Data Dissemination in Wireless Sensor Networks · 2014. 11. 10. · (TOSSIM) • Redundancy: ! •...
Transcript of Data Dissemination in Wireless Sensor Networks · 2014. 11. 10. · (TOSSIM) • Redundancy: ! •...
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Data Dissemination in Wireless Sensor Networks
Neil Patel UC Berkeley
David Culler UC Berkeley
Scott Shenker UC Berkeley
ICSI
Philip Levis UC Berkeley
Intel Research Berkeley
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NSDI, Mar 2004 2
Sensor Networks
• Sensor networks are large collections of small, embedded, resource constrained devices – Energy is the limiting factor
• A low bandwidth wireless broadcast is the basic network primitive (not end-to-end IP) – Standard TinyOS packet data payload is 29 bytes
• Long deployment lifetimes (months, years) require retasking
• Retasking needs to disseminate data (a program, parameters) to every node in a network
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NSDI, Mar 2004 3
To Every Node in a Network
• Network membership is not static – Loss – Transient disconnection – Repopulation
• Limited resources prevent storing complete network population information
• To ensure dissemination to every node, we must periodically maintain that every node has the data.
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NSDI, Mar 2004 4
The Real Cost
• Propagation is costly – Virtual programs (Maté, TinyDB): 20-400 bytes – Parameters, predicates: 8-20 bytes – To every node in a large, multihop network…
• But maintenance is more so – For example, one maintenance transmission every minute – Maintenance for 15 minutes costs more than 400B of data – For 8-20B of data, two minutes are more costly!
• Maintaining that everyone has the data costs more than propagating the data itself.
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NSDI, Mar 2004 5
Three Needed Properties
• Low maintenance overhead – Minimize communication when everyone is up to date
• Rapid propagation – When new data appears, it should propagate quickly
• Scalability – Protocol must operate in a wide range of densities – Cannot require a priori density information
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NSDI, Mar 2004 6
Existing Algorithms Are Insufficient
• Epidemic algorithms – End to end, single destination communication, IP overlays
• Probabilistic broadcasts – Discrete effort (terminate): does not handle disconnection
• Scalable Reliable Multicast – Multicast over a wired network, latency-based suppression
• SPIN (Heinzelman et al.) – Propagation protocol, does not address maintenance cost
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NSDI, Mar 2004 7
Solution: Trickle
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NSDI, Mar 2004 8
Solution: Trickle
• “Every once in a while, broadcast what data you have, unless you’ve heard some other nodes broadcast the same thing recently.”
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NSDI, Mar 2004 9
Solution: Trickle
• “Every once in a while, broadcast what data you have, unless you’ve heard some other nodes broadcast the same thing recently.”
• Behavior (simulation and deployment): – Maintenance: a few sends per hour – Propagation: less than a minute – Scalability: thousand-fold density changes
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NSDI, Mar 2004 10
Solution: Trickle
• “Every once in a while, broadcast what data you have, unless you’ve heard some other nodes broadcast the same thing recently.”
• Behavior (simulation and deployment): – Maintenance: a few sends per hour – Propagation: less than a minute – Scalability: thousand-fold density changes
• Instead of flooding a network, establish a trickle of packets, just enough to stay up to date.
