Towards a New Routing Framework in MANETs

Task 3: Theory of Scalable and

Robust Protocols Marcelo Carvalho, Hari Rangarajan,

Marco Spohn, Ravindra Vaishampayan, Rolando Menchaca, Zhenjiang Li

J.J. Garcia-Luna-AcevesUniversity of California, Santa Cruz

jj@soe.ucsc.edu

http://www.cse.ucsc.edu/research/ccrg/home.htmlhttp://www.cse.ucsc.edu/research/ccrg/home.html

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Outline Summary of results obtained

over the past year Analytical models, routing,

multicastingRecent results on ordering in

distributed algorithmsPlan for the next year.

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Summary of Results: Analytical Models of MAC Protocols

First analytical model of IEEE 802.11 DCF that considers directional antennas or space time block codes (STBC) operating in a multihop MANET taking into account the characteristics of PHY layer in detail.

Effective SINR of the Alamouti scheme under multiple access interference (MAI).

A new Markov model for the operation of the IEEE 802.11DCF that includes: (a) the impact of the carrier-sensing activity, (b) the finite-retry limit of frame retransmissions, and (c) the impact of errors in both control and data frames within a four-way handshake.

1. M. Carvalho, "Analytical Modeling of Medium Access Control Protocols in Wireless Networks," PhD Thesis, Computer Engineering, University of California, Santa Cruz, CA 95064, March 2006.

2. M. Carvalho and J.J. Garcia-Luna-Aceves, ``Modeling Wireless Ad Hoc Networks with Directional Antennas,'' Proc. IEEE Infocom 2006, Barcelona, Spain, 23--29 April, 2006.

3. M. Carvalho and J.J. Garcia-Luna-Aceves, ``Analytical Modeling of Ad Hoc Networks that Utilize Space-Time Coding,'' Proc. IEEE WiOpt 2006: 4th Intl. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, Boston, Massachusetts, April 3--7, 2006.

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Summary of Results: Distributed QoS Routing in MANETs

First distributed algorithms for QoS routing with multiple constraints that only require local information to operate.

MPOR (multi-constrained path optimization routing) algorithm supports (a) multi-constrained path selection (finding feasible paths satisfying constraints) and (b) multi-constrained path optimization (obtaining feasible paths that are optimal w.r.t. optimization metric.

Key ideas: Define “logical distance” computed by an optimization function that is

monotone and isotone. Compute a k-optimal path set (the first k shortest paths w.r.t. logical

distance) for each destination using ordering invariants.

1. Z. Li and J.J. Garcia-Luna-Aceves, ``Finding Multi-Constrained Feasible Paths by Using Depth-First Search,'' accepted for publication in ACM WINET Journal, 2005.

2. Z. Li and J.J. Garcia-Luna-Aceves, ''A Distributed Approach for Multi-Constrained Path Selection and Routing Optimization'', Proc. QShine 06: Third International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks, Waterloo, Ontario, Canada, August 7-9, 2006.

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Summary of Results: Multicast Routing in MANETs

Robustness through learning and and cross-layer designs for mesh-based multicast routing in MANETs

Incorporation of directional antennas in mesh-bsed multicasting.

1. Ravindra Vaishampayan, "Efficient and Robust Multicast Routing in Mobile Ad Hoc Networks," PhD Thesis, Computer Science, University of California, Santa Cruz, CA 95064, March 2006.

2. R. Vaishampayan and J.J. Garcia-Luna-Aceves, ``An Adaptive Redundancy Protocol for Mesh Based Multicasting,'' accepted for publication in Computer Communications Journal, special issue on Advances in Computer Communication Networks, 2006.

3. R. Vaishampayan and J.J. Garcia-Luna-Aceves, ``Cross Layer Ad hoc Multiple Channel Multicasting Protocol,'' Proc. IEEE MASS 2006, Vancouver, Canada, October 9--12, 2006.

4. R. Menchaca-Mendez, R. Menchaca-Mendez and J.J. Garcia-Luna-Aceves, "ADMP: An Adaptive Multicast Routing Protocol for Mobile Ad Hoc Networks," Proc. 19th IFIP World Computer Congress, Santiago, Chile,August 20--25, 2006.

5. R. Vaishampayan and J.J. Garcia-Luna-Aceves, ``Efficient Multicasting in Multi-Hop Ad Hoc Networks Using Directional Antennas,'' Proc. IEEE MASS 2005: 2nd IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, 7--10 November 2005, Washington, D.C.

