1 using directionality in wireless routing bow-nan cheng advisors: dr. shivkumar kalyanaraman dr....

1

Using Directionality in Wireless Routing

Bow-Nan ChengAdvisors:

Dr. Shivkumar KalyanaramanDr. Partha Dutta

2

Motivation

Main Issue: Scalability

Infrastructure / Wireless Mesh Networks

• Characteristics: Fixed, unlimited energy, virtually unlimited processing power• Dynamism – Link Quality• Optimize – High throughput, low latency, balanced load

Mobile Adhoc Networks (MANET)

• Characteristics: Mobile, limited energy• Dynamism – Node mobility + Link Quality• Optimize – Reachability

Sensor Networks• Characteristics: Data-Centric, extreme limited energy• Dynamism – Node State/Status (on/off)• Optimize – Power consumption

Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

3

Scaling Networks: OSI Model

1: Physical Layer

4: Transport Layer

3: Network Layer

2: Link Layer

Layers 5-7

Z

C

E

F

H

GA B

A Z

1011010

Physical Layer – Handles transmission of bits through a medium

Link Layer – Manages node-to-node transmissions

Network Layer – Manages routing from end-to-endTransport Layer – Handles reliable transmissions end-to-end


Application/Presentation/Session Layers – Deal with the actual programs/data

4

Scaling Networks: Trends in Layer 3

Flood-based Hierarchy/Structured Unstructured/FlatScalable

Mobile Ad hoc /Fixed Wireless Networks

DSR, AODV,TORA, DSDVPartial Flood:OLSR, HSLS

LGF, VRR, GPSR+GLSHierarchical Routing,

Peer to Peer /Overlay Networks

Wired Networks

Gnutella Kazaa, DHT Approaches: CHORD, CAN

Ethernet Routers (between AS)

WSR (Mobicom 07)ORRP (ICNP 06)

SEIZE


BubbleStorm (Sigcomm 07)LMS (PODC 05)

5

Trends: Directional Antennas

• Directional Antennas – Capacity Benefits Theoretical Capacity Improvements - factor of 42/sqrt()

where and are the spreads of the sending and receiving transceiver ~ 50x capacity with 8 Interfaces (Yi et al., 2005)

Sector Antennas in Cell Base Stations – Even only 3 sectors increases capacity by 1.714 (Rappaport, 2006)

Directional Antennas – Simulations show 2-3X more capacity (Choudhury et al., 2003)

A’

B’

C’

D’

A

B

C

D

Omni-directional Transmission

A’

B’

C’

D’

A

B

C

D

Directional Transmission


6

Trends: Hybrid FSO/THz FSO/RF MANETs• Current RF-based Ad Hoc Networks:

802.1x with omni-directional RF antennas

High-power – typically the most power consuming parts of laptops

Low bandwidth – typically the bottleneck link in the chain

Error-prone, high lossesFree-Space-Optical

(FSO) Communications

Mobile Ad Hoc Networking

• High bandwidth• Low power• Dense spatial reuse• License-free band of operation

• Mobile communication• Auto-configuration

Free-Space-OpticalAd Hoc Networks

• Spatial reuse and angular diversity in nodes• Low power and secure• Electronic auto-alignment• Optical auto-configuration (switching, routing)• Interdisciplinary, cross-layer design


7

Research Objectives• Wireless Mesh Context

Can directionality be used to address issues with scalability at higher throughput in layer 3 routing?

• Mobile Ad Hoc Context Can directionality be used to address issues with

high mobility and throughput in layer 3 routing?• Overlay Network Context

Can directionality be used to scale flat, unstructured networks?


8

By removing position

information, can we still efficiently

route packets?

Orthogonal Rendezvous Routing Protocol

L3: Geographic Routing using Node IDs (eg. GPSR, TBF etc.)

L2: ID to Location Mapping (eg. GHT, GLS etc.)

L1: Node Localization

ORRP

N/A

Issues in Position-based Schemes

S

N

W E

(0,4)

(4,6)

(5,1)

(8,5)

(12,3)

(15,5)S

D

D(X,Y)? ?


