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

Seung-gyu, [email protected]

Intelligence Networking & Computing Lab.Dept. of Electrical & Computer Eng.

Pusan National University

Shuo Guo, Liang He, Yu Gu, Bo Jiang, Tian HeIEEE Transactions on Computers, November 2014

Opportunistic Flooding in Low-Duty-CycleWireless Sensor Networks with Unreli-

able LinksPart #2

1

Intelligence Networking and Comput-ing Lab. 2

IntroductionLow-Duty-Cycle Wireless Sensor NetworksFlooding in Low-Duty-cycle NetworksReview: Typical Issues in Flooding

MotivationFit for Intermittent ReceiversTraditional methods with Low-Duty-Cycle

PreliminariesNetwork ModelAssumptionsPerformance Metrics

Main DesignDesign OverviewFlooding Energy Cost and DelayThe Delay pmf of the Energy-Optimal TreeDecision Making ProcessDecision Conflict ResolutionShape of Opportunistic Flooding

Contents

Practical IssuesOn Node FailuresOn Link Quality Change

EvaluationSimulation SetupBaseline I : Optimal Performance BoundsBaseline II : Improved Traditional FloodingPerformance ComparisonInvestigation on System ParametersEvaluation of Practical IssuesOverhead Analysis

Implementation and EvaluationExperiment SetupPerformance ComparisonWhy Opportunistic Flooding is Better

Conclusion

Review

Intelligence Networking and Computing Lab.

Review

I. Flooding in Low-Duty-Cycle NetworksII. Traditional Flooding with Intermittent Re-

ceiversIII. Issues in Low-Duty-Cycle Flooding

IV. OptimalityV. Main Design

Intelligence Networking and Computing Lab. 4

Review Broadcast in Low-Duty-Cycle Networks

Different wake-up timeIf its receivers do not wake up at the same time

A sender has to transmit the same packet multiple times

SenderOnOff

Unreliable wireless linkUnpredictable and unstable wireless medium

A transmission is repeated if the previous transmissions are not successful

Combination of the two featuresThe problem becomes more difficult

… …


①

②

③

Review Traditional Flooding with Intermittent Receivers

Major Energy Drain1.3 ms to transmit a TinyOS packet3 ~ 4 orders of magnitude longer duration waiting for reception

Series116

17

18

19

20

17.4

19.7

Energy Consumption of CC2420 Radio

TransmissionIdle 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%


Review Issues in Low-Duty-Cycle Flooding

Efficiency or ReliabilitySourceRelayDestina-tion

Tradeoff RelationshipIf # of the relay nodes is increased, Broadcast Storm occursIf # of the relay nodes is reduced, the next node could fail to receive a broadcast packet

Blind flood-ing

Routing tree

in always-wake networks

In low-duty-cycle networksIf # of the relay nodes is increased, they cost of high energy consumptionIf # of the relay nodes is reduced, the cost of long delays


Review Network Model

Two Possible Sensor StatesActive : Able to sense an event, or receive a packetDormant : Turning off all its modules except a timer to wake itself upA node can only receive a packet when it is active, but can transmit a packet at any time

10

Working Schedules: T : working period of the whole network : string of ‘1’ and ‘0’s denoting the schedule : time units of length, T can be divided intoEach node picks one or more time units as active

AssumptionsOnly one flooding in one time

Working schedules are sharedPractical Asynchronous Neighbor Discovery and Rendezvous for Mobine Sensing Applica-tions, SenSys ‘08

Unreliable links and collision are existLink quality is measured using probe-based method and updated infrequentlyDo not consider capture effect

Hop count = minimum number from source


Main Design Optimality

About Energy OptimalityFlooding in low-duty-cycle is realized by multiple unicasts

Energy-optimal tree’s Energy optimality

If multiple nodes wake up simultaneously

About Delay Optimality

FD

E

D and E receives the packet at time tF wake up at time instances t +4, t +8, …

0.8

0.7

¿ 𝑡+4.999⋯¿ 𝑡+5.71⋯

Delay in the case DFDelay in the case EF

Delay in the case DF | EF ¿ 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

Review Main Design

1) Computation of pmf

S

0

1.00

0

0.90

100.0920

0.00930

…

t

t

350.05 …

t5

0.72

15

0.22

25

2) Decision Making Process

Time

𝐸𝑎𝑟𝑙𝑦 𝐸𝑃𝐷

𝐷𝑝

Time

𝐿𝑎𝑡𝑒𝐸𝑃𝐷

𝐷𝑝

3) Decision Making Result4) Decision Conflict Resolution

Selection of Forwarding Selection

0.9 0.7

0.5

Link-Quality-Based Back-off


Main Design Shape of Opportunistic Flooding

SourceCandi-dates

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


Practical Issues

I. On Node FailuresII. On Link Quality Change

11


Practical Issues On Node Failures

Possible Real World SituationPhysical damageEnergy depletion

Failure of an sender in Opportunistic Flooding

Results only in a larger delayDue 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

!