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Summary of Results: Dominating Sets in MANETs

The first distributed algorithm that solves the (k, r) domination problem in arbitrary graphs and MANETs using only local information:

Each node is covered by k dominating nodes that are at most r hops away.

Applicable to many problems in protocol design for multicasting, broadcasting and topology control.

1. Marco A. Spohn, "Domination in Graphs in the Context of Mobile Ad Hoc Networks," PhD Thesis, Computer Science, University of California, Santa Cruz, CA 95064, 2005

2. M.A. Spohn and J.J. Garcia-Luna-Aceves, ``Bounded-Distance Multi-Clusterhead Formation in Wireless Ad Hoc Networks,'' Ad Hoc Networks Journal, accepted for publication, 2006.

3. M.A. Spohn and J.J. Garcia-Luna-Aceves, ``Multicasting in Ad Hoc Networks in the Context of Multiple Channels and Multiple Interfaces,'' Proc. International Workshop on Localized Communication and Topology Protocols for Ad hoc Networks (LOCAN 2005) 7 November 2005, Washington, D.C.

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Summary of Results: Secure and Robust Routing in MANETs

The first secure routing algorithm for MANETs based on self-certifying keys.

Enables secure routing in disrupted networks; no need for connectivity to a certifying authority once the network is deployed.

1. Z. Li and J.J. Garcia-Luna-Aceves, ``Non-Interactive Key Establishment in Mobile Ad Hoc Networks,'' accepted for publication in Ad Hoc Networks, 2006.

2. Z. Li and J.J. Garcia-Luna-Aceves, `` New Non-Interactive Key Agreement and Progression (NIKAP) Protocols and Their Applications to Security in Ad Hoc Networks,'' Proc. International workshop on Wireless and Sensor Networks Security (WSNS'05), 7 November 2005, Washington, D.C.

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Summary of Results: Distributed Ordering in MANETs

The first on-demand routing algorithm for MANETs based only on source-based sequence numbers.

Much better performance and much simpler than AODV, DSR, and variations on destination-based sequence numbers or path caching.

Proof that routing framework based on distributed ordering sequences is feasible.

1. Hari Rangarajan, "Robust Loop-free On-demand Routing in Ad hoc Networks," PhD Thesis, Computer Engineering, University of California, Santa Cruz, CA 95064, June 2006.

2. H. Rangarajan and J.J. Garcia-Luna-Aceves, ``Efficient Use of Route Requests for Loop-free On-demand Routing in Ad hoc Networks,'’ accepted for publication in Computer Networks, Elsevier, 2006.

3. H. Rangarajan and J.J. Garcia-Luna-Aceves, ``On-demand Loop-Free Routing in Ad hoc Networks Using Source Sequence Numbers,'' Proc. IEEE MASS 2005: 2nd IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, 7--10 November 2005, Washington, D.C. Best Paper AwardBest Paper Award

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Distributed Ordering Using Source Sequence Numbers

Motivation: All on-demand routing protocols require unique identifiers for RREQs,

all dissemination protocols require unique IDs for packets being disseminated.

All on-demand routing schemes to date have used additional mechanisms to ensure loop freedom (e.g., AODV uses destination sequence-numbers, DSR uses source-routes).

We have to identify RREQs and disseminated packets! Can we realize routing protocols and dissemination

protocols that maintain ordering solely on the basis of the sequence numbers already used to identify RREQs or similar messages?

Does the new approach attain the desired simplicity with the same or better performance than previous schemes?

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Distributed Ordering Using Source Sequence Numbers

Approach: Use the source sequence labels (SSL) needed in RREQs to build

destination-based directed acyclic graphs (DAG). Since multiple DAGs can be created, each node remembers the DAG in

which it participates, and its neighbors inform it of the DAG in which they collaborated.

Ensure that no node can “jump back” to a prior DAG, which can create loops. A node can change its relative order by changing DAGs

Nodes use an SSL and a reported sequence label (RSL) to uniquely identify a DAG in the presence of topology changes.

RSL is used to avoid joining the wrong DAGs SSL is used together with RSL for ordering within a DAG to enable local

repairs Enable local repairs based on the ordering of nodes within a DAG.

Many strategies are possible. We have only explored the simplest ones.

