Victoria Manfredi Thesis Defense August 13, 2009 Advisor: Jim Kurose Committee: Andrew Barto Deepak Ganesan Weibo Gong Don Towsley Sensor Control and Scheduling

Victoria ManfrediThesis DefenseAugust 13, 2009

Advisor: Jim KuroseCommittee: Andrew Barto

Deepak Ganesan Weibo Gong Don Towsley

Sensor Control and Scheduling Strategies for Sensor Networks

2

Motivation

Data

Sensor Controls

Bursty, high-bandwidth data, many-to-one data routing to sink: congestion

Wireless, Closed-Loop Sensor Network

How to make sensing robust to delayed and dropped packets?

How to accommodate multiple users?

Multiple users making conflicting sensor requestsChanging network topology

How to make routing robust to network changes?

Where to focus sensing?

Cameras, radars: cannot collect data simultaneously

from all environment locations

3

Contributions

Adaptive sensors– where to focus sensing in adaptive meteorological radar network?

• show lookahead strategies useful when multiple small phenomena, trade-off between scan quality and re-scan interval

– accommodating multiple users?• identify call admission control problem, give complexity results

How to make sensing robust to delayed, dropped packets?– show good application-level performance possible in closed-loop

sensor network when congestion if sensor control prioritized

How to make routing robust to network changes?– propose routing algorithm, show can significantly control

overhead while minimally degrading % of packets delivered

4

Adaptive sensors– where to focus sensing?– multiple users

Prioritizing sensor control trafficRobust routing in dynamic networksConclusions



Outline

5

small scan sectorshigh quality, butmay miss storm

large scan sectorslow quality, but

miss fewer storms


CASA: Adaptive Meteorological Radar Network

6

What are ``good” sensing strategies?

sit-and-spin– all radars always scan 360

myopic– consider only current environmental state

limited lookahead– Kalman filters to predict storm cell attributes k time-steps ahead

full lookahead– formulate as Markov decision process– reinforcement learning to obtain policy: Sarsa()

Sensing Strategies

7

Radar network

Storm model– storms arrive according to spatio-temporal Poisson process – storm dynamics from Kalman filters

Radar sensing model – observed attribute value = true attribute value + Gaussian noise

30 km

Storm Tracking Application

30km

max storm radius: 4km

Depends on scan quality

8

30 km

Performance Metrics

Re-scan interval– how long before storm first observed or rescanned

Scan quality– how well storm observed – function of

• scan sector size• distance from radar• % of storm scanned

– value between 0 and 1

Cost– function of re-scan interval, quality, penalty for missing storms– 2-step and full lookahead have similar cost for 2 radars

9

Optimize over all radars?

1-Step Lookahead

Myopic

Sit-and-Spin

Ave

rage

Qua

lity

Myopic

Sit-and-Spin

1-Step Lookahead

No gains in quality as optimize over more radars

Decreasing gains as optimize over more radars

Max 1 Storm Max 8 Storms

10


Showed lookahead strategies useful when multiple storms, storm radius (much) smaller than radar radius trade-off scan quality and frequency storm scanned may not need to optimize over all radars in network

Related work– track ground targets from airplanes [Kreucher, Hero, 2005] – our focus: track meteorological phenomena using ground radars

Summary

11



Outline

12

QuickTime™ and a decompressor

are needed to see this picture.

Mt. Toby

MA1 Tower

CS Building

Call admission control problem


Virtualize sensing resources– virtualized private sensor network

To each user– looks like own private network– but user only has virtual slice

Users request resources– possibly conflicting requests– which requests to satisfy?

QuickTime™ and a decompressorare needed to see this picture.


13Select set of non-interfering requests that maximizes utility

Call Admission Control Problem

Utility of request j– to requesting user i: uij

– to each other user i: uij

Sensor request– use sensor in particular way possibly during particular time

Sensing strategy– sequence of requests over time

Strategy for User 1

Strategy for User 2

i

i Combine into single utilityuij = uij + uij

ii

i

Scan 360° every 2 min

Scan 360°

14

Space of Problems

Divisible requests? Utility received if only part

of request satisfied? Yes

– scan x of y elevations

No– obtain full scan of storm

Shifting permitted?

