Challenges in Geographic Routing: Sparse Networks, Obstacles ...

Challenges in Geographic Routing: Sparse Networks, Obstacles, and Traffic Provisioning Brad Karp

Berkeley, CA [email protected]

DIMACS Pervasive Networking Workshop 21 May, 2001

Motivating Examples Vast wireless network of mobile temperature sensors, floating on the ocean’s surface: Sensor Networks Metropolitan-area network comprised of customer-owned and -operated radios: Rooftop Networks

Challenges in Geographic Routing

Brad Karp

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1

Scalability through Geography How should we build networks with a mix of these characteristics?

  

Mobility Scale (number of nodes) Lack of static hierarchical structure

Use geography in system design to achieve scalability. Examples:

  

Greedy Perimeter Stateless Routing (GPSR): scalable geographic routing for mobile networks [Karp and Kung, 2000] GRID Location Service (GLS): a scalable location database for mobile networks [Li et al., 2000] Geography-Informed Energy Conservation [Xu et al., 2001]

Challenges in Geographic Routing

Brad Karp

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2

Outline Motivation GPSR Overview GPSR’s Performance on Sparse Networks: Simulation Results Planar Graphs and Radio Obstacles: Challenge and Approaches Geographic Traffic Provisioning and Engineering Conclusions

Challenges in Geographic Routing

Brad Karp

[email protected]

3

GPSR: Greedy Forwarding Nodes learn immediate neighbors’ positions through beacons/piggybacking on data packets: only state required! Locally optimal, greedy forwarding choice at a node: Forward to the neighbor geographically closest to the destination

D

x y

Challenges in Geographic Routing

Brad Karp

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Greedy Forwarding Failure: Voids When the intersection of a node’s circular radio range and the circle about the destination on which the node sits is empty of nodes, greedy forwarding is impossible Such a region is a void:

D v

z void

w

y x

Challenges in Geographic Routing

Brad Karp

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Node Density and Voids Existing and Found Paths, 1340 m x 1340 m Region 1.1 1

Fraction of paths

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

Fraction existing paths Fraction paths found by greedy

0.1 0

50

100

150 200 Number of nodes

250

300

The probability that a void region occurs along a route increases as nodes become more sparse Challenges in Geographic Routing

Brad Karp

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6

GPSR: Perimeter Mode for Void Traversal

D

x Traverse face closer to D along xD by right-hand rule, until reaching the edge that crosses xD Repeat with the next closer face along xD, &c. Forward greedily where possible, in perimeter mode where not Challenges in Geographic Routing

Brad Karp

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Challenge: Sparse Networks Greedy forwarding approximates shortest paths closely on dense networks Perimeter-mode forwarding detours around planar faces; not shortest-path Greedy forwarding clearly robust against packet looping under mobility Perimeter-mode forwarding less robust against packet looping on mobile networks; faces change dynamically Perimeter mode really a recovery technique for greedy forwarding failure; greedy forwarding has more desirable properties How does GPSR perform on sparser networks, where perimeter mode is used most often?

Challenges in Geographic Routing

Brad Karp

[email protected]

8

Simulation Environment ns-2 with wireless extensions [Broch et al., 1998]: full 802.11 MAC, physical propagation; allows comparison of results Topologies and Workloads: Nodes

Region

Density

CBR Flows

50

1500 m  300 m

1 node / 9000 m2

30

1 node / 9000 m2

30

1 node / 35912 m2

30

200 50

3000 m  600 m

1340 m  1340 m

Simulation Parameters: Pause Time: 0, 30, 60, 120 s

Motion Rate: [1, 20] m/s

GPSR Beacon Interval: 1.5 s

Data Packet Size: 64 bytes

CBR Flow Rate: 2 Kbps

Simulation Length: 900 s

Challenges in Geographic Routing

Brad Karp

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Packet Delivery Success Rate (50, 200; Dense) 1

Fraction data pkts delivered

0.9 0.8 0.7 0.6 0.5 0.4 DSR (200 nodes) GPSR (200 nodes), B = 1.5 DSR (50 nodes) GPSR (50 nodes), B = 1.5

0.3 0.2 0

Challenges in Geographic Routing

20

40

Brad Karp

60 Pause time (s)

80

100

120

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Packet Delivery Success Rate (50; Sparse) 1

Fraction data pkts delivered

0.95

0.9

0.85

0.8

0.75 DSR (50 nodes) GPSR (50 nodes), B = 1.5 GPSR (50 nodes), B = 1.5, Greedy Only

0.7 0

Challenges in Geographic Routing

20

40

Brad Karp

60 Pause time (s)

80

100

120

[email protected]

11

Path Length (50; Dense) DSR

120

DSR

60 GPSR

DSR

30

Pause Time

Routing Algorithm

GPSR

GPSR

DSR

0 GPSR

0.0

0.2

0.4

0.6

0.8

1.0

Fraction of delivered pkts 0

1

2

3

4

>= 5

Hops Longer than Optimal

Challenges in Geographic Routing

Brad Karp

[email protected]

