Autonomic and Collaborative Protocols in Wireless Delay Tolerant Networks

Stavros Toumpis Department of Informatics

Athens University of Economics and Business Athens, Greece

CROWN KICKOFF, 11/5/121

PART A: Delay Tolerant Networks

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Definition• Delay in the delivery of packets is very large

(specifically, comparable to the time needed for the topology to change substantially)

• Two cases1. Very large delays are necessarily large (e.g.,

interplanetary networks [Burleigh et al. 2003]) 2. Very large delays are a design choice (e.g., Zebranet,

Juang et al. 2002). Delay is conscientiously traded off.

• In the context of wireless networks, large delays typically translate to communication by physical transportation of data (either partially or exclusively)

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Applications

• Interplanetary networks• Sensor networks (Zebranet)• The Internet • Vehicular Networks, for certain kinds of traffic

(GeoDTN+Nav)

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Recent History• Infostations [Goodman et al. ‘97]• Epidemic Routing [Vahdat/Becker ‘00]• Mobility Increases the Capacity of Wireless

networks [Grossglauser/Tse ‘01, Toumpis/Goldsmith ‘04]

• Data Mules [Shah et al. ‘03]• Zebranet [Juang et al. 02, Small/Hass ‘03]• Delay Tolerant Architecture [Jain et al. ‘03]• Spray and Wait [Spyropoulos et al. ‘05]• MaxProp [Burgess et al. ‘06] 5

Earlier History• Much work in Operations Research in the

context of dynamic flows and networks (which are functions of time)

• Ford/Fulkerson constructed maximal flows in ‘54 and maximal dynamic flows in ’58!

• Ogier studied minimum delay routing and related problems in the ‘80s.

• Ferreira et al. [Ferreira 04,10] and Merugu et al. [Merugu et al. ‘04] applied dynamic flows in the context of (wireless) DTNs

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Classification of DTN Analysis

• Do we know the topology evolution of the network?– If YES, then we can study it using tools from

network optimization theory, notably dynamic flows and networks

– If NO, then we can use tools from probability and related fields (e.g., stochastic control)

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Why are DTNs interesting in the context of this project?

• If Wireless Networks added a spatial component to the analysis of networks…

• … then Delay Tolerant Networks add a time component…

• …and room for innovation is still there…• … particularly in the areas covered by this

project.

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Part B: Autonomous and Collaborative Protocols in DTNs

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Organization of the project

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WP1: Understanding and influencing uncoordinated interactions of autonomic wireless networks

WP2: Optimization through network coordination

WP3: Autonomic and collaborative protocols in Wireless DTNs

Task 3.1: Autonomic operation of wireless DTNsTask 3.2: Coordinated operation of wireless DTNsTask 3.3 Realistic wireless DTN protocol design

Task 3.1: Autonomic operation of wireless DTNs

• Main tool: probability theory• DTNs are frequently partitioned, therefore autonomic

operation based on local decisions is appealing/necessary– Nodes decide on next hop of packet– Nodes decide on what packet to transmit/delete– etc.

• Resources (bandwidth, buffer spaces) are typically constrained, so selfish behavior is expected– Nodes would like to only transmit/store their own packets, etc.

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Sub-Task 3.1.1: Geographic Routing

• In geographic routing, next hop for a packet is decided according to location of destination and topology near the current packet holder.

• Problem: find optimal behavior for current holder, based on local knowledge.

• An obvious tradeoff exists between transportation cost and packet delivery delay.

• Tool for performing the analysis: Stochastic Geometry

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Subtask 3.1.2: Delay-Throughput tradeoff of DTNs

• Fundamental tradeoff: The more copies of a packet are transmitted, the faster it will arrive at its destination, but the smaller the throughput becomes.

• Goal: evaluate the performance of network coding particularly in terms of this tradeoff– What is the optimal tradeoff– How well do practical protocols achieve it?

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Task 3.2: Coordinated operation of wireless DTNs

• Main tool: network optimization theory and dynamic flows.

• Main goal: study the tradeoff of delay with other metrics in a systematic manner.

• Common approach:– First, find optimum tradeoffs– Then, find good heuristics and compare them with

optimum tradeoffs

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Current status of Task 3.2

• Delay versus cost tradeoff – i.e., if we wait more, we will transport data with

smaller cost [Tasiopoulos et al. 12]

• Delay versus data volume tradeoff – i.e., if we wait more, we will transport more data

[Gitzenis et al. ‘12]

• Delay versus storage capacity tradeoff– i.e., if we wait more, we need less storage

capacity [Iosifidis/Koutsopoulos ‘11]15

Task 3.3 Realistic wireless DTN protocol design

• Main tool: simulations• Aim: develop simulator for simulating the operation

of DTNs in the 10,000 node regime• Currently available tools:

– Generic, i.e., NS2, OMNET, etc. These are not good fits for DTN research, because large delays mean large buffers.