Utility received if request executed at different time?

Yes – perform surveillance scan

No– sense storm expected at

location (x,y) at time t

Time

User 1 Request

User 2 Request

Interleaved Requests

User 1 Request

Shifted Request

Time

15

Complexity

Divisible requests?

Shifting permitted?

Polynomial Polynomial PolynomialNP-Complete

Yes No

Shifting permitted?

Yes No Yes No

Same as fractional knapsack problem

Interval scheduling[Arkin, Silverberg, 1987]

Interleave sensor requests

16

Capacity W

w1

v1

wN

vN

vN

T=W Time

Utility for satisfying user i’s request– to user i: vi

– to each other user: 0

Sensing Strategy for User 1 v1

Sensing Strategy for User N

Indivisible, Shifting

NP-complete– assume utility independent of when request executed– In NP: can check whether solution correct in polynomial time

Knapsack Problem

Reduction

w1

wN

17


Requests divisible or fixed in time polynomial-time algorithmsRequests indivisible but may be shifted NP-complete

Summary

Related work– adaptively select set of sensors for task [Jayasumana et al, 2007]

– our focus: virtualizing sensing resources within a sensor

Future work– online, decentralized algorithms– trade-off between maximizing utility and user fairness – implement proposed algorithms in deployed network

18



Outline

19

Many-to-one routing to sink

Congestion

Bursty, high-bandwidth data

Wireless links

How does prioritizing sensor control traffic over data traffic impact application-level performance?

Data

Sensor Controls

Data spatially, temporally redundant

Prefer to delay, drop data rather

than control

Why prioritize sensor control traffic?

Sensor network

Closed-loop sensor network

Data >> control

20

Data

Controlk-1 k k+1

Data from control k-1

Data from control k

Data delay FIFO control delay Priority control

delay

Small data delay, large control delay more data collected in time to compute next sensor control

Update interval

Closed-loop Sensor Networks

Prioritizing sensor control – impact on packet delays?– impact on data collected?

Control loop delayControl, data share queues

e.g., wireless links

21

More data samples

Cramer-Rao bound:

SD(W) ≥ 1 / n I

– accuracy sub-linearly with n

Effect of data packet drops?– accuracy sub-linearly with n


Sensing accuracy changes slowly with # of samples

Std Dev of W from

Fisher information

# of iid samples

Lower bound on std dev of unbiased estimator W (sample mean) from parameter (population mean)

Radars, Sonars, Cameras, …

Better Quality Data

22

Network model– obtain sensor control and data packet delays– CASA network is closed-loop sensor network

Sensing model– convert packet delays into sensing error

Tracking model– convert sensing error into storm location error– tracking: compute next scan for radar from 99% confidence ellipse

Bursty arrivals

Deterministic arrivals

control

data

other

Storm Tracking Application

Delays at bottleneck link dominate,

assume wireless links

23

idx = 1 idx = 25

Per-interval performance gains/losses may accumulate across multiple update intervals

t=1

# intervals

# intervals

RMSE =

(truet-obst)2

√

+

+

+

Tracking Error

idx = 55

+

24

Summary

When network congestion, prioritizing sensor control in closed-loop sensor network quantity, quality of

data, and gives better application-level performance

How to make sensing robust to delayed and dropped packets?

