Sprinkler: A Reliable and Energy Efficient Data

Dissemination Service in Extreme Scale Wireless

Networks of Embedded Devices

Vinayak NaikVinayak Naik, Anish Arora, , Anish Arora,

Prasun Sinha, and Hongwei ZhangPrasun Sinha, and Hongwei Zhang

Dependable Distributed and Networked Systems

December 7, 2005December 7, 2005

Vinayak NaikVinayak Naik, Anish Arora, , Anish Arora,

Prasun Sinha, and Hongwei ZhangPrasun Sinha, and Hongwei Zhang

Dependable Distributed and Networked Systems

December 7, 2005December 7, 2005

7

New Model due to Extreme Scale Wireless Embedded Devices

• Embedded devices are constrained in following resources: CPU Memory Power

• Characteristics of wireless medium Spatial and Temporal variation in link quality Hidden terminal effect

• Extreme scale demands sub-linear time complexity O(n) isn’t good enough for resource constrained devices

• Different model as compared to that of the Internet Existing network services may not work

8

Outline

a. Motivation and Requirements

b. Insight behind Solution

c. Formal Problem Statement and Algorithms

d. Analysis and Comparison

e. Conclusion

10

Irrigating ExScal

• Motivation behind data dissemination service

1. Reprogramming in the field (hundreds of packets)

2. System reconfiguration (tens of packets)

3. Health monitoring (< ten packets)

• Problem of bulk data dissemination service

1. 100% Reliability

2. Energy efficiency

3. Low latency

11

Outline

a. Motivation and Requirements

b. Insight behind Solution

c. Formal Problem Statement and Algorithms

d. Analysis and Comparison

e. Conclusion

12

Energy Saved is Energy Generated

Operation Current Draw

Mote Stargate

Microprocessor and Idle Radio 8 mA 330 mA

Packet Reception 16 mA 280 mA

Packet Transmission 24 mA 650 mA

• Load shedding

1. Packet Transmissions

2. Microprocessor and Idle Radio (Not covered in this talk)

13

Unit Disk Model

R = Transmission Radius

R

R A

B

14

Connected Dominating Set

Fewer number of senders

CDS

15

Hidden Terminal Effect

Collision!

Lost packet

A

BC

16

Time Division Multiple Access

Schedule transmissions

R R

A

C BD

17

Outline

a. Motivation and Requirements

b. Insight behind Solution

c. Formal Problem Statement and Algorithms

d. Analysis and Comparison

e. Conclusion

18

Formal Problem Statement

Divide-n-Conquer

1. An algorithm to compute a CDS, of size O(1) times the

minimum, in O(1) time

2. An algorithm to compute a distance-2 vertex coloring, with

O(1) times the minimum # of colors, in O(1) time

3. A reliable data dissemination protocol that utilizes a CDS

and a corresponding distance-2 vertex coloring

Assumptions

1. Minimum density: ≥ 1 node per square of length

2. Location information

19

Algorithm to Compute CDS

Division of network into disjoint square-shaped clusters,each of

length

Election of a cluster-head in each cluster

Decision whether a cluster-head belongs to CDS or not

Variables:

1. r be the total number of cluster-heads in X axis

2. c be the total number of cluster-heads in Y axis

3. u(i,j) be any cluster-head and (i,j) be its (X,Y) coordinates

1. Program: A node u(i,j) ∈ M, where 0 ≤ i ≤ r−1 and 0 ≤ j ≤ c−1, if

• r mod 3 ≡ 0 : [i mod 3 ≡ 1] ∨ [(i mod 3 ≡ 1) ∧ (0 < i < r−1) ∧ (j = 0)]

• r mod 3 ≡ 1 : [i mod 3 ≡ 0] ∨ [(i mod 3 ≡ 0) ∧ (j = 0)]

• r mod 3 ≡ 2 : [i mod 3 ≡ 1] ∨ [(i mod 3 ≡ 1) ∧ (i ≡ 0) ∧ (j = 0)]

O(1)

20

CDS Computation

Selecting cluster-heads, Computing CDS

R

Clustering,

Performance Ratio =

21

D-2 Vertex Coloring

R

8 9 10 11 1213

14

14

15

15

0

0

1

1

2

2

3

3

4

4 5

5

6

6

6 7

7

7

Numbers indicate colors.

R

< 2R

Performance Ratio =

22

Data Dissemination Protocol

• Streaming phase

Only CDS nodes transmit

Transmissions in TDMA slots

Results in reliable data dissemination to all CDS nodes

• Recovery phase

Any node can transmit

Unscheduled transmissions

Results in reliable data dissemination to all the nodes

23

Streaming Phase

R 2

A B C D

0 0 01 201

Lost!

1

Empty Slot

2

Empty Slot

12,1

Recovery Req

2,1

Recovery Req

3 1

Recovery Packet

14 1E

25

Models for Real Radio

• Radio models in real environment are more complex than unit disk model

• Packet delivery rate for XSS in an outdoor environment

• Similarly, for indoor testbeds

27

Adapting Sprinkler to Real Radio Models

• Input parameter

Transmission radius ( )

• Procedure

Initialize = , where is the reliable communication

range (100% packet delivery)

Keep incrementing till the number of transmissions for the

test broadcast are reducing

• Density assumption still holds

Since every square of length contains at least one node,

every square of length also contains at least one node

29

Outline

a. Motivation and Requirements

b. Insight behind Solution

c. Formal Problem Statement and Algorithms

d. Analysis and Comparison

e. Conclusion

31

Anatomy of XSS

• XSS: Extreme Scaling Stargate Stargate

SMC 2532W-B High Power IEEE 802.11b PCMCIA card

BU-303 GPS mouse via USB

External antenna connection

33

Kansei [The 2nd TinyOS Technology Exchange at Berkeley, 2005]

• A testbed containing 200 pairs of XSSs and XSMs

• A multi-hop IEEE 802.11 network Using attenuators and S/W Tx

power control

• Applications Debugging Measuring performances of

protocols

• Web interface for experimentations http://exscal.nullcode.org/kansei

34

Scalability of Sprinkler

Hops Density

36

Comparison

• Existing reliable bulk data dissemination services Deluge Infuse MNP PSFQ

• Deluge protocol Doesn’t uses CDS and TDMA Uses sender suppression technique to reduce number of

packet transmissions Commonly used service for mote reprogramming

• Simulation and experiment setup A 7x7 network with a base station at a corner Payload of 240 packets

37

Performance: # Packet Transmissions

Deluge Sprinkler

Source Source

38

Performance: Latency

Deluge Sprinkler

Source Source

39

Outline

a. Motivation and Requirements

b. Insight behind Solution

c. Formal Problem Statement and Algorithms

d. Analysis and Comparison

e. Conclusion

40

Conclusion

• Sprinkler: Reliable and energy efficient data dissemination

service [The 26th IEEE Real-Time Systems Symposium at Miami, 2005]

1. Energy efficient

– Reduces # packet transmissions

2. Scalable

– Constant time algorithms

3. Low latency

– Pipelines transmissions in space

• Future work

• Use of hexagon-shaped clusters instead of square-shaped clusters

• CDS and D-2 vertex coloring in the presence of holes of bounded

size and regular shape