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Loop-freedom when Destination Replies (DLSR)

A

B

C

D

SSL (A,1)RSL (B,1)

XSSL (A,1)RSL (C,1)

SSL (C,2) RSL (F,2)

F

SSL (C,2)RSL (A,2)

SSL (C,2)RSL (G,1)

G

SSL (C,2)RSL (C,2)

Valid DAG at Node A: SSL (A,1)RSL (A,1)

1

SSL (C,2)RSL (A,2)

2

RREQ 2

RREQ 1

RREP 1

RREP 2

SSL (A,1)RSL (A,1)

A

B

C

D

SSL (A,1)RSL (B,1)

X

SSL (C,2) RSL (F,2)

F

SSL (C,2)RSL (A,2)

SSL (C,2)RSL (G,1)

G

SSL (C,2)RSL (C,2)

SSL (A,1)RSL (A,1)

Node A cannot accept RREP from B,Because it belongs to an invalid DAG

Node A can only accept RREPs in DAG 2, or become part of a new DAG.

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Intermediate node replies in AODV

S

A

X Y

B C

Z

D

SN: 1SN: 1SN: 1

SN: 1 SN: 1 SN: 1

SN: 1

Only a node with SN > 1 can answer source S’s RREQ.All RREQs will have to be answered by the destination.

SN: 2

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Distances as Labels for Local Repairs (LSR-D)

A

B

C

D

SSL (A,1)RSL (A,1)

SSL (A,1)RSL (B,1)]

SSL (A,1)RSL (C,1)]

SSDL: [(A,1),1)

SSDL: [(A,1),2)

SSDL: [(A,1), inf )

[(A,1), 1)] is fresher than [(A,1), 2) is fresher than [(A,1), inf]

SSL (A,2)RSL (P,1)

A

B

C

D

SSL (A,2)RSL (A,2)

P

Q

SSL (A,2)RSL (Q,1)

A

B

C

D

P

Q

SSDL: [(A,1), inf)

SSDL: [(A,1),2)

SSDL: [(A,1),1)SSDL: [(A,2),1)

SSDL: [(A,2),2)

[(A,2), 1)] is fresher than [(A,2), 2) is fresher than [(A,1), inf]

We can use source-sequence numbers instead of destination-sequence numbers to identify fresh We can use source-sequence numbers instead of destination-sequence numbers to identify fresh distancesdistances

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* Standard test scenarios

Performance Summary

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Packet Delivery Ratio

SSL-based Protocols

AODV, DSR, OLSR

30-random flows, 100 nodes

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Control Overhead

SSL-based protocols, OLSR

DSR: Drops data packets

AODV

30-random flows, 100 nodes

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Delivery Latency30-random flows, 100 nodes

SSL-based protocols

AODV

DSR,OLSR

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Implications We have shown that distributed ordering of nodes in

dynamic networks is possible using SSLs (same information needed to disseminate packets w/o replications).

Performance results show that this new way or ordering nodes renders protocols that are simpler (in logic) and out-perform the current state-of-the-art MANET routing protocols in terms of packet delivery, delivery latency, and control overhead.

Protocols for any type of routing and dissemination (proactive and on-demand unicast, multicast, dissemination, publish-subscribe, etc.) can be cast as a problem of distributed ordering in graphs.

So, we have the start of a new framework for routing in dynamic networks.

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Develop a unified framework for routing in MANETs centered around “distributed ordering of sequences”

Integrate routing and scheduling using the notion of ordering in a neighborhood and ordering in the network.

Limit signaling overhead incurred in informing nodes about interest in certain destinations or the presence of such destinations while nodes move.

Next Steps

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Distributed Ordering in Routing

R

R

i

p

k

m

j

u

b

h

c

a

v

e

g

f

dRREQ,Update,Subscription,InterestNodes are ordered for each destination, which can be a

node, a service, content or a role. Ordering is maintained as nodes move around carrying

content. Ordering of nodes forms a directed acyclic graph (DAG) independently of any routing metric used

MAX

(0)

(1)

(1)

(2)

(2)

(2)

(3)

(3)

(3)

(3)(4)

(4)

(4)

(5)

MIN

(5)

ordering sequence

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Distributed Ordering in Routing

R

R

i

p

k

m

j

u

b

h

c

a

v

e

g

f

dRREQ,Update,Subscription,Interest

MAX

(0)

(1)

(1)

(2)

(2)

(2)

(3)

(3)

(3)

(3)(4)

(4)

(4)

(5)

MIN

(5)

ordering sequence

Load balancing and constraints (e.g., end-to-end delay and jitter) used for forwarding over DAG.Constraints can be made part of the ordering

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Different than Virtual Circuits!