Related work– SS7, ATM, [Fredj et al, 2001] [Kyasanur et al, 2005]– our focus: prioritizing sensor control (not network control)

25



Outline

26

But if frequent changes, adapting is costly: e.g., in MANET may have as much routing control traffic as data

Adapt to every change? yes: potentially perform optimally, but more overhead no: likely perform sub-optimally, but less overhead

What do we mean by robust?

network structure changing over time

protocols must adapt

Robust: solution performs well over many scenarios, solution is not fragile

27

Identify structural properties that make graph reliable, efficiently find subgraph with such properties

But, reliability #P-complete to compute, can’t search

over all sub-graphs

Robust routing: routing subgraph has path from src to dest, as links up/down

Most robust routing subgraph should contain shortest path and have large min cut

Robust Routing

src-dest reliability – prob instantaneous path in stochastic graph– want max reliability sub-graph for overhead

Effect of graph structure on src-dest reliability?– show reliability (in limits) dominated by shortest paths, smallest cuts

28

k-hop braid: most reliable path + all nodes within k-hops

Braid

d

1-Hop Braid 2-Hop BraidMost Reliable Path

d d

s ss

Given fixed amount of overhead, is braid most reliable sub-graph?

reliability simulations + theoretical analysis

(shortest)

29

Theoretical Analysis

Note: lemma does not hold when adding links

How Reliable is Braid?

N

s d

Add black node rather than blue nodes?Lemma: Suppose sub-

graph contains shortest path and 0<n<N 1-hop nodes. Given 1 or 2 extra nodes, to max reliability, use all 1-hop nodes before any 2-hop nodes

Partial braid less reliable than 2-disjoint paths for

1p√2/3ds s d

Partial Braid 2-Disjoint Paths

30

If no path from src to dest:

Step 1: Identify shortest path in network

Step 2: Build braid around shortest path

Step 3: Perform local forwarding within braid e.g., flooding, opportunistic routing,

backpressure

When link breaks, use braid path back to DSR path

Overheard RREQ and RREP contain 1-hop braid info

Use dynamic source routing (DSR)

Braid Routing

DSR vs Braid– path breaks

• use new DSR path or existing 1-hop braid path

– primary difference• control overhead incurred to find this new path

31

Simulation Set-up QualNet

Gauss-Markov mobility – BonnMotion to generate traces– min 0.5 m/s, max 2 m/s– speed, angle updates every 100s

20-80 nodes– 400m transmission radius– 2km x 2km area

1 constant bit-rate flow– 4 pkts/s, 1 million seconds

10 runs, each lasting life of flow

32

# of Nodes

Control Overhead

As node density increases, braid incurs fewer control packets than DSR

Braid

DSR

#

of C

ontr

ol P

acke

ts

33

# of Nodes

Control Overhead

Braid incurs fewer route requests, replies, errors than DSR

Braid

DSR

#

of C

ontr

ol P

acke

ts

Route Requests

Braid

DSR

Braid

DSR

Route Replies

Route Errors

# of Nodes # of Nodes

Up to ~30% fewer requests

Up to ~40% fewer replies

Up to ~25% fewer errors

34

Packets Delivered and Delay

Braid delivers slightly fewer packets, incurs higher delay than DSR

Braid

DSR

% o

f Pa

cke

ts D

eliv

ere

d

# of Nodes

Braid

DSR

De

lay

(se

con

ds)

# of Nodes

(4 million packets)

35

SummaryHow to make routing robust to network changes?

gains depend on network characteristics

Proposed routing algorithm that control overhead by (1) updating routes less frequently(2) performing local forwarding within routing sub-graph

Related work– [Shacham et al 1983] [Lee, Gerla, 2000] [Ganesan et al, 2001] [Ghosh et al, 2007]

– our work: differs in structure and/or usage of routing subgraph

Future work– which network characteristics most impact performance? – joint rate control and routing– what should be braid width (trade-off with interference)?

36



Outline

37

Conclusions

Adaptive sensors– where to focus sensing in adaptive meteorological radar network?

• show lookahead strategies useful when multiple small phenomena, trade-off between scan quality and re-scan interval

– accommodating multiple users?• identify call admission control problem, give complexity results

How to make sensing robust to delayed, dropped packets?– show good application-level performance possible in closed-loop

sensor network when congestion if sensor control prioritized

How to make routing robust to network changes?– propose routing algorithm, show can significantly control

overhead while minimally degrading % of packets delivered

38

Thanks!

