Mncs 16-09-1주-변승규-opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links. part #2

Intelligence Networking and Computing Lab.
Introduction
Low-Duty-Cycle Wireless Sensor Networks
Flooding in Low-Duty-cycle Networks
Review: Typical Issues in Flooding
Motivation
Fit for Intermittent Receivers
Traditional methods with Low-Duty-Cycle
Preliminaries
Network Model
Assumptions
Performance Metrics
Main Design
Design Overview
Flooding Energy Cost and Delay
The Delay pmf of the Energy-Optimal Tree
Decision Making Process
Decision Conflict Resolution
Shape of Opportunistic Flooding
2
Practical Issues
On Node Failures
On Link Quality Change
Evaluation
Simulation Setup
Baseline I : Optimal Performance Bounds
Baseline II : Improved Traditional Flooding
Performance Comparison
Investigation on System Parameters
Evaluation of Practical Issues
Overhead Analysis
Implementation and Evaluation
Experiment Setup
Performance Comparison
Why Opportunistic Flooding is Better
Conclusion
Review

Different wake-up time
If its receivers do not wake up at the same time
A sender has to transmit the same packet multiple times
4
Sender
On
Off
Unreliable wireless link
Unpredictable and unstable wireless medium
A transmission is repeated if the previous transmissions are not successful
Combination of the two features
The problem becomes more difficult
… …

①
②
③
Major Energy Drain
1.3 ms to transmit a TinyOS packet
3 ~ 4 orders of magnitude longer duration
waiting for reception
5
17.4
19.7
16
17
18
19
20
Energy Consumption
of CC2420 Radio
Transmission Idle Listening / Receiving
mA
Energy Consumption of Zigbee If applied directly
Probabilistic Proof: 20%
Two nodes: 60%
Three nodes: 30%
…
N nodes: near-zero%
0 1 5432
0 1 2
Probabilistic Proof: 50%
: 0%

Efficiency or Reliability
6
Source
Relay
Destination
Tradeoff Relationship
If # of the relay nodes is increased, Broadcast Storm occurs
If # of the relay nodes is reduced, the next node could fail to receive a broadcast packet
Blind flooding Routing tree
in always-wake networks
In low-duty-cycle networks
If # of the relay nodes is increased, they cost of high energy consumption
If # of the relay nodes is reduced, the cost of long delays

Two Possible Sensor States
Active : Able to sense an event, or receive a packet
Dormant : Turning off all its modules except a
timer to wake itself up
A node can only receive a packet when it is active,
but can transmit a packet at any time
7
1
0
Working Schedules: 𝑤𝑖, 𝜏
T : working period of the whole network
𝑤𝑖 : string of ‘1’ and ‘0’s denoting the schedule
𝜏 : time units of length, T can be divided into
Each node picks one or more time units as active
Assumptions
Only one flooding in one time
Working schedules are shared
Practical Asynchronous Neighbor Discovery
and Rendezvous for Mobine Sensing Applications,
SenSys ‘08
Unreliable links and collision are exist
Link quality is measured using probe-based
method and updated infrequently
Do not consider capture effect
Hop count = minimum number from source

About Energy Optimality
Flooding in low-duty-cycle is realized by multiple unicasts
Energy-optimal tree’s Energy optimality
If multiple nodes wake up simultaneously
8
About Delay Optimality
F
D
E
D and E receives the packet at time t
F wake up at time instances t +4, t +8, …
0.8
0.7
= 𝑡 + 4.999 ⋯
= 𝑡 + 5.71 ⋯
Delay in the case DF
Delay in the case EF
Delay in the case DF | EF = 𝑡 + 4.26 ⋯
= 𝑡 + 4 ÷ 0.8
= 𝑡 + 4 ÷ 0.7
= 𝑡 + 4 ÷ 1 − 1 − 0.8 1 − 0.7
= A benefit of opportunistic routing

D
A
1) Computation of pmf
9
S
0
1.00
0
0.90
10
0.09
20
0.009
30
…
t
t
35
0.05 …
t5
0.72
15
0.22
25
2) Decision Making Process
Time
𝐸𝑎𝑟𝑙𝑦 𝐸𝑃𝐷
𝐷 𝑝
Time
𝐿𝑎𝑡𝑒 𝐸𝑃𝐷
𝐷 𝑝
3) Decision Making Result
4) Decision Conflict Resolution
Selection of
Forwarding
Selection
0.9 0.7
0.5
Link-Quality-
Based Backoff

Intelligence Networking and Computing Lab. 10
Source
Candidates
S
A
B
C
D
E
F
H
G
(a) Original Network
S
A
B
C
D
E
F
H
G
(b) Sender Selection
S
A
B
C
D
E
F
H
G
(c) B receives the packet early
S
A
B
C
D
E
F
H
G
(d) B receives the packet late

Intelligence Networking and Computing Lab.11

Possible Real World Situation
Physical damage
Energy depletion
Failure of an sender in Opportunistic Flooding
Results only in a larger delay
Due to lower chances for the receivers to get “early packets”
S
A
B
C
D
E
F
H
G
B receives the packet late
S
A
B
C
D
E
F
H
G
Failure occurs in A
S
A
B
C
D
E
F
H
G
B transmits the packet
!