9

ORRP Big Picture

Up to 69%

A

98%

B

180o

Orthogonal RendezvousRouting Protocol

ST

ORRP Primitive1: Local sense of directionleads to ability to forwardpackets in opposite directions

2: Forwarding alongOrthogonal lines hasa high chance of intersection in area


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ORRP Design Considerations• Considerations:

High probability of connectivity without position information [Reachability]

Scalability O(N3/2) total state information maintained. (O(N1/2) per node state information)

Even distribution of state information leading to no single point of failure [State Complexity]

Handles voids and sparse networks• Trade-offs

Path Stretch Probabilistic Reachability


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ORRP Proactive and Reactive Elements

Node B Fwd Table

Dest Next Hops

A A 1120o

NorthNorth

North

North

North

Node F Fwd Table

Dest Next Hops

A B 2

Node C Fwd Table

Dest Next Hops Dir

A F 3 120o

D D 1 230o

230o

1. ORRP Announcements (Proactive) – Generates Rendezvous-to-Destination Routes2. ORRP Route Request (RREQ) Packets (Reactive) – Generates Source-to-Rendezvous Rts3. ORRP Route Reply (RREP) Packets (Reactive)4. Data path after route generation

D

CFBA


A to D

12

Reachability Numerical AnalysisP{unreachable} =

P{intersections not in rectangle}

4 Possible Intersection Points

1

2

3

98.3% 99.75%

57%

67.7%

Probability of Unreach highest at perimeters and corners

NS2 Simulations with MAM show

around 92% reachability


13

Path Stretch Analysis

Average Stretch for various topologies

• Square Topology – 1.255• Circular Topology – 1.15• 25 X 4 Rectangular – 3.24• Expected Stretch – 1.125

x = 1.255 x = 1.15

x = 3.24


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State Complexity Analysis/SimulationsGPSR DSDV XYLS ORRP

Node State O(1) O(n2) O(n3/2) O(n3/2)

Reachability High High 100% High (99%)

Name Resolution O(n log n) O(1) O(1) O(1)

Invariants Geography None Global Comp. Local Comp.

ORRP state scales with Order N3/2

ORRP states are distributed fairly evenly

in an unstructured manner

(no single point of failure)


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ORRP: Simulation Results Summary• Base Case

Reach – 99% for Square topologies, 92% for Rectangular topologies (MAM helped)

Path Stretch – Roughly 1.2 Goodput – About 30x more aggregate network goodput than AODV, 10x more

aggregate network goodput than OLSR and 35x more aggregate network goodput than GPSR with GLS (due to better usage of medium)

• Network Voids Average path length fairly constant (Reach and State not different)

• Additional Lines Reach/Path Stretch – All showed large gains from 1 to 2 lines but diminishing

returns thereafter Goodput – Higher average network throughput with additional lines (better

paths and higher reach) but not by much• Varying Number of Interfaces

Significant increase in reachability from 4 to 8 interfaces, but gains trail off


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ORRP: Summary• ORRP achieves high reachability in random topologies• ORRP achieves O(N3/2) state maintenance – scalable even

with flat, unstructured routing• ORRP achieves low path stretch (Tradeoff for connectivity

under relaxed information is very small!)• ORRP achieves roughly 30X in aggregate network goodput

compared to AODV, 10X the aggregate network goodput compared to OLSR, and 35X the aggregate network goodput compared to GPSR with GLS.

Relevant Papers• B. Cheng, M. Yuksel, and S. Kalyanaraman, Rendezvous-based Directional Routing: A Performance Analysis, In Proceedings of IEEE International

Conference on Broadband Communications, Networks, and Systems (BROADNETS), Raleigh, NC, September 2007. (invited paper) • B. Cheng, M. Yuksel, and S. Kalyanaraman, Directional Routing for Wireless Mesh Networks: A Performance Evaluation, Proceedings of IEEE Workshop

on Local and Metropolitan Area Networks (LANMAN), Princeton, NJ, June 2007. • B. Cheng, M. Yuksel, and S. Kalyanaraman, Orthogonal Rendezvous Routing Protocol for Wireless Mesh Networks, Proceedings of IEEE International

Conference on Network Protocols (ICNP), pages 106-115, Santa Barbara, Nov 2006.


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Mobile-ORRP (MORRP) Motivation

A

B

98%

ORRP• High reach, O(N3/2) State

complexity, Low path stretch, high goodput, unstructured

• BUT.. What happens with mobility?

• Issues with Mobility Interface Handoff

Issue Nodes closer

seemingly incur MORE dynamism than nodes farther away

R

~1.2 vs. SP

65%55%

42%

IncreasingMobility


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MORRP Introduction

A

B

What can we do?• Replace intersection

point with intersection region.