Jim Don, Deepak, Andy, Weibo Networks Lab

– Bruno, Mike, Yung-Chih, Daniel2, Majid, Yu, Bo, Patrick, Junning, Giovanni, Guto, Elisha, Suddu, Bing, Sookhyun, Chun …

ALL Lab– Sridhar, Mohammad, George, Sarah, Khash, Ash, Ozgur, Pippin …

Laurie, Tyler, ….

Questions?

39

Adaptive Sensing

40

Re-scan interval– # of decision epochs before storm cell first observed or rescanned

Quality – how well storm cell p observed

– how well sector ri scanned

sr : radar configuration, start, end angles of scan sector

Sr: set of radar configurations

Performance Metrics

Up(p, Sr) = max [ Fc(c(p, sr )) [ Fd(d(r,p)) + (1-) Fw(w(sr ) / 360)] ] sr Sr

% covered radar rotation speed

distance to storm cell

Us(ri, sr) = Fw(w(sr ) / 360)]

41

Goal: maximize quality, minimize re-scan time

Performance Metrics

Cost – Re-scan time and quality + penalty for never scanning storm cell

Pm := penalty for never scanning storm Pr := penalty for not rescanning storm

C = |dij - dij| + (Np -Np) Pm + I(tk)Pro

o

i=1 j=1 k=1

Storm scanned within Tr

decision epochs?

Difference between true and observed # of storm cells

Difference between observed and true storm attribute

Np Ndo Np

42

State

Actions– select scan action that minimizes cost– additionally scan any sector not scanned in last T=4 decision epochs

True state: true = [ x, y, x, y ]T

Observed state: obs = [ x, y ]T

(x,y) x

y

Use Kalman filters to predict storm cell attributes 1 and 2 decision epochs ahead

Limited Look-ahead Strategy

truet = A truet-1 + N[0, Q]obst = B truet + N[0,R]

A, B, Q, R initialized using prior knowledge

Assume:

43

State

Actions

Transition function – encodes observed environment dynamics, obtained from simulator

Cost function– obtained from performance metrics

Sarsa()– linear combination of basis functions to approximate value function– tile coding to obtain basis functions, one tiling for each state variable

Markov Decision Process Formulation

(x,y)

Storm radius

x

y

+# of storm cells, Up quality of storm cells, Us quality of sectors

Full Look-ahead Strategy

44

True state– storms arrivals: spatio-temporal

Poisson process – storm attributes from distributions

derived from real data– max storm radius: 4km– max number of storms

Observed state – observed attribute value = true attribute value plus noise ~ N[0, 2]

10 km or30 km

Simulation Set-up

= (1-u) Vmax /

scaling term

Largest positive value of attribute

Us(ri,sr) quality

45

Scan Quality

2-Step scans have higher quality than Sarsa(), especially when little noise in environment (when 1/ is small)

Avg

Diff

eren

ce in

Qua

lity

(25

0,00

0 st

eps)

2Step - Sarsa

SitandSpin - Sarsa

Max 1 storm

Max 4 storms

1/

46

Cost

Full lookahead and 2-step look-ahead have similar costs

1/

SitandSpin - Full Lookahead

Fullt=1

# timesteps

# timesteps

Ct - Ct2step

Average Difference in Cost

2StepLookahead - FullLookahead

2 radars

47

Re-scan IntervalSit-and-Spin

1-Step

2-Step

Sarsa()