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NSDI, Mar 2004 11
Outline
• Data dissemination
• Trickle algorithm
• Experimental methodology
• Maintenance
• Propagation
• Conclusion
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NSDI, Mar 2004 12
Trickle Assumptions
• Broadcast medium
• Concise, comparable metadata – Given A and B, know if one needs an update
• Metadata exchange (maintenance) is the significant cost
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NSDI, Mar 2004 13
Detecting That a Node Needs �an Update
• As long as each node communicates with others, inconsistencies will be found
• Either reception or transmission is sufficient
• Define a desired detection latency, τ
• Choose a redundancy constant k – k = (receptions + transmissions)
– In an interval of length τ
• Trickle keeps the rate as close to k/ τ as possible
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NSDI, Mar 2004 14
Trickle Algorithm
• Time interval of length τ
• Redundancy constant k (e.g., 1, 2)
• Maintain a counter c
• Pick a time t from [0, τ]
• At time t, transmit metadata if c < k • Increment c when you hear identical metadata to your own
• Transmit updates when you hear older metadata
• At end of τ, pick a new t
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NSDI, Mar 2004 15
Example Trickle Execution
time τ
B
C
transmission suppressed transmission reception
A
k=1 c
0
0
0
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NSDI, Mar 2004 16
Example Trickle Execution
tA1
time τ
B
C
transmission suppressed transmission reception
A
k=1 c
0
0
0
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NSDI, Mar 2004 17
Example Trickle Execution
tA1
time τ
B
C
transmission suppressed transmission reception
A
k=1 c
0
1
0
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NSDI, Mar 2004 18
Example Trickle Execution
tA1
time τ tC1
B
C
transmission suppressed transmission reception
A
k=1 c
0
1
0
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NSDI, Mar 2004 19
Example Trickle Execution
tA1
time τ tC1
B
C
transmission suppressed transmission reception
A
k=1 c
0
2
0
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NSDI, Mar 2004 20
Example Trickle Execution
tA1
time
tB1
τ tC1
B
C
transmission suppressed transmission reception
A
k=1 c
0
2
0
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NSDI, Mar 2004 21
Example Trickle Execution
tA1
time
tB1
τ tC1
B
C
transmission suppressed transmission reception
A
k=1 c
0
0
0
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NSDI, Mar 2004 22
Example Trickle Execution
tA1
time
tB1 tB2
τ tC1
B
C
transmission suppressed transmission reception
A
k=1 c
1
0
1
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NSDI, Mar 2004 23
Example Trickle Execution
tA1
time
tB1 tB2
τ tC1 tC2
B
C
transmission suppressed transmission reception
A
k=1 c
1
0
1
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NSDI, Mar 2004 24
Example Trickle Execution
tA1 tA2
time
tB1 tB2
τ tC1 tC2
B
C
transmission suppressed transmission reception
A
k=1 c
1
0
1
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NSDI, Mar 2004 25
Outline
• Data dissemination
• Trickle algorithm
• Experimental methodology
• Maintenance
• Propagation
• Future Work
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NSDI, Mar 2004 26
Experimental Methodology
• High-level, algorithmic simulator – Single-hop network with a uniform loss rate
• TOSSIM, simulates TinyOS implementations – Multi-hop networks with empirically derived loss rates
• Real world deployment in an indoor setting
• In experiments (unless said otherwise), k =1
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NSDI, Mar 2004 27
Outline
• Data dissemination
• Trickle algorithm
• Experimental methodology
• Maintenance
• Propagation
• Future Work
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NSDI, Mar 2004 28
Maintenance Evaluation
• Start with idealized assumptions, relax each – Lossless cell – Perfect interval synchronization – Single hop network
• Ideal: Lossless, synchronized single hop network – k transmissions per interval – First k nodes to transmit suppress all others – Communication rate is independent of density
• First step: introducing loss
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NSDI, Mar 2004 29
Loss�(algorithmic simulator)
0
2
4
6
8
10
12
1 2 4 8 16 32 64 128 256
Motes
Transmissions/Interval
60%40%20%0%
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NSDI, Mar 2004 30
Logarithmic Behavior of Loss
• Transmission increase is due to the probability that one node has not heard n transmissions
• Example: 10% loss – 1 in 10 nodes will not hear one transmission – 1 in 100 nodes will not hear two transmissions – 1 in 1000 nodes will not hear three, etc.
• Fundamental bound to maintaining a per-node communication rate
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NSDI, Mar 2004 31
Synchronization�(algorithmic simulator)
0
2
4
6
8
10
12
14
1 2 4 8 16 32 63 128 256
Motes
Transmissions/Interval
Not SynchronizedSynchronized
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NSDI, Mar 2004 32
Short Listen Effect
• Lack of synchronization leads to the “short listen effect”
• For example, B transmits three times:
A B C D
Time
τ
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NSDI, Mar 2004 33
Short Listen Effect Prevention
• Add a listening period: t from [0.5τ, τ]
Listen-only period
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NSDI, Mar 2004 34
Effect of Listen Period�(algorithmic simulator)
0
2
4
6
8
10
12
14
1 2 4 8 16 32 63 128 256
Motes
Transmissions/Interval
Not SynchronizedSynchronizedListening
