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
… …
Intelligence Networking and Computing Lab. 5
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%
Intelligence Networking and Computing Lab. 6
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
Intelligence Networking and Computing Lab. 7
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
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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
Intelligence Networking and Computing Lab. 8
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
Intelligence Networking and Computing Lab. 9
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
Intelligence Networking and Computing Lab. 10
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
Intelligence Networking and Computing Lab.
Practical Issues
I. On Node FailuresII. On Link Quality Change
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Intelligence Networking and Computing Lab.
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
!
Intelligence Networking and Computing Lab. 13
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
𝐸𝑎𝑟𝑙𝑦 𝐸𝑃𝐷
𝐷𝑝
Time
𝐿𝑎𝑡𝑒𝐸𝑃𝐷
𝐷𝑝
Intelligence Networking and Computing Lab.
Evaluation
I. Simulation SetupII. Baseline I: Optimal Performance BoundsIII. Baseline II: Improved Traditional Flood-
ingIV. Performance Comparison
V. Investigation on System Parameters
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Intelligence Networking and Computing Lab.
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
Intelligence Networking and Computing Lab. 16
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
Intelligence Networking and Computing Lab. 17
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
Intelligence Networking and Computing Lab. 18
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
Intelligence Networking and Computing Lab. 19
Evaluation Performance Comparison
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
Intelligence Networking and Computing Lab. 20
Evaluation Performance Comparison
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
Intelligence Networking and Computing Lab. 21
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
Intelligence Networking and Computing Lab. 22
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
Intelligence Networking and Computing Lab. 23
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
𝐸𝑎𝑟𝑙𝑦 𝐸𝑃𝐷
𝐷𝑝
Time
𝐿𝑎𝑡𝑒 𝐸𝑃𝐷
𝐷𝑝
Intelligence Networking and Computing Lab. 24
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
Intelligence Networking and Computing Lab.
Implementation and Evaluation
I. Experiment SetupII. Performance Comparison
III. Why Opportunistic Flooding is Better
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Intelligence Networking and Computing Lab.
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
Intelligence Networking and Computing Lab. 27
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% ↓
Intelligence Networking and Computing Lab. 28
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
Intelligence Networking and Computing Lab. 29
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