– Specialized (for example, ONE), but slow

• Our approach: fast, dedicated simulator written in C.

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Current status of Task 3.3• Features of our partially built simulator:

– Can handle 10,000+ nodes– Uses realistic channel model and includes a realistic

slotted MAC protocol– Can handle a variety of mobility models– We have evaluated a variety of well known DTN protocols

such as Spray and Wait, GeoDTN+Nav, Geocross, etc. – We have created our own protocol, DTFR (more later)

[Sidera ‘11]

• Main aim: use simulator to test ideas and protocols coming from other workpackages and tasks.

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Part C: Flow Optimization in Delay Tolerant Networks using Dual

Decomposition

Savvas Gitzenis (Informatics and Telematics Institute, CERTH, Greece)

George Konidaris, Stavros Toumpis (Informatics Department, AUEB, Greece)

(RAWNET 2012, 18/5/12, Paderborn18

Our work• Mobile Wireless DTNs

– Topology changes due to node mobility

• Objective: Single commodity flow optimization• Challenge: Flow Optimization is a hard problem

in wireless networks even in non-DTN setting• Contributions:

1.Fast non-causal centralized algorithm (taking into account the structure of the problem)

2.Heuristic causal centralized algorithms(Heuristic causal decentralized algorithms are subject of future work)

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Network Model (1/2)

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• N nodes 1,2,…,N with set of links A• T time epochs 1,2,…, T

– Topology (i.e., link properties) remains fixed during each epoch– (following Ferreira ‘02 and others)

• Traffic flow in epoch t is x(t)={xij(t), (i,j) in A}, t=1,2,…, T

– NB: x(t) describes volume of data, not data rate.

• x(t) must be inside capacity region R(t)• At transition from epoch t-1 to epoch t, node i must have

volume of data less than buffer size Bi(t), i=1,2,…, N, t=2,…, T

• Internal buffer size vectors B(t)={Bi(t), i=1,2,…N}

Network Model (2/2)

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• Let yi(t) be the data volume at node i at start of epoch t. Let y(t)={yi(t), i=1,2,…, N}

• Let zi(t) be the data volume at node i at end of epoch t. Let z(t)={zi(t), i=1,2,…, N}

• Let input cost function Ci(yi(1)), i=1,2,…, N

• Let utility function Ui(zi(T)), i=1,2,…, N

• Let external buffer size vectors B(1), B(T+1) such that

)1(,),1()()(

)1(,),1()1()1(

1

1

TBTBTT

BB

N

N

Bz

By

Capacity Region Evolving Graph

(CREG)

22

11

31

41

21

x13(1) x34(1)

x23(1) x32(1)

x12(1)

x31(1)

x42(1)

x24(1)

z1(1) z2(1) z3(1) z4(1)

RE

PL

ICA

1

C1(y1(1))

z(1)≤ B(2)

x(1) ϵ R(1)

y1(1) y2(1) y3(1) y4(1) y(1)≤ B(1)

12

32

42

22

x13(2) x34(2)

x23(2) x32(2)

x12(2)

x31(2)

x42(2)

x24(2)

z1(2) z2(2) z3(2) z4(2)

RE

PL

ICA

2

λ1(1) λ2(1) λ3(1) λ4(1)

z(2)≤ B(3)

x(2) ϵ R(2)

λ(1)ϵR

y1(2) y2(2) y3(2) y4(2) y(2)≤ B(2)

C2(y2(1)) C3(y3(1)) C4(y4(1))

U1(z1(2)) U2(z2(2)) U3(z3(2)) U4(z4(2))

s1 s2 s3 s41 1 11

s1 s2s3 s42 2 22

s1 s2 s3 s43 3 33

• Replica t ↔epoch t• Vertices it , t=1,…,T

correspond to node i for different replicas

• Storage vertices sti

will be used in dual decomposition

Problem DTN Utility Maximization (DTNUM)

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1,...,2,1 ,1

, ,0)()(

)()(

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),(subject to

1maximize

),(:

),(:

N

1i1

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t (t)(t)

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zy

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Rx

A key idea• Complexity of problem is dominated by the

capacity regions, which are often very hard to describe accurately (e.g., Johansson&Soldati, ’06)– Even simple flow maximization problems can be shown

to be NP-complete (e.g., Ephremides&Truong ’90).

• Therefore, lumping multiple capacity regions in same problem is a bad idea.

