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Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links. Shuo Guo , Yu Gu, Bo Jiang and Tian He University of Minnesota, Twin Cities . Background. Traffic Control. Habit Monitoring. Target Tracking. Space Monitor. Border Control.

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opportunistic flooding in low duty cycle wireless sensor networks with unreliable links

Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links

Shuo Guo, Yu Gu, Bo Jiang and Tian He

University of Minnesota, Twin Cities

Shuo Guo @ University of Minnesota

background
Background

Traffic

Control

Habit

Monitoring

Target

Tracking

Space

Monitor

Border

Control

Infrastructure health

Monitoring

  • Why a low-duty-cycle WSN is needed?
    • Growing need for sustainable sensor networks
    • Slow progress on battery capacity

sustainable sensor networks

Shuo Guo @ University of Minnesota

background1
Background
  • Sleep latency in low-duty-cycle wireless sensor networks

Sender

t

Receiver

t

Active State

Dormant State

Low Duty Cycle => Long Network Lifetime

Shuo Guo @ University of Minnesota

motivation
Motivation

C

B

D

C

B

D

C

D

B

A

t

A

Active State

Dormant State

Shuo Guo @ University of Minnesota

  • Why is Flooding in low-duty-cycle WSNs different?
    • No longer consists of a number of broadcasts.
    • Instead, it consists a number of unicasts.
motivation1
Motivation
  • Existing solutions are not suitable to be directly applied to low-duty-cycle wireless sensor networks

Shuo Guo @ University of Minnesota

X. Chen, M. Faloutsos, and S. Krishnamurthy. Power Adaptive Broadcasting with Local Information in Ad Hoc Networks. ICNP’03.

J. W. Hui and D. Culler. The Dynamic Behavior of a Data Dissemination Protocol for Network Programming at Scale. SenSys’04.

P. Kyasanur, R. R. Choudhury, and I. Gupta. Smart Gossip: An Adaptive Gossip-based Broadcasting Service for Sensor Networks. MASS’06.

P. Levis, N. Patel, D. Culler, and S. Shenker. Trickle: A Self-Regulating Algorithm for Code Propagation and Maintenance in Wireless Sensor Networks. NSDI’04.

L. Li, R. Ramjee, M. Buddhikot, and S. Miller. Network Coding-Based Broadcast in Mobile Ad-hoc Networks. INFOCOM’07.

M. J. Miller, C. Sengul, and I. Gupta. Exploring the Energy-Latency Trade-Off for Broadcasts in Energy-Saving Sensor Networks. ICDCS’05.

F. Stann, J. Heidemann, R. Shroff, and M. Z. Murtaza. RBP: Robust Broadcast Propagation in Wireless Networks. SenSys’06

network model and assumptions
Network Model and Assumptions

Shuo Guo @ University of Minnesota

  • Local synchronization of sensor nodes
  • Pre-determined working schedules shared with all neighbors.
  • Unreliable wireless links
    • The probability of a successful transmission depends on the link quality q
  • Flooding packets are only forwarded to a node with larger hop-count to avoid flooding loops
design goal
Design Goal

B

C

A

Two challenging issues

  • Redundant transmissions
  • Collisions

Shuo Guo @ University of Minnesota

Fast data dissemination: shorter flooding delay

Less transmission redundancy: less energy cost

tree based simple solution
Tree-based Simple Solution

Shuo Guo @ University of Minnesota

  • Energy-Optimal Tree
    • No redundant transmissions
    • Long flooding delay
main idea
Main Idea
  • Early Packets
  • Help reduce delay
  • SEND

Decision Making

  • Late Packets
  • Redundant
  • DO NOT SEND

for each neighbor

  • Early packets are forwarded to reduce delay
  • Late packets are not forwarded to reduce energy cost

Shuo Guo @ University of Minnesota

  • Adding opportunistically early links into the energy-optimal routing tree
how to determine early packets
How to Determine Early Packets?

Q1:When will B receive A’s packet?

Q2:Is this time early enough?

A

B

By the time Dp, the probability that B has received the packet is p

B’s delay distribution

p-quantile

EPD < Dp, SEND

EPD > Dp, DO NOT SEND

t

Dp

Delay distribution that B receives packets from its parent!

Early Packets’ EPD

Late Packets’ EPD

Shuo Guo @ University of Minnesota

Flooding delay distribution (pmf) at node B

Delay threshold Dp based on a threshold probability p

Expected Packet Delay (EPD) : the packet delay when B receives A’s packet

how to determine early packets1
How to Determine Early Packets?

A

B

B’s delay distribution

p-quantile

t

Dp

Early Packets’ EPD

Late Packets’ EPD

Shuo Guo @ University of Minnesota

Flooding delay distribution (pmf) at node B

Delay threshold Dp based on a threshold probability p

Expected Packet Delay (EPD) : the packet delay when B receives A’s packet

11

delay distribution computation
Delay Distribution Computation

0.9

0.8

Linear Complexity!

Shuo Guo @ University of Minnesota

how to determine early packets2
How to Determine Early Packets?