12

Path Length (50 nodes, Sparse) DSR

120

DSR

60 GPSR

DSR

30

Pause Time

Routing Algorithm

GPSR

GPSR

DSR

0 GPSR

0.0

0.2

0.4

0.6

0.8

1.0

Fraction of delivered pkts 0

1

2

3

4

>= 5

Hops Longer than Optimal

Challenges in Geographic Routing

Brad Karp

[email protected]

13

Outline Motivation GPSR Overview GPSR’s Performance on Sparse Networks: Simulation Results Planar Graphs and Radio Obstacles: Challenge and Approaches Geographic Traffic Provisioning and Engineering Conclusions

Challenges in Geographic Routing

Brad Karp

[email protected]

14

Network Graph Planarization Relative Neighborhood Graph (RNG) [Toussaint, ’80] and Gabriel Graph (GG) [Gabriel, ’69] are long-known planar graphs Assume an edge exists between any pair of nodes separated by less than a threshold distance (i.e., the nominal radio range) RNG and GG can be constructed using only neighbors’ positions, and both contain the Euclidean MST! w w u

v

RNG Challenges in Geographic Routing

u

v

GG Brad Karp

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Planarized Graphs: Example 200 nodes, placed uniformly at random on a 2000-by-2000-meter region; radio range 250 meters

Full Network

Challenges in Geographic Routing

GG Subgraph

Brad Karp

RNG Subgraph

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Challenge: Radio-Opaque Obstacles and Planarization Obstacles violate assumption that neighbors determined purely by distance:

Full Network

GG and RNG Subgraph

In presence of obstacles, planarization can disconnect destinations! Challenges in Geographic Routing

Brad Karp

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17

Coping with Obstacles Eliminate edges only in presence of mutual witnesses; edge endpoints must agree

Full Graph

Mutual GG

Mutual RNG

Prevents disconnection, but doesn’t planarize completely Forward through a randomly chosen partner node (location) Compensate for variable path loss with variable transmit power Challenges in Geographic Routing

Brad Karp

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18

Traffic Concentration Demands Provisioning 1 0 00 0 11 1 00 11 0 11 1 00

1 0 00 0 11 1 00 11 0 11 1 00

1 0 00 0 11 1 00 11 0 11 1 00

1 0 0 1 0 1

1 0 0 1 0 1

1 11 0 00 0 11 1 00

1 11 0 00 0 11 1 00

1 11 0 00 0 11 1 00

1 0 0 1

1 0 0 1

1 11 0 00 0 11 1 00

1 11 0 00 0 11 1 00

1 11 0 00 0 11 1 00

1 0 0 1

1 0 0 1

1 0 00 11 0 11 1 00

1 0 00 11 0 11 1 00

1 0 00 11 0 11 1 00

1 0 0 1

1 0 0 1

1 0 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1

1 0 0 1 0 1 0 1 0 1

1 0 0 1 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1 0 1

1 0 0 1 0 1 0 1 0 1 0 1

If we assume uniform traffic distribution, flows tend to cross the center of the network All link capacities symmetric!

Challenges in Geographic Routing

Brad Karp

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Geographic Network Provisioning 1 0 00 0 11 1 00 11 0 11 1 00

1 0 00 0 11 1 00 11 0 11 1 00

1 0 00 0 11 1 00 11 0 11 1 00

1 0 0 1 0 1

1 0 0 1 0 1

1 11 0 00 0 11 1 00

1 11 0 00 0 11 1 00

1 11 0 00 0 11 1 00

1 0 0 1

1 0 0 1

1 11 0 00 0 11 1 00

1 11 0 00 0 11 1 00

1 11 0 00 0 11 1 00

1 0 0 1

1 0 0 1

1 0 00 11 0 11 1 00

1 0 00 11 0 11 1 00

1 0 00 11 0 11 1 00

1 0 0 1

1 0 0 1

1 0 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1

1 0 0 1 0 1 0 1 0 1

1 0 0 1 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1 0 1

11 00 00 11 00 11 00 11 00 11 00 11

1 0 0 1 0 1 0 1 0 1 0 1

1 0 0 1 0 1 0 1 0 1 0 1

In a dense wireless network, position is correlated with capacity Symmetric link capacity and dense connectivity Route congested flows’ packets through a randomly chosen point Challenges in Geographic Routing

Brad Karp

[email protected]

20

Conclusions On sparse networks, GPSR delivers packets robustly, most of which take paths of near-shortest length Non-uniform radio ranges complicate planarization; variable-power radios and random-partner proxying may help Geographically routed wireless networks support a new, geographic family of traffic engineering strategies, that leverage spatial reuse to alleviate congestion Use of geographic information offers diverse scaling benefits in pervasive network systems

Challenges in Geographic Routing

Brad Karp

[email protected]

21