• We will use duality to make sure that we never have to worry about more than one capacity region at a time.

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Solving DTNUM directly

25Epochs, TNodes, N

Com

puta

tion

Tim

e, T

(se

c)

Solving DTNUM by Dual Decomposition

26Epochs, TNodes, N

Com

puta

tion

Tim

e, T

Co

mpu

tatio

n Ti

me,

T (

sec)

Causal Algorithms• Finding optimum involves knowing complete

network evolution beforehand. • Greedy DTNUM Algorithm:

– Do greedy maximization at each epoch– Initial costs are assumed 0, and buffer spaces

increase

• Geographic DTNUM Algorithm:– Greedy optimization takes into account node

locations and direction of their movement– May be thought of as generalization of geographic

routing27

Performance

(a): Optimal algorithm, (b) Geographic algorithm(c): Greedy algorithm, (d): Optimal algorithm, with 1/10 the speed of nodes

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Total Epochs

Volu

me

Part D: On the Cost/Delay Tradeoff of Wireless Delay Tolerant

Geographic Routing

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A. Tasiopoulos*, Ch. Tsiaras$, S. Toumpis*

*Informatics Department, Athens University of Economics and Business, Greece

$Department of Informatics, Communication Systems Group, University of Zurich, Switzerland

(WOWMOM 2012, 25-28/6/12, San Francisco, CA)

Basic Idea• In DTNs, there is a tradeoff between packet

delay and cost (including transmission and storage cost)

• Currently, tradeoff appears implicitly in formulations [Juang et al. ‘02, Jain et al. 04, Laoutaris et al. 09, Small et al. 03, etc.]

• We want to capture this tradeoff formally and explicitly– Under optimal operation – Using practical protocols

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Cost/Delay Evolving Graphs (C/DEGs)

• Time is divided in epochs

• The C/DEG is comprised of one subgraph for each epoch

• Transmission delay is 0.

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11

41

31

21

12

42

32

22

13

43

33

23

14

44

34

24

c121

c112

c232

c143

c134c1

24

c11

c12 c1

3c1

4

c2

1

c22

c23

c24

c212

c221

c223

c31

c32

c33 c3

4

c332

c323

c343

c334

c421

c412

c443

c434c4

24

c132

c432

Optimal Cost/Delay Curves (OC/DCs)

• Let two nodes i, j in a network• The OC/DC Cij(t) is the minimum cost with which i

can send a packet to j with a delay of at most t epochs.

• To calculate it, we need to find the minimum cost path between node i0 and the set of nodes i0, j1,…, jt

– Simple minimum cost path problem– Special structure permits fast calculation

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Example OC/DCs

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Achievable Cost/Delay Curves (AC/DCs)

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• Let two nodes i, j in a network• The AC/DC Cij(t) is the minimum cost with

which i can send a packet to j with a delay of at most t epochs, assuming optimization over the parameters of the protocol

• To calculate it, we need simulations

Example AC/DCs

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Delay Tolerant Geographic Routing• When node S has a packet for node D, it

sends it to one of its neighbors using only its local topology and the location of D.

• Traditionally, there is no delay. • Newer approach: wait for topology to change.

– MoVe [Lebrun et al. ‘05]– AeroRP [Peters et al. ‘11]– GeOpps [Leontiadis et al ‘07.]– BRR, CR [Tasiopoulos et al. ‘12]

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Rules for selecting next hop

1. MoVe: A selects node that will pass closest to D2. AeroRP: A selects node that approaches D fastest

3. Min-Cost-per-Progress Rule: minimize

4. Balanced Ratio Rule: minimize

5. Composite Rule: minimize

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BDAD

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ABABAB rrc ,min

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Part E: Delay Tolerant Firework Routing

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A. Sidera$, S. Toumpis* $ Department of Electrical and Computer Engineering,

University of Cyprus, Cyprus

*Department of Informatics, Athens University of Economics and Business, Greece

(Med-Hoc-Net 2011, Sicily, Italy)

DTFR Operation

1. Homing Phase: travel to estimated location of destination using delay tolerant geographic routing

2. Explosion Phase: create multiple copies

3. Spread Phase: systematically search for destination

4. Lock Phase: do routing in usual sense when in same partition with destination

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Performance

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Analysis of Delay Tolerant Geographic Forwarding

• Assume mobile node, placed according to spatial Poisson process at any given time.

• Assume a packet destination at an infinite distance.

• There is an obvious tradeoff between– Speed vp with which packet moves towards the

destination– Transmission cost per distance, cp

• We find cp(vp) curve for specific forwarding protocol, under some approximations.

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