P[X

≤ x]

x = # of decision epochs between storm scans

Sarsa() more likely than 2-step look-ahead to scan storm within

Tr=4 decision epochs

48

Related Work

2005: Mainland, Parkes, Welsh– game theory + reinforcement learning to

allocate resources – learn profit associated with different

actions, rather than profit associated with different state-action pairs

2005: Stone, Sutton, Kuhlmann– robot soccer

2004: Ng, Coates, Diel, Ganapathi, Schulte, Tse, Berger, Liang

– helicopter control 2002: Zilberstein, Washington,

Bernstein, Mouaddib– planetary rovers

Large State-space Reinforcement Learning

Sensor Networks

2005: Kreucher, Hero– look-ahead scheduling of radars on airplanes for detecting and

tracking ground targets – information-theoretic reward, Q-learning

2005: Suvorova, Musicki, Moran, Howard, Scala– target radar beams, select waveform for electronically steered

phased array radars – show 2-step lookahead outperforms one-step look-ahead for

tracking multiple targets

Radar Control

We consider tracking meteorological phenomena using ground radars

Do not consider infinite-horizon case

49

Call Admission Control Problem

50

Divisible, No Shifting

Polynomial-time– assume utility depends on how much of request executed– select max utility sensor request during each conflicting interval

Sensing Strategy for User 1

Sensing Strategy for User 2

Interleaved Sensor Requests

51

Separation of Control/Data

52

(xk-1,yk-1)

(xk,yk)

Network model: control, data delays, depend on scheduling (FIFO, priority)

Sensing model: given scan, quantity and quality of data, estimated storm location

Tracking model: predict storm location based on current, past estimates and observations using Kalman filters

Quality of estimated storm location affects tracking

Quality of tracking affects scan angle, quality of estimates

Timeliness of control, data affects amount of sensed data gathered

Storm Tracking Application: 3 Coupled Models

d

d

c

d

53

Wireless network– radar data sent to control center, sensor control back to radars– much more data traffic than sensor control traffic

Delays at bottleneck link dominate control-loop delay

Network ModelObtain sensor control and

data packet delays

d

d

c

d

Bursty arrivals

Deterministic arrivals

control

data

other

Obtain delays for FIFO, priority queuing using simulation

54

Radar– transmits pulses to estimate reflectivity at point in space

Reflectivity– # of particles in volume of atmosphere– standard deviation,

=

Sensing ModelConvert packet delays into

sensing error

sensing timescan angle width

radar SNR where Nc

Smaller angle, longer time sensing lower sensing error

55

Location of storm centroid– equals location of peak reflectivity– standard deviation,

Kalman filters– generate trajectory of storm centroid– track storm centroid

r d

30 dBzz =

z used in measurement covariance matrix

Convert sensing error into location error, perform tracking

(xk-1,yk-1)

(xk,yk)

Tracking Model

mid-range reflectivity value

distance from radar

Goal: track storm centroid with highest possible accuracy

56

Kalman filter

(xk-1,yk-1)(xk,yk)

Measure: radar data received, measured position yk, with r(,+)

Filter: estimate xk based on yk, predicted x-

k

Predict: next x-(k+1) 99%

confidence region, gives k+1 to scan next time step

Estimated state error covariance matrix, dependson velocity noise model, r(,+)

xk := estimated (location, velocity)

yk := measured (location, velocity)

noisy, with std deviation r(,+)

57

Network parameters

Kalman filter parameters– initialize based on storm data

10 NS-2 simulation runs, 100,000 sec each

Simulation Set-up

data= 2000/30

pkts/s

other= 2000/30

pkts/s

off on

r1 = 1s

r2 = 1s

1= po 2= (1-p)o

control+ data+other 133.37 pkts/s = 148.5 pkts/s

avg load 0.90

idx =

control= 1/ pkts/s

Vary burstiness of ``other” traffic,

Index of dispersion

58

Data Quantity

(seconds)

As and burstiness , gains from prioritizing increase

Number of times more voxels scanned under

priority than under FIFOidx = 55

idx = 25

idx = 1

59

Data Quality

Small decision epoch, bursty traffic: FIFO achieves ~80% as many pulses

as priority ~80% of time

idx1

idx55

= 5sec

idx55

Number of Pulses

= 30sec

idx55 = 5sec

idx55idx1

Reflectivity Standard Deviation

= 30secidx1

idx1

Small decision epoch, bursty traffic: priority has at least 90% as much

uncertainty as FIFO ~90% of the time

x = NFIFO / NPriorityx = r,Priority / r,FIFO

F(x

)

