNI : (Path, 25%)

w/o NavigationVNI : (?, 0%)

w/ NavigationVNI : (Dest, 75%)

OutlineMotivationGeo‐routing Related WorksNavigation‐Assisted Model – VNIGeoDTN+NavEvaluationConclusion and Future Work

8/27/2008 ISVCS 2008 25

GeoDTN+NavIntroduce third forwarding mode in geo‐routing

DTN recovery modeComplements conventional two‐mode geo‐routing

Three routing modesGreedyPerimeterDTN

8/27/2008 ISVCS 2008 26

AlgorithmEach node periodically broadcasts VNI’s two‐tuple navigation information

“Route_info, Confidence”Execute conventional Greedy/Perimeter mode forwardingWhile in Perimeter forwarding

You assume the destination is disconnected with some probability (which increases with permanence in perimeter)Check neighbors’ navigation informationSwitch to DTN mode if 1. Destination disconnection probability is high2. There are neighbors which can deliver packets closer to

destination8/27/2008 ISVCS 2008 27

When to switch to DTN?In perimeter mode

Current node CNeighbors Ni (i=1~n)Hops h

Compute a “switch score” for each neighbor with

Scoring function SSwitch threshold Sthres

8/27/2008 ISVCS 2008 28

RULE:If there exists Ni, such that S(Ni) > Sthres and S(Ni) > S(Nj), i j for all j

• Switch to DTN mode• Forward the packet to Ni• Ni stores and carries the packet until it can “resume” greedy mode

Score functionScore Function for N i :

S(Ni) = αP(h) • βQ(Ni) • γDir(Ni)

Where

P(h): (0 ~ 1) Probability that destination is disconnectedQ(Ni): (0 ~ 1) forwarding quality ( to destination) by N iDir(Ni): (0 ~ 1) Direction projection towards destination

8/27/2008 ISVCS 2008 29

P(h)Disconnection prob = “traversed hop counts”Linear probability function after offset h min

8/27/2008 ISVCS 2008 30

Q(Ni)Calculate Ni’s forwarding quality

Navigation information Confidence

8/27/2008 ISVCS 2008 31

Dir(Ni)Determine Ni’s “routability”: Can Ni carry the packets?

Ni’s direction wrt destinationCurrent node’s direction wrt destination

8/27/2008 ISVCS 2008 32

Letα = β = γ = 1

Sthres = 0.25

Example

8/27/2008 ISVCS 2008 33

Q(N1) = 0.1S(N1) = P(8) * Q(N1)

= 0.04

P(9) = 0.5

Q(N2) = 0S(N2) = 0

P(8) = 0.4

Q(N3) = 0.6S(N3) = 0.24

Q(N1) = 0.2S(N1) = 0.01

Q(N2) = 0.7S(N2) = 0.35

Q(N3) = 0.6S(N3) = 0.30

OutlineMotivationGeo‐routing Related WorksNavigation‐Assist Model – VNIGeoDTN+NavEvaluationConclusion and Future Work

8/27/2008 ISVCS 2008 34

Evaluation – SetupQualnet 3.95, 802.11b, transmission rate 2Mbps, radio range 350m

α = β = γ = 1, Sthres = 0.25Synthetic scenario:

Artificial topology to create two separate partitionsStatic nodes randomly placedMobile nodes travel at 50km/hrLoad varies from 5 to 40 nodes; 10 runs each load.

Mobile nodes set off from location A to B at fixed or random intervalsFixed source and destinationCompare GPCR and GeoDTN+Nav in PDR, hop count, and latency

8/27/2008 ISVCS 2008 35

Evaluation – Setup (cont.)Realistic scenario:

San Francisco to Oakland, 1500m by 4000mMobile nodes generated by VanetMobisimAdditional mobile nodes generated (5 to 40) artificially to increase intermittent connectivityInjected at uniform or random intervalsCompare GPSR, GPCR, and GeoDTN+Nav in PDR, hop count, and latency

8/27/2008 ISVCS 2008 36

Evaluation – Synthetic Scenario (PDR)GeoDTN+Nav has above 70% PDR in both random and uniform setoff timeAlmost Fullconnectivity explains the sharp increase in PDR for GPCR/UNIM

8/27/2008 ISVCS 2008 37

0

0.2

0.4

0.6

0.8

1

5 10 15 20 25 30 35 40

Pack

et D

eliv

ery

Rati

o

Number of 'Bus' Nodes

GPCR/RAND

GeoDTN+NAV/RAND

GPCR/UNIM

GeoDTN+NAV/UNIM

Evaluation – Synthetic Scenario (Hop Count)

GeoDTN+Nav has higher hop count due to higher PDRCloseness in PDR explains the hop count convergence of GPCR/UNIM and GeoDTN+Nav/UNIM from 25 onward

