Smart Networks Project

University of California, Berkeley

DARPA NMS PI MeetingMiami, Jan 21-23, 2004

Group Members

Faculty Jean Walrand Pravin Varaiya Venkat

Anantharam David TseIndustry David Jaffe (Cisco)Staff Bill Hodge

Students Eric Chi Antonios Dimakis Rajarshi Gupta Linhai He Zhanfeng Jia John Musacchio Wilson So Teresa Tung

Outline

Modeling interference in MANET Using models for QoS strategies

Clustering Admission Control Routing

New multi-channel MAC Conclusions and Future Work

Why Interference is critical

In wired networks, all links may be used simultaneously

In MANET, neighboring links interfere

Interference Range (Ix) > Transmission Range (Tx)

InterferenceRange

TransmissionRange

Node A

Node D

Node C

Node B

Link 2

Link 1

Outline

Modeling interference in MANET Using models for QoS strategies

Clustering Admission Control Routing

New multi-channel MAC Conclusions and Future Work

Interference may be modeled as a Conflict Graph

‘Cliques’ in a Conflict Graph Clique = Complete

Subgraph Only one vertex in a clique

may be active at once Capacity closely related to

cliques

Conflict Graph and Cliques

A

B C

E F

DLink

Link

Links A and Cinterfere

Cliques:

ABC, BCEF, CDF

Theoretical Result Unfortunately, capacity

constraints based on cliques are not sufficient

Graph theory result: Flows that satisfy scaled clique constraints have a realizable schedule

Scaling factor:

Clique constraints suggest a rate of 0.5 per link

But only 0.4 per link is achievable

A

B C

E D

ConflictGraph

Assume all linkcapacity = 1

“Graph Imperfection I”, S. Gerke and C. McDiarmid, Journal of Combinatorial Theory, Series B, vol. 83 (2001), pp. 58-78.

46.0

321

1

Complete Distributed Mechanism

Local link state exchange: position, flow Distributedly compute all cliques

Recompute upon topology change

Requested flow (rate + path) checked by all nodes in neighborhood of path Check allocated and requested flows against

clique constraints scaled by 0.46 Admit flows if satisfied

OPNET Simulation Model

Received vs Sent Rates

-- 3 Flows

-- 4 Flows

-- 5 Flows

Clique Predicted Limit – 3 Flows

Clique Predicted Limit – 4 Flows

Clique Predicted Limit – 5 Flows

All flows have the same sending rate

X-axis: average rate of sent traffic

Y-axis: average rate of received traffic

Vertical lines show theoretical capacity limits predicted by clique constraints

Outline

Modeling interference in MANET Using models for QoS strategies

Clustering Admission Control Routing

New multi-channel MAC Conclusions and Future Work

Routing using Clustering with Interference Considerations

Routing without clustering does not seem to scale

Consider the effects of interference in clustering

Minimize cross cluster interference Decompose intracluster route computation

Clustering

RoutingSource

Dest

Decomposition of Clique Constraints

Intracluster routing strategies OSPF Integer Linear Programming

Decomposing to per-cluster computation Decomposed clique constraints by cluster Comparison against network-wide clique

constraints Simulations show that decomposed constraints

result in reasonable network performance

See poster for detailed results

Outline

Modeling interference in MANET Using models for QoS strategies

Clustering Admission Control Routing

New multi-channel MAC Conclusions and Future Work

New Multi-Channel MAC For

Infrastructure and ad hoc wireless networks with many channels and a high node density

Propose A new protocol to increase network throughput by

allowing parallel packet transfers on different channels

Key Distinction Parallel contention on all channels Per-packet dynamic channel selection

Initial Simulation Results Seems stable under high load in various simulations Reasonable delay statistics

Conclusions and Future Work

Conclusions Modeled interference constraints in MANET Routing and Clustering considering interference Simulations validate theoretical models Novel multi-channel MAC to augment throughput

Future Work Distributed QoS routing algorithm for a general

MANET Measurements to refine clique constraints Incorporate timing and mobility considerations Handle multiple channels and classes of service