R

R

i

p

k

m

j

x

b

h

c

a

y

e

g

f

d

Nodes b, h, and x can reply to the request (for route, content, service) from p, rather than just d (destination,

origin of content or service).Each node has multiple paths to reach destination while

satisfying the given constraints.

(3)

(3)

(3)

(3) (4)

(4)

(4)

(5)

(5)

source

(2)

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Proactive Routing:

DD

aa SSee

ccff

hh

bb

Too many nodes are forced to know about how to Too many nodes are forced to know about how to reach each destination! Does not work well with random partitionsreach each destination! Does not work well with random partitions

Path first, then data forwarding

DD

Information Information about D about D propagates propagates away from D in away from D in a circle of a circle of radius rradius r

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On-Demand Routing:

DD

aa SSee

ccff

hh

bb

Too many nodes are forced to help find or repair ways to reach a few Too many nodes are forced to help find or repair ways to reach a few destinations! (RREQ flooding). Does not work with partitioned networks!destinations! (RREQ flooding). Does not work with partitioned networks!

Nodes with paths to D reply to S.

Path first, then data forwarding

SS

Too few nodes keep state for D.So too many nodes try to fix broken paths

Information Information from S from S propagates propagates away from S away from S in a circle of in a circle of radius rradius r

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Epidemic Routing

DD

aa SSee

ccff

hh

bb

Too many nodes are forced to relay data from S to D.Too many nodes are forced to relay data from S to D.Does not work with partitioned networks, unless infinite storage is Does not work with partitioned networks, unless infinite storage is assumed.assumed.

Data create paths

SS

Information Information from S from S propagates propagates away from S away from S in a circle of in a circle of radius rradius r

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Goal

DD

aa SSee

ccff

hh

bb

Limit the number of nodes that incur signaling and Limit the number of nodes that incur signaling and forwarding overhead between S and Dforwarding overhead between S and D

Region of interest is a function of the source and destination

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Goal

DD

aa SSee

ccff

hh

bb

Limit the number of nodes that incur signaling and Limit the number of nodes that incur signaling and forwarding overhead between S and Dforwarding overhead between S and D

DD

SS

Conjecture:Use elipitic curves defined by the distances to source and destination

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Goals

SS

hh

DD

ee

ff

Enable Correct Signaling and Forwarding in Partitioned Enable Correct Signaling and Forwarding in Partitioned Networks. Preserve efficiency in each network componentNetworks. Preserve efficiency in each network component

J.J. Garcia-Luna-Aceves (PI) Hamid SadjadpourKatia ObraczkaMuriel Medard Andrea GoldsmithPravin VaraiyaRajive BagrodiaMario GerlaJennifer HouNitin VaidyaTony Ephremides

UCSC:

MIT:Stanford University:

UC Berkeley:UCLA:

UIUC:

University of Maryland:

http://www.soe.ucsc.edu/research/ccrg/DAWN

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Summary of Scientific Progress

19 journal papers published or accepted for publication. ACM WINET, ACM/IEEE Trans. Networking, IEEE Trans.

Comm., IEEE Trans. Wireless Comm., ACM Trans. Sensor Networks, Computer Communications, Ad Hoc Networks

35 peer-reviewed papers in conference proceedings ACM Mobicom, ACM Mobihoc, ACM SIGCOMM, IEEE Infocom,

IEEE WiOpt, IEEE MASS, IEEE Qshine, IEEE ICC, IEEE IPSN One Best Paper Award (IEEE MASS 2005) 8 Invited papers 12 manuscripts 13 Ph.D. theses completed, at least 2 graduates with

faculty positions Active and growing intercampus collaboration (e.g.,

UCLA-MIT, UCLA-UCSC, Stanford-MIT)

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Summary of Scientific Progress (2) More accurate treatments of the effects of the physical layer in the

protocol stack First analytical models that accurately reflect the impact of node

mobility in links and paths New approaches on how MAI should be treated New modeling tools for the characterization of energy consumption

in MANETs New performance trade-offs involving node complexity, node

mobility, packet length, channel utilization, and delay. First results on network coding applied to unicasting and multicasting

in MANETs The first algorithm for on-demand routing that operates solely with

source-originated sequence numbers New approaches to information dissemination New optimization techniques for large-scale simulations