8/27/2008 ISVCS 2008 38

0

5

10

15

20

5 10 15 20 25 30 35 40

Number of Hops

Number of ’Bus’ Nodes

GPCR/RANDGeoDTN+NAV/RAND

GPCR/UNIMGeoDTN+NAV/UNIM

Evaluation – Synthetic Scenario (Latency)

GeoDTN+Nav’s latency drops as more mobile nodes increase connectivityLatency for GPCR remains close to 0 because most of the src/dest pairs are one‐hop away

8/27/2008 ISVCS 2008 39

0

50

100

150

200

250

5 10 15 20 25 30 35 40

Latency (sec)


GPCR/RANDGeoDTN+NAV/RAND

GPCR/UNIMGeoDTN+NAV/UNIM

0

0.1

20 25 30

Evaluation – Realistic Scenario (PDR)GeoDTN+Nav has higher PDR in both random and uniform setoff time than GPSR and GPCR

8/27/2008 ISVCS 2008 40

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

5 10 15 20 25 30 35 40

Packet Delivery Ratio


GPSR/RANDGPCR/RAND

GeoDTN+NAV/RANDGPSR/UNIMGPCR/UNIM

GeoDTN+NAV/UNIM 0

0.1

5 10 15

Evaluation – Realistic Scenario (Hop Count)GeoDTN+Nav has higher hop count due to higher PDR1 hop for GPSR and GPCR shows that there is NO connectivity except src/dest pair that is one hop away!!!GeoDTN+Nav operates under intermittent connectivity!

8/27/2008 ISVCS 2008 41

0

5

10

15

20

25

30

5 10 15 20 25 30 35 40

Number of Hops


GPSR/RANDGPCR/RAND


GeoDTN+NAV/UNIM

0

1

2

5 10 15

Evaluation – Realistic Scenario (Latency)GeoDTN+Nav’s latency increases as hop count increasesGPCR’s latency remains 0 for 1‐hop src/dest pairs

8/27/2008 ISVCS 2008 42

0

50

100

150

200

250

300

5 10 15 20 25 30 35 40

Latency (sec)


GPSR/RANDGPCR/RAND


GeoDTN+NAV/UNIM

0

0.1

5 10 15

OutlineIntroductionGeo‐routing Related WorksNavigation‐Assist Model – VNIGeoDTN+NavEvaluationConclusion and Future Work

8/27/2008 ISVCS 2008 43

Conclusion and Future WorkNavigation Assist Model – Virtual Navigation Interface (VNI)

Provide generalized navigation informationDifferent level of information for privacy: Path, Destination, DirectionConsider uncertainty: Confidence

A hybrid geo‐routing protocol with DTN modeUse DTN to recover to greedy modeUse VNI to select the appropriate “mule” for DTN mode

Future work:Parameter optimization: setting switch score threshold adaptively based on topology connectivityMotion pattern and data pattern impactCache policy: How much to cache? What is the right buffer capacity?

2008/8/27 ISVCS 2008 44

Questions?

Thank you!

8/27/2008 ISVCS 2008 45

Additional InformationHow to determine the scoring threshold to switch to DTN mode?Use analytical model to determine parameters:

Gx: probability of switching to greedy mode after x hopsTx : probability of switching to DTN mode after x hopsSthres

8/27/2008 ISVCS 2008 46

Analytic Model(1)Markov Chain in “Perimeter Mode”

MetricsPacket Deliver Ratio = N1 + (1‐N1)P1 +

(1‐N1)(1‐P1)N2 + (1‐N1)(1‐P1)(1‐N2)P2 + ….Latency = N1Dg + (1‐N1)P1Dd +

(1‐N1)(1‐P1)N2 Dg+ (1‐N1)(1‐P1)(1‐N2)P2Dd+ ….

8/27/2008 ISVCS 2008 47

N(x): Prob. recover to Greedy modeP(x): Prob. Switch to DTN modeDg: Deliver latency in Greedy modeDd: Deliver latency in DTN mode

Goal: Find P(x) that achieves high packet deliver rate while minimizing latency

Analytic Model(2)Simplified assumption

Consider only one packet in perimeter modeThis packet will be successfully delivered once it is recovered to greedy mode or switch to DTN modeNi and Pi are probability functions, but here assume Ni(x) is fixed N

ConfigurationsTTL = 16 hopsDg = 5 (time units)Dd = 5, 50, 500 (time units)N = 0.1, 0.001

8/27/2008 ISVCS 2008 48

P vs. PDR/Latency (Connectivity)