Preferable Simulated Situation & Practice
The qualities of all the links do not change once they are measured
Link quality changes over time
Deviation of Link Quality
Could lead to misestimating whether the packet is “early” or not
13
Time𝐷 𝑝
Time
𝐸𝑃𝐷
𝐷 𝑝
Time𝐷 𝑝′
𝐸𝑃𝐷′
Time
𝐷 𝑝
Time
𝐷 𝑝

Random 10 Topologies, each 1000 flooding packets
200 nodes to 1000 nodes with Random Schedules
Wireless Path Loss / Shadowing Effects
Default Parameters: 𝑙 𝑡ℎ = 0.7, 𝑝 = 0.9
Flooding delay based on 99% Delivery Ratio

Optimal Energy Costs
with Energy Optimal Tree
Optimal Flooding Delay
with Pure Flooding (Blind ?)
Oracle collision-free media access control
Tradeoff between Optimal Energy and Delay
Neither of which can achieve both the optimal simultaneously
16

Collision Avoidance
The same link-quality-based backoff method
To avoid collisions among multiple senders
Reduction of Redundant Transmissions
Stops sending to a certain neighbor after hearing the transmission of another node
To reduce energy costs
Alleviation of HTP
𝑝-persistent backoff scheme
To recover quickly
17

Different Network Sizes
# of nodes: 200  1000, network side length : 200m  400m
But to keep similar density
Performance Gap
ITF↔OF: OF saves about 40% delay and 50% energy cost
OF↔Optimal: very close to the optimal, with around 10% more delay and energy cost
18

Different Duty Cycles
# of nodes: 800
Network size: 300 𝑚 × 300 𝑚
Performance Gap
ITF↔OF: OF achieves 80% of delay with only 30% of transmissions
OF↔Optimal: very close to the optimal, redundant tx is around 400 among 800 nodes
Only 0.5 packets are redundant on average
Opportunistic Delivery Ratio
Significantly reducing the delay of OF compared to ITF
𝑃𝑟 that has more than one active neighbor is higher in a network with a higher duty cycle
19

Comparison with Optimal Schemes
Dotted dash  Energy-Optimal
Blue dash  Delay-Optimal
Performance Gap
OF is quite close to the respective scheme
Not simple tradeoff relationship
20
= Draw upper/lower boundary

Sender Set Link Quality Threshold 𝑙 𝑡ℎ
# of nodes: 800
Network size: 300 𝑚 × 300 𝑚
𝑝: 0.9, 𝑙 𝑡ℎ: 0.51.0, a node’s best link is always selected even if no greater than 𝑙 𝑡ℎ
Applausable Tradeoff Relationship
As 𝑙 𝑡ℎ increases, fewer nodes are included in the sender set
 leading to less opportunistic forwarding
An increasing flooding delay, decreasing energy cost and decreasing opportunistic delivery ratio
21

Quantile Probability 𝑝
# of nodes : 800
Network size : 300 𝑚 × 300 𝑚
𝑙 𝑡ℎ : 0.7, 𝑝 : 0.5  0.9, a node’s best link is always selected even if no greater than 𝑙 𝑡ℎ
Applausable Tradeoff Relationship
As 𝑝 increases, more nodes get the chance to start transmissions
 leading to shorter delay and larger number of transmissions
An increasing flooding delay, decreasing energy cost and decreasing opportunistic delivery ratio
22

Link Quality out-of-Date
As more link quality deviates, more nodes making wrong decision
 𝑁𝑒𝑒𝑑 or 𝑅𝑒𝑑𝑢𝑛𝑑𝑎𝑛𝑡 becomes less reliable
𝐸𝑃𝐷 with 𝐷 𝑝 to make forwarding decisions
Reasonable Changing Range
30%
23
Time𝐷 𝑝′
𝐸𝑃𝐷′
Time
𝐷 𝑝
Time
𝐷 𝑝

Link Quality Measurement
With 10 hello messages among neighbors
Packet Size Ratio, Overhead = Data Packet Size / Control Packet Size
Energy Conservation
When a reasonable amount of flooding bits is sent per link quality update period
24

Deployment
30 MicaZ nodes on indoor testbed
Randomly
Tx power is Tuned down so that they form a 4-hop network
Determination of Duty Cycle
Initialization phase with a 100% duty cycle
Randomly generates a specified working schedule
Pairwise Link Quality Measurement
Between itself and each neighboring node in its neighbor table
By counting the reception ratio of 20 packets
System Parameters
𝑙 𝑡ℎ : 0.6
𝑝 : 0.9
Time unit : 50 𝑚𝑠

Delay Performance
At duty-cycles 2% and above: comparable delay
At duty-cycle of 1%: 25% shorter
Doesn’t show the similar significant delay reduction observed in
the simulation
Physical Limitations of the testbed
4-hop network with only 30 nodes  less opportunistic
Pure-flooding is delay-optimal when a network is not congested
Energy Performance
Due to the small network size and limited number of opportunity
Doesn’t show significant performance gap
27
30~35% ↓

Observation on Delay Distribution
3 stages of flooding
OF achieves 80% more slowly, but 100% more quickly
Observation on Energy Distribution
70% of the nodes in OF transmits only less than 4 times, in ITF transmits 5 times
Observation on Opportunistic Ratio
Opportunistic early packets are received at large hop counts
Especially when the network scale becomes large, Opportunistic Flooding design is very effective
28
1ℎ𝑜𝑝 2ℎ𝑜𝑝 3ℎ𝑜𝑝 4ℎ𝑜𝑝𝑟𝑜𝑜𝑡

Just make the use of Elementary mathematics
First and last
Nothing to waste
Functional Qualifications
Meticulous analysis
Imitable study
Future work
Flooding Time Synchronization Protocol, SenSys ’04
Practical Asynchronous Neighbor Discovery and Rendezvous for Mobine Sensing Applications, SenSys ‘08
Advanced CLOF
29

Mncs 16-09-1주-변승규-opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links. part #2

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (11)

Similar to Mncs 16-09-1주-변승규-opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links. part #2

Similar to Mncs 16-09-1주-변승규-opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links. part #2 (20)

Recently uploaded

Recently uploaded (20)

Mncs 16-09-1주-변승규-opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links. part #2