• Shift directions of send based on local movement information

• Route packets probabilistically rather than based on rigid next-hop paths. (No need for route maintenance!)

• Solution: a NEW kind of routing table: Directional Routing Table (DRT)

R


19

J

K

LM

IH

O P

S

N

R

Q

F

C

G

E

A

B

MORRP Basic Example

Original Path

Original Path

OriginalDirection ()

NewDirection()

R: Near Field DRTRegion of Influence

D: Near Field DRTRegion of Influence

S: Near Field DRTRegion of Influence

D

D’

D

R

R’

R


S

1. Proactive Element – Generates Rendezvous to Dest Paths2. Reactive Element – Generates Source to Rendezvous Paths

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The Directional Routing Table

DestID

NextHop

DestID

NextHop

BeamID

Dest IDs(% of Certainty)

BeamID

BCD:Z

BBZ:Z

BCD:Z

BBZ:Z

113:3

B(90%), C(30%).Z(90%), D(40%).

1234

BC

ZD

A4

1

2

3

Routing Table RT w/ Beam ID Directional RT (DRT)

ID ID ID set of IDs Set of IDs set of IDs

Routing Tables viewed from Node A

• Destination ID % of Certainties for each Beam ID stored within a Decaying Bloom Filter

• Bloom Filter – A space-efficient probabilistic data structure that is used to test whether an element is a member of a set. Consist of a bit array and a set of k linearly independent hash functions Storage: IDs are hashed to each of the k hash functions stores a ``1’’ in

position in the bit array for each hash function. Search: IDs are hashed through each of the k hash functions if all positions

have a “1”, then the ID is in the set. Otherwise, the ID is not in the set


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DRT: Decaying Bloom Filter Primer

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

h1(x) = (x2 + 20) % 32 h2(x) = x % 32 h3(x) = (x + 5) % 32 h4(x) = (x3 + 25) % 32h1(1) = 21 h2(1) = 1 h3(1) = 6 h4(1) = 26

029

030

031

ID: 1 ID: 2

h1(2) = 24 h2(2) = 2 h3(2) = 7 h4(2) = 1

1 1 1 1 1 1 1

ID: 6

h1(6) = 24 h2(6) = 6 h3(6) = 11 h4(6) = 17

Search ID 1 – 4 of 4 bits match (IN set)Search ID 6 – 2 of 4 bit match (Not in set)

Traditional Bloom Filter

Decaying Bloom Filter (DBF)Search ID 1 – 4 of 4 bits match (100% chance in set)Search ID 6 – 2 of 4 bit match (50% chance in set)


32 Bit Array:

4 HashFuncs:

A 1

234

5

67 8

Dest Prob.(DBF)

BeamID

0010..10000000..10010011..01010101..10010010..00000000..00010011..10110111..1001

12345678

DRTWhat policiesFor decayingbits can we employ?

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4

1

2

3

DRT Inter-Node Decay

Decay 50% of Bits

DNoise

CLow Info

BMed Info

AStrong InfoS

0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 …

0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 …

0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 …

0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 …

DRT at Node ABEAM ID: 1

BEAM ID: 2

BEAM ID: 3

BEAM ID: 4

Bitwise-OR0 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 … Merged DBF (Update DBF)

0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 … Decayed DBF (50% bits dropped)

CB

A

0 0 1 1 0 1 1 0 1 0 0 1 0 1 0 1 …

My ID (A)

h1(x), h2(x), …, hn(x)

Broadcasted by A to all Neighbors

B is now 100% sure A is 1 hop away while only 50% sure C can be reached through sending out interface 1


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DRT Intra-node DecayTime Decay with Mobility Spread Decay with Mobility

7

8

x

As node moves in direction +x, bits in DBF of region 8 should decay faster than of region 7 depending on speed

As node moves in direction +x, bits in DBF of region 2 should be SPREAD to region 1 and 3 faster than the opposite direction

a

a

x

Beam ID 1

Beam ID 2

Beam ID 3


0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 01 1 1 1 1 1 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 011 1 1

1 1 1 1

0 0 0 0 00 00

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N

N

N

N

N

N

N N

N

N

N

N

N

N

N

N

N

N

N

MORRP Fields of Operation

• Near Field Operation Uses “Near Field DRT” to match for

nodes 2-3 hops away• Far Field Operation

RREQ/RREP much like ORRP except nodes along path store info in “Far-Field DRT”