8/27/2008 ISVCS 2008 49

Conclusion

• P ↑ PDR ↑Latency ↑

• When N is highP should be chosen carefully

• In this model, P is optimal between 0.15 and 0.2

• P higher than 0.2 is meaningless

P vs. PDR/Latency (DTN Delay)

8/27/2008 ISVCS 2008 50

Conclusion

• P ↑ PDR ↑Latency ↑

• When DTN delay is high, P should be chosen carefully

•Be conservative

• In this model, P is optimal between 0.15 and 0.2

Super car

ReferenceBrad Karp H T Kung. GPSR: Greedy Perimeter Stateless Routing for Wireless Networks. Mobicom 2000Young‐Jin Kim, Ramesh Govindan, Brad Krap, Scott Shenker. On the Pitfalls of Geographic Face Routing. Networked Systems Design & Implementation 2005C. Lochert, M. Mauve, H. Fubler. H. Hartenstein. Geographic Routing in City Scenarios. ACM SigMobile 2005

Young‐Jin Kim, Ramesh Govindan, Brad Krap, Scott Shenker. Geographic Routing Made Practical. Discrete Algothrithms and Methods for MOBILE Computing and Communications 2005

Kevin C. Lee, Jerome H¨arri, Uichin Lee, Mario Gerla. Enhanced Perimeter Routing for Geographic Forwarding Protocols in Urban Vehicular Scenarios. AutoNet 2007

Ilias Leontiadis and Cecilia Mascolo. GeOpps: Geographical Opportunistic Routing for Vehicular Networks

8/27/2008 ISVCS 2008 51

Geo Location ServiceWhat is missing in GeoDTN?

To georoute to a host I must have its x,y coordinatesGeo Location Service (GLS)

Provides Mobility management to georouting usersMaps vehicle ID (eg,license plate) to X,Y coordinates

Infrastructure GLS Options:Mobile IP type solution ‐ vehicles register coordinates with private home agentsDNS solution: centralized or distributed

Wireless GLS Option:Totally contained in the wireless grid (eg.Geo DHT)Important in case of infrastructure failure

Infrastructure based Overlay Location Service (OLS)

Distributed DNS Vehicular ID hashed into overlay DHTMapping: Vehicular ID location, time, speed, AP

IP address, etc

Georouting through the infrastructure

IPv6 addressing (destination xy coordinates in header extension)

How to make the system resilient to failures/attacks?Maintain both the Infrastructure OLS and a “mirror” wireless GLS If access points fail, use GLS implemented in wireless urban grid

Testbeds

Why a Testbed?Designer needs more than simulationTestbed helps understand:

Realistic user behavior in reaction to motion, data etcRealistic channel behavior with new advanced radios (MIMO, SDR)Real implementation/HW constraints

Helps Uncover:interactions between layersIncorrect common beliefs

Helps Assess:HW, SW, Mgmt costs and performance

Helps compare:Different algorithms in consistent motion pattern and propagation conditions by using node virtualization and slicing

CC‐‐VVeeTTCampus Vehicular Campus Vehicular TestbedTestbed

E. Giordano, A. Ghosh, G. Marfia, S. Ho, J.S. Park, PhD

System Design: Giovanni Pau, PhD Advisor: Mario Gerla, PhD

Vehicle FleetWe are installing our node equipment in:

A dozen private cars: customized experimentsUp to 20‐30 Campus Facilities operated vehicles (including shuttles and facility management trucks).

Experiments: Controlled motion experiments with private cars Campus vehicle experiments (locally or remotely initiated) on “realistic”motion patternsOpportunistic ad hoc and infrastructure synergistic experiments

The vehicular radio: Industrial PC (Linux OS)2 x WLAN Interfaces1 Radio Interface1 Control Channel 1 GPS

Initial Demos: Equipment:

6 Cars driven on CampusClocks are in synch with the GPSOLSR for ad hoc C2C routing1 EvDO interface in the Lead Car 1 Remote Monitor connected through the Internet

Experiments:Connectivity map though OLSRBit Torrent content sharingOpportunistic multihop access to AP’s

The V2V testbed

6‐Car Caravan on CAMPUS communicating via OLSR

On Going Vehicular Research at UCLA

V2V communications for safe navigation:Emergency Multimedia Information streaming

V2V communications for content/entertainment:Car torrent, Code torrent, Ad TorrentCar to Car Internet games

V2V for urban surveillance:Pervasive, mobile sensing: MobEyesEmergency NetworkingEvacuation

Test bed support is critical to validate above results

Future Testbed ExperimentsMIMO radiosRealistic assessment of radio, mobility characteristics on performanceRouting algorithm comparisonInteraction with (and support of ) the Infrastructure

GLS overlay implementationContent P2P sharingAlert broadcast performanceNetwork games

THE ENDTHANK YOU

research in mobile wireless networks -...

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