A

B

B’s delay distribution

p-quantile

t

Dp

Early Packets’ EPD

Late Packets’ EPD

Shuo Guo @ University of Minnesota

Flooding delay distribution (pmf) at node B

Delay threshold Dp based on a threshold probability p

Expected Packet Delay (EPD) : the packet delay when B receives A’s packet

13

expected packet delay computation
Expected Packet Delay Computation

B’s working schedule

EPD = 24

8

16

24

A’s second try to B

A receives packet

A’s first try to B

Active State

Dormant State

A is expected to transmit twice!

t

Shuo Guo @ University of Minnesota

how to determine early packets3
How to Determine Early Packets?

A

B

B’s delay distribution

p-quantile

t

Dp

Early Packets’ EPD

Late Packets’ EPD

Shuo Guo @ University of Minnesota

Flooding delay distribution (pmf) at node B

Delay threshold Dp based on a threshold probability p

Expected Packet Delay (EPD) : the packet delay when B receives A’s packet

15

final decision making
Final Decision Making

Dp= 16

EPD= 24

For p = 0.8

Dp = 16< EPD = 24.

A will not start the transmission to B!

Shuo Guo @ University of Minnesota

slide17
How “early” an early packet should be?

Delay Distribution

p-quantile

Dp

t

Late Packets’ EPD

Early Packets’ EPD

  • Small p value: smaller Dp, fewer early packets, longer flooding delay, less energy cost => Energy-Sensitive
  • Large p value: larger Dp, more early packets, shorter flooding delay, more energy cost => Time-Sensitive

Shuo Guo @ University of Minnesota

evaluation
Evaluation

Shuo Guo @ University of Minnesota

  • Test-bed Implementation
    • 30 MicaZ nodes form a 4-hop network
    • Randomly generated working schedules
    • Duty cycle from 1% to 5%
  • Simulation Setup
    • Randomly generated network, 200~1000 nodes
    • Randomly generated working schedules
    • Duty cycle from 1%~20%
evaluation1
Evaluation

Shuo Guo @ University of Minnesota

  • Baseline 1: optimal performance bounds
    • Delay optimal: collision-free pure flooding
    • Energy optimal: tree-based solution
  • Baseline 2: improved pure flooding
    • Two techniques are added to avoid collisions:
      • Link-quality based back-off scheme
      • p-persistent back-off scheme
simulation results
Simulation Results

Improved Pure Flooding

Flooding delay vs. Duty Cycle

Shuo Guo @ University of Minnesota

simulation results1
Simulation Results

Improved Pure Flooding

Improved Pure Flooding

Opportunistic Flooding

Flooding delay vs. Duty Cycle

Shuo Guo @ University of Minnesota

simulation results2
Simulation Results

Improved Pure Flooding

Improved Pure Flooding

Improved Pure Flooding

Opportunistic Flooding

Opportunistic Flooding

Optimal Delay Bound

Flooding delay vs. Duty Cycle

Shuo Guo @ University of Minnesota

simulation results3
Simulation Results

Improved Pure Flooding

Energy Cost vs. Duty Cycle

Shuo Guo @ University of Minnesota

simulation results4
Simulation Results

Improved Pure Flooding

60%

Opportunistic Flooding

Energy Cost vs. Duty Cycle

Shuo Guo @ University of Minnesota

simulation results5
Simulation Results

Improved Pure Flooding

60%

Opportunistic Flooding

Optimal Energy Bound

Energy Cost vs. Duty Cycle

Shuo Guo @ University of Minnesota

test bed performance
Test-bed Performance

Improved Pure Flooding

Opportunistic Flooding

30%

Flooding delay vs. Duty Cycle

Energy Cost vs. Duty Cycle

Shuo Guo @ University of Minnesota

test bed performance1
Test-bed Performance

Ratio of Opportunistically Early Packets

Hop Count 2

Hop Count 4

Hop Count 1

Hop Count 3

Shuo Guo @ University of Minnesota

test bed performance2
Test-bed Performance

Improved Pure Flooding

Opportunistic Flooding

30%

Flooding delay vs. Duty Cycle

Energy Cost vs. Duty Cycle

Shuo Guo @ University of Minnesota

28

summary
Summary

Shuo Guo @ University of Minnesota

The flooding process in low-duty-cycle networks consists of a number of unicasts. This feature calls for a new solution

Opportunistically early packets are forwarded outside the energy-optimal tree to reduce the flooding delay

Late packets are not forwarded to reduce energy cost

Evaluation reveals our design approaches both energy- and delay-optimal bounds

decision conflict resolution
Decision Conflict Resolution

Shuo Guo @ University of Minnesota

  • The selection of flooding senders
    • Only a subset of neighbors are considered as a node’s flooding packet senders.
    • Flooding senders have a good enough link quality between each other.
    • Avoid hidden terminal problem without the overhead caused by using RTS/CTS control packets
decision conflict resolution1
Decision Conflict Resolution

Shuo Guo @ University of Minnesota

  • Link-quality based back-off scheme
    • Better link quality, higher chance to send first
    • Further avoids collision when two nodes can hear each other and make the same decision
    • Further saves energy since the node with the best link quality has the highest chance to send
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