S R

D


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Performance Evaluation of MORRP• Metrics Evaluated

Reachability – Percentage of nodes reachable by each node in network (Hypothesis: high reachability)

Delivery Success – Percentage of packets successfully delivered network-wide Scalability – The total state control packets flooding the network (Hypothesis:

higher than ORRP but lower than current protocols out there) Average Path Length End to End Delay (Latency) Aggregate Network Goodput

• Scenarios Evaluated Affect of Time Decay Factor on Reach for various mobility speeds Affect of Distance Decay Factor on Reach for various mobility speeds Affect of NF and FF Threshold on Reach for various mobility speeds Evaluation of metrics vs. AODV (reactive), OLSR (proactive), GPSR with GLS

(position-based), and ORRP under various node velocities, densities, topology-sizes, transmission rates.

Evaluation of metrics vs. AODV and OLSR modified to support directional antennas.


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MORRP: Aggregate Goodput Results• Aggregate Network Goodput vs.

Traditional Routing Protocols MORRP achieves from 10-14X the

goodput of AODV, OLSR, and GPSR w/ GLS with an omni-directional antenna

Gains come from the move toward directional antennas (more efficient medium usage)

• Aggregate Network Goodput vs. AODV and OLSR modified with directional antennas MORRP achieves about 15-20%

increase in goodput vs. OLSR with multiple directional antennas

Gains come from using directionality more efficiently


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MORRP: Simulations Summary• MORRP achieves high reachability (93% in mid-sized, 1300x1300m2

and 87% in large-sized, 2000x2000 m2 topologies) with high mobility (30m/s).

• With sparser and larger networks, MORRP performs fairly poorly (83% reach) suggesting additional research into proper DRT tuning is required.

• In lightly loaded networks, MORRP end-to-end latency is double of OLSR and about 7x smaller than AODV and 40x less than GPSR w/ GLS

• MORRP scales well by minimizing control packets sent• MORRP yields over 10-14X the aggregate network throughput

compared to traditional routing protocols with one omnidirectional interface gains from using directional interfaces

• MORRP yields over 15-20% the aggregate network goodput compared to traditional routing protocols modified with 8 directional interfaces gains from using directionality constructively


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MORRP: Key Contributions• The Directional Routing Table

A replacement for traditional routing tables that routes based on probabilistic hints

Gives a basic building block for using directionality to overcome issues with high mobility in MANET and DTNs

• Using directionality in layer 3 to solve the issues caused by high mobility in MANETs

• MORRP achieves high reachability (87% - 93%) in high mobility (30m/s)• MORRP scales well by minimizing control packets sent• MORRP shows that high reach can be achieved in probabilistic routing

without the need to frequently disseminate node position information.• MORRP yields high aggregate network goodput with the gains coming not

only from utilizing directional antennas, but utilizing the concept of directionality itself.

• MORRP is scalable and routes successfully with more relaxed requirements (No need for coordinate space embedding)

Relevant Papers• B. Cheng, M. Yuksel, and S. Kalyanaraman, Using Directionality in Wireless Routing, Under Review in IEEE International Conference on Mobile Ad-hoc and

Sensor Systems (MASS) 2008.


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Wireless Nets: Key Concepts to Abstract• Directionality CAN be used to provide high reach, high

goodput, low latency routing in wireless mesh (ORRP) and highly mobile adhoc networks (MORRP)

• Primitives: Local directionality is enough to maintain forwarding along

a straight line Two sets of orthogonal lines intersect with a high

probability in a bounded region• Overlay Networks:

Can we take these concepts to scale unstructured, flat, overlay networks?


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Virtual Direction Routing Introduction• Structured vs. Unstructured Overlay Networks

Unstructured P2P systems make little or no requirement on how overlay topologies are established and are easy to build and robust to churn

• Typical Search Technique (Unstructured Networks) Flooding / Normalized Flooding

• High Reach• Low path stretch• Not scalable

Random Walk• Need high TTL for high reach• Long paths• Scalable, but hard to find rare objects

• Virtual Direction Routing Globally consistent sense of direction (west is always

west) Scalable interface to neighbor mapping Routing can be done similarly to ORRP

• Focus (for now) Small world approximations

Random Walk

Virtual DirectionRouting

Flooding

Normalized Flooding


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VDR: Neighbor to Virtual Interface Map