F(x

)

Assuming = 360

60

Number of Pulses

FIFO and Priority each achieve about 6x as many pulses per voxel for = 30 sec vs = 5 sec, and total

# of pulses is independent of

61

NFIFO / Npriority

Data Quantity vs QualityC

DF

r,Priority / r,FIFO

360 scans, = 5sec, very bursty traffic

FIFO achieves at least 80% as many samples as priority ~80% of time

Priority has at least 90% as much

uncertainty as FIFO ~90% of the time

**During times of congestion, prioritizing sensor

control quantity, quality of data

62

Effect of Packet Loss

As system goes into overload sensing accuracy degrades (more) gracefully when sensor control is prioritized

Capacity: when >1000, data dropped

Priority: no sensor control packets dropped

= pkts / second

r (w

ith lo

ss)

/

r (n

o lo

ss)

FIFO: sensor control packets dropped

63

Related Work

Networked Control Systems

Prioritize Network Control

Do not consider effects of prioritizing only sensor control

in a sensor network

Our focus: prioritize sensor control

Service Differentiation for Different Classes of Traffic

2001: Bhatnager, Deb, Nath– assign priorities to packets, forwarding

higher-priority packets more frequently over more paths to achieve higher delivery prob

2005: Karenos, Kalogeraki, Krishnamurthy

– allocate rates to flows based on class of traffic and estimated network load

2006: Tan, Yue, Lau– bandwidth reservation for high-priority

flows in wireless sensor networks

2008: Kumar, Crepadir, Rowaihy, Cao, Harris, Zorzi, La Porta

– differential service for high priority data traffic versus low-priority data traffic in congested areas of sensor network

SS7 telephone signaling system ATM networks, IP networks 1998: Kalampoukas, Varma, Ramakrishan,

2002: Balakrishnan et al, – priority handling of TCP acks

2005: Kyasanur, Padhye, Bahl– separate control channel for controlling

access to shared medium in wireless

data, sensor control sent over network– constrained to be feedback and

measurements of classical control system

– ratio of data to control much smaller than that of closed-loop sensor network

2001: Walsh, Ye– put error from network delays in control eqns

2003: Lemmon, Ling, Sun– drop selected data during overload by

analyzing effect on control equations

We assume amount of data sensor control

64

Robust Routing

65

What do we mean by robust?

T=1 T=2 T=3 T=4

Robust routing: routing subgraph has path from src to dest, as links up/down

2/4

3/4

4/4

66

Given graph G, src, dest, assume links iid and up with prob p

Paths Small p limit:

reliability dominated by shortest paths

Cuts Small q=1-p limit:

un-reliability dominated by smallest cuts

Most robust routing subgraph should contain shortest/most reliable path and have large min cut

What is effect of graph structure on src-dest reliability?Some Intuition

67

P({q0,q1} | {s0,s1}) P(d | {d0,d1})Product always for

adding black node

Theoretical Analysis

d

Proof:

s

P(d|s) = P(d | s0 s1) P(s0 s1|s) + P(d | s0 s1) P(s0 s1|s) + P(d | s0 s1) P(s0 s1|s)