Practical Is-sues On Link Quality Change

Preferable Simulated Situation & PracticeThe qualities of all the links do not change once they are measuredLink quality changes over time

Deviation of Link QualityCould lead to misestimating whether the packet is “early” or not

Time𝐷𝑝

Time

𝐸𝑃𝐷

𝐷𝑝

Time𝐷𝑝 ′

𝐸𝑃𝐷 ′Time


𝐷𝑝

Time

𝐿𝑎𝑡𝑒𝐸𝑃𝐷

𝐷𝑝


Evaluation

I. Simulation SetupII. Baseline I: Optimal Performance BoundsIII. Baseline II: Improved Traditional Flood-

ingIV. Performance Comparison

V. Investigation on System Parameters

14


Evaluation Simulation Setup

Random 10 Topologies, each 1000 flooding packets

200 nodes to 1000 nodes with Random Schedules

Wireless Path Loss / Shadowing Effects

Default Parameters: ,

Flooding delay based on 99% Delivery Ratio


Evaluation Baseline I: Optimal Performance Bounds

Optimal Energy Costswith Energy Optimal Tree

Optimal Flooding Delaywith Pure Flooding (Blind ?)Oracle collision-free media access control

Tradeoff between Optimal Energy and DelayNeither of which can achieve both the optimal simultaneously


Evaluation Baseline II: Improved Traditional Flood-ing

Collision AvoidanceThe same link-quality-based backoff methodTo avoid collisions among multiple senders

Reduction of Redundant TransmissionsStops sending to a certain neighbor after hearing the transmission of another nodeTo reduce energy costs

Alleviation of HTP-persistent backoff schemeTo recover quickly


Evaluation Performance Comparison

Different Network Sizes# of nodes: 200 1000, network side length : 200m 400mBut to keep similar density

Performance GapITF↔OF: OF saves about 40% delay and 50% energy costOF↔Optimal: very close to the optimal, with around 10% more delay and energy cost



Different Duty Cycles# of nodes: 800Network size: 300 300

Performance GapITF↔OF: OF achieves 80% of delay with only 30% of transmissionsOF↔Optimal: very close to the optimal, redundant tx is around 400 among 800 nodesOnly 0.5 packets are redundant on average

Opportunistic Delivery RatioSignificantly reducing the delay of OF compared to ITF that has more than one active neighbor is higher in a network with a higher duty cycle



Comparison with Optimal SchemesDotted dash Energy-OptimalBlue dash Delay-Optimal

Performance GapOF is quite close to the respective schemeNot simple tradeoff relationship

= Draw upper/lower boundary


Evaluation Investigation on System Parameters

Sender Set Link Quality Threshold # of nodes: 800Network size: 300 300 : 0.9, : 0.51.0, a node’s best link is always selected even if no greater than

Applausable Tradeoff RelationshipAs increases, fewer nodes are included in the sender set

leading to less opportunistic forwardingAn increasing flooding delay, decreasing energy cost and decreasing opportunistic delivery ratio


Evaluation Investigation on System Parameters

Quantile Probability # of nodes : 800Network size : 300 300 : 0.7, : 0.5 0.9, a node’s best link is always selected even if no greater than

Applausable Tradeoff RelationshipAs increases, more nodes get the chance to start transmissions

leading to shorter delay and larger number of transmissionsAn increasing flooding delay, decreasing energy cost and decreasing opportunistic delivery ratio


Evaluation Evaluation of Practical Issues

Link Quality out-of-DateAs more link quality deviates, more nodes making wrong decision

or becomes less reliable with to make forwarding decisions

Reasonable Changing Range30%

Time𝐷𝑝 ′

𝐸𝑃𝐷 ′Time


𝐷𝑝

Time

𝐿𝑎𝑡𝑒 𝐸𝑃𝐷

𝐷𝑝


Evaluation Overhead Analysis

Link Quality MeasurementWith 10 hello messages among neighborsPacket Size Ratio, Overhead = Data Packet Size / Control Packet Size

Energy ConservationWhen a reasonable amount of flooding bits is sent per link quality update period


Implementation and Evaluation

I. Experiment SetupII. Performance Comparison

III. Why Opportunistic Flooding is Better

25


Implementa-tion

and EvaluationExperiment Setup

Deployment30 MicaZ nodes on indoor testbedRandomly Tx power is Tuned down so that they form a 4-hop network

Determination of Duty CycleInitialization phase with a 100% duty cycleRandomly generates a specified working schedule

Pairwise Link Quality MeasurementBetween itself and each neighboring node in its neighbor tableBy counting the reception ratio of 20 packets

System Parameters : 0.6 : 0.9 Time unit : 50


Implementationand Evaluation Performance Comparison

Delay PerformanceAt duty-cycles 2% and above: comparable delayAt duty-cycle of 1%: 25% shorterDoesn’t show the similar significant delay reduction ob-served in the simulation

Physical Limitations of the testbed4-hop network with only 30 nodes less opportunisticPure-flooding is delay-optimal when a network is not con-gested

Energy PerformanceDue to the small network size and limited number of op-portunityDoesn’t show significant performance gap

30~35% ↓


Implementationand Evaluation Why Opportunistic Flooding is Better

Observation on Delay Distribution3 stages of floodingOF achieves 80% more slowly, but 100% more quickly

Observation on Energy Distribution70% of the nodes in OF transmits only less than 4 times, in ITF transmits 5 times

Observation on Opportunistic RatioOpportunistic early packets are received at large hop countsEspecially when the network scale becomes large, Opportunistic Flooding design is very effective

1h𝑜𝑝 2h𝑜𝑝 3h𝑜𝑝4h𝑜𝑝𝑟𝑜𝑜𝑡


Conclusion Delay-driven Flooding Method

Just make the use of Elementary mathematicsFirst and lastNothing to waste

Functional QualificationsMeticulous analysisImitable study

Future workFlooding Time Synchronization Protocol, SenSys ’04Practical Asynchronous Neighbor Discovery and Rendezvous for Mobine Sensing Applications, SenSys ‘08

Advanced CLOF

Seung-gyu, [email protected]

Intelligence Networking & Computing Lab.Dept. of Electrical & Computer Eng.

Pusan National UniversityIntelligence Networking and Comput-ing Lab.

I appreciate your deep interest

Engineering

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