• Neighbors are either physical neighbors connected by interfaces or neighbors under a certain RTT latency away (logical neighbors)

• Neighbor to Virtual Interface Mapping Each neighbor ID is hashed to 160 bit IDs using SHA-1 (to standardize small or

large IDs) The virtual interface assigned to the neighbor is a function of its hashed ID

(Hashed ID % number of virtual interfaces)

1

10

26

30

15687

10

12

3

4

5 6

8 Virtual Interfaces30 % 8 = 6

15 % 8 = 710 % 8 = 2

26 % 8 = 268 % 8 = 4

68

15

26

30

10


Example: Neighbor IDs used Instead Of SHA-1 Hashes

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VDR: State Seeding and Route Request|10 – 1| = 9|26 – 1| = 25

|5 – 1| = 4|13 – 1| = 12

|14 – 1| = 13|22 – 1| = 21Ex: Seed Source: Node 1

State Seeding – State info forwarded in orthogonal directions, biasing packets toward IDs that are closer to SOURCE ID. Packets are forwarded in virtual straight lines.

100

12

3

4

5 6

7

1

67

513

2868

10

12

3

4

5 6

7

1026

30

15

48

130

12

3

4

5 6

7

38

10

6

|10 – 12| = 2|26 – 12| = 15

|5 – 12| = 7|13 – 12| = 1

|6 – 12| = 6|38 – 12| = 26

Ex: Route Request: Node 12RREQ Source: Node 1

Route Request – RREQ packets are forwarded in orthogonal directions, biasing packets towards REQUESTED ID

0

12

3

4

5 6

7

26

30

1568

48

1

1010

100

12

3

4

5 6

7

1

67

13

28

55

50

12

3

4

5 6

7

55

10

221414


10

136

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VDR: Simulation Parameters

26

5

38

68

48

3010

136

12

2

46

1

RREQ: Node 12

Rendezvous Node

VDR Route RequestVirtual View

Seed Path

RREQ Path

RREP Path

Flooding

Random Walk

VDR – Random NB Send (VDR-R)

Virtual DirectionRouting

Normalized Flooding

Random Walk Routing (RWR)

• Simulation of VDR vs. RWR, VDR-R VDR-R: VDR with random neighbor forwarding (no biasing) RWR: Data is seeded in 4 random walks and 4 walkers are sent

for search• PeerSim – 50,000 Nodes, Static + Dynamic Network

Reach Probability – High (98% w/ TTL of 100) Average Path Stretch – High (16) State and Load Spread – Not evenly distributed


34

VDR: Robustness Results

5% drop

15% drop

12% drop

• State Distribution Network-wide Average States maintained

relatively equal for VDR, VDR-R and RWR at 350-390

VDR States are not very evenly distributed, with some nodes having more state than others. This is due to the sending bias

• Robustness to Network Churn VDR drops only 5% compared to

VDR-R and RWR which drop 12-15% reach when going from 0% to 50% network churn

Even with a TTL of 50, VDR reaches a good amount of the network


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VDR: Key Contributions• Introduction of the concept of Virtual Directions to eliminate

need for structure (coordinate space, DHT structures) to scale flat, unstructured overlay networks

• A flat, highly scalable, and resilient to churn routing algorithm for overlay networks

• VDR provides high reach (98% even only for a TTL of 100 in a 50,000 node network)

• VDR drops only 2-5% going from 0% churn to 50% churn

Relevant Papers• B. Cheng, M. Yuksel, and S. Kalyanaraman, Virtual Direction Routing for Overlay Networks, In preparation for submission to IEEE Peer to Peer

Computing (P2P) 2008.


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Conclusion / Future Work• Used Directionality to scale wireless networks (ORRP,

MORRP)• Used concept of Virtual Directions to scale overlay networks

(VDR)• Future Work: Extensions

Virtual direction abstraction analysis Hybrid ORRP (that works with omnidirectional and directional

antennas) Analysis of Effect of knobs in MORRP

• New Directions with Directionality Multi-path / multi-interface diversity Directional Network Coding Destination-based routing based on local directions


37

Thank You!• Questions and Comments?• Papers / Posters / Slides / Code [ http://networks.ecse.rpi.edu/~bownan ]• [email protected]


http://networks.ecse.rpi.edu/~bownan

1 using directionality in wireless routing bow-nan cheng advisors: dr. shivkumar kalyanaraman dr....

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