- -- -

Recursively iterate: get eqn with 27 terms

s0

s1 s1

s0

q1

q0 q0 d0

q1 d1

d0

d1

68

Conjecture 1: N extra nodes: 1-hop braid most reliable

From lemma: true for N ≤ 5

Conjectures

Generally: conjecture no “holes” in most reliable graph

N=6 N=6

Conjecture 2: 2N extra nodes: 2-hop braid most reliable

s d s d

Experimentally: for N=6, 2-hop braid more reliable than pyramid

69

Conjectures

p

relia

bilit

y

Conjecture 2: 2N extra nodes: 2-hop braid most reliable

experimentally: for N=6, 2-hop braid more reliable than pyramid

70

Adding edges rather than nodes

Conjecture 3: N+1 extra edges: partial 1-hop braid most reliable

not true, see counterexamples

Partial braid less reliable than 2-disjoint

paths for 1p0

Partial Braid

N=3

2-Disjoint Paths

s sd d

N=4Partial braid less

reliable than 2-disjoint paths for 1p√2/3ds s d

71

Adding edges rather than nodes

link up prob above which 2-disjoint paths more

reliable than partial braid

N

Scaling behavior

As N increases, partial braid more reliable for more values of p

Conjecture 3: N+1 extra edges: partial 1-hop braid most reliable

not true, see counterexamples

72

Have intuition that braids have good reliability properties

Reliability Experiments

But,– how does reliability of braid compare with other routing subgraphs?– what is impact of time between braid re-computations T on reliability?

Experiment set-up– model

• 100 nodes, random graph• links iid, 2-state link model• src, dest randomly chosen

– Monte Carlo simulation• 500 runs, each lasting 100 time-steps

up downp

1-p

1-q

q

Link model

73

Link Failures

# of Nodes Used in Addition to Shortest PathG

ain

in R

elia

bilit

y ov

er S

hort

est P

ath

Braid reliability close to full graph, braid overhead significantly less than full graph

Rel

iabi

lity

T = length of routing update interval

1-hop braid

2-shortest disjoint paths

shortest path

full graph

1-hop braid

full graph

p=0.85, q=0.5, T=5p=0.85, q=0.5


74

Node vs Link Failures

# of Nodes Used in Addition to Shortest PathG

ain

in R

elia

bilit

y ov

er S

hort

est P

ath

All algorithms have lower reliability, braid overhead still less than full graph

Rel

iabi

lity

T = length of routing update interval

1-hop braid


shortest path

full graph

1-hop braid

full graph2-shortest disjoint paths

p=0.85, q=0.5, T=5p=0.85, q=0.5

Node failures imply correlated link failures, as in mobility

75

Routing Experiments

GloMoSim– 60 nodes, 250m transmission radius– 1km x 1km area– 1 cbr flow: 5 million pkts (~29 days)– random waypoint, Gauss Markov mobility

Compare throughput, overhead– AODV– 1-hop braid built around AODV path choose next hop based on last successful use

10 runs, each lasting life of flow

76

Random Waypoint Mobility

Packets delivered: braid delivers up to 5% more

packets than AODV

Braid overhead: ~25% more control overhead than AODV

T = routing update interval (seconds)T = routing update interval (seconds)

77

Gauss-Markov Mobility

Packets delivered: braid delivers up to 5% more

packets than AODV

Insights: braids work well when links can reappear in T Independent link failure

Braid overhead: ~40% more control overhead than AODV

T = routing update interval (seconds) T = routing update interval (seconds)

78

Reliability vs Routing

Reliability gains Throughput gains

don’t use AODV, instead estimate link reliability

Braid construction independent of “best” path algorithm

Reliability experiments iid links shortest path = most reliable path

Routing experiments non-iid links

shortest path ≠ most reliable path

79

Reliability vs Routing

Reliability gains Throughput gains

Consider link correlations, mobility characteristics

Reliability experiments iid links rate at which down links re-appear is “high”

prob down link reappears = 0.5

broken link likely re-appears during T

Routing experiments non-iid links rate at which down links re-appear is “low”

2 nodes meet on avg once every 22.7 min

broken link likely does not re-appear during T

80

Link Failures and Braid Attempts

Documents

Victoria Manfredi Thesis Defense August 13, 2009 Advisor: Jim Kurose Committee: Andrew Barto Deepak Ganesan Weibo Gong Don Towsley Sensor Control and Scheduling