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NSDI, Mar 2004 35
Multihop Network�(TOSSIM)
• Redundancy:
• Nodes uniformly distributed in 50’x50’ area
• Logarithmic scaling holds Redundancy over Density in TOSSIM
0
0.5
1
1.5
2
2.5
3
3.5
4
1 2 4 8 16 32 64 128 256 512 1024
Motes
Red
un
dan
cy
No CollisionsCollisions
(transmissions + receptions) intervals
- k
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NSDI, Mar 2004 36
Empirical Validation�(TOSSIM and deployment)
• 1-64 motes on a table, low transmit power
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NSDI, Mar 2004 37
Maintenance Overview
• Trickle maintains a per-node communication rate
• Scales logarithmically with density, to meet the per-node rate for the worst case node
• Communication rate is really a number of transmissions over space
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NSDI, Mar 2004 38
Outline
• Data dissemination
• Trickle algorithm
• Experimental methodology
• Maintenance
• Propagation
• Future Work
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NSDI, Mar 2004 39
Interval Size Tradeoff
• Large interval τ – Lower transmission rate (lower maintenance cost) – Higher latency to discovery (slower propagation)
• Small interval τ – Higher transmission rate (higher maintenace cost) – Lower latency to discovery (faster propagation)
• Examples (k=1) – At τ = 10 seconds: 6 transmits/min, discovery of 5 sec/hop – At τ = 1 hour: 1 transmit/hour, discovery of 30 min/hop
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NSDI, Mar 2004 40
Speeding Propagation
• Adjust τ: τl, τh
• When τ expires, double τ up to τh
• When you hear newer metadata, set τ to τl
• When you hear newer data, set τ to τl
• When you hear older metadata, send data
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NSDI, Mar 2004 41
Simulated Propagation
Time
Time To Reprogram, Tau, 10 Foot Spacing (seconds)
18-2016-1814-1612-1410-128-106-84-62-40-2
• New data (20 bytes) at lower leA corner
• 16 hop network
• Time to reception in seconds
• Set τl = 1 sec
• Set τh = 1 min
• 20s for 16 hops
• Wave of activity
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NSDI, Mar 2004 42
Empirical Propagation
• Deployed 19 nodes in office setting
• Instrumented nodes for accurate installation times
• 40 test runs
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NSDI, Mar 2004 43
Network Layout�(about 4 hops)
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NSDI, Mar 2004 44
Mote Propagation Distribution
0%
5%
10%
15%
20%
25%
30%
35%
0 5 10 15 20 25 30 35 40 45+
Time (seconds)
Empirical Results
k=1, τl=1 second, τh=1 minute
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NSDI, Mar 2004 45
Mote Propagation Distribution
0%
5%
10%
15%
20%
25%
30%
35%
0 5 10 15 20 25 30 35 40 45+
Time (seconds)
Empirical Results
k=1, τl=1 second, τh=1 minute
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NSDI, Mar 2004 46
Network Layout�(about 4 hops)
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NSDI, Mar 2004 47
Network Layout�(about 4 hops)
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• A single, lossy link can cause a few stragglers
Mote Propagation Distribution
0%
5%
10%
15%
20%
25%
30%
35%
0 5 10 15 20 25 30 35 40 45+
Time (seconds)
Empirical Results
k=1, τl=1 second, τh=1 minute
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Mote Distribution, τh=20m, k=1
0%
5%
10%
15%
20%
25%
30%
0 5 10 15 20 25 30 35 40 45+
Time (seconds)
Mote Distribution, τh=20m, k=2
0%
5%
10%
15%
20%
25%
30%
0 5 10 15 20 25 30 35 40 45+
Time (seconds)
Changing th to 20 minutes
• Reducing maintenance twenty-fold degrades propagation rate slightly
• Increasing redundancy ameliorates this
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Outline
• Data dissemination
• Trickle algorithm
• Experimental methodology
• Maintenance
• Propagation
• Future Work and Conclusion
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Extended and Future Work
• Further examination of τl, τh and k needed
• Reducing idle listening cost
• Interaction between routing and dissemination – Dissemination must be slow to avoid the broadcast storm – Routing can be fast
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Conclusions
• Trickle scales logarithmically with density
• Can obtain rapid propagation with low maintenance – In example deployment, maintenance of a few sends/hour,
propagation of 30 seconds
• Controls a transmission rate over space – Coupling between network and the physical world
• Trickle is a nameless protocol – Uses wireless connectivity as an implicit naming scheme – No name management, neighbor lists… – Stateless operation (well, eleven bytes)
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Questions
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Sensor Network Behavior
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Energy Conservation
• Snooping can limit energy conservation
• Operate over a logical time broken into many periods of physical time (duty cycling)
• Low transmission rates can exploit the transmit/receive energy tradeoff
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Use an Epidemic Algorithm?
• Epidemics can scalably disseminate data
• But end to end connectivity is the primitive (IP) – Overlays, DHTs, etc.
• Sensor nets have a local wireless broadcast
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Use a Broadcast?
• Density-aware operation (e.g., pbcast) – Avoid the broadcast storm problem
• Broadcasting is a discrete phenomenon – Imposes a static reachable node set
• Loss, disconnection and repopulation
• We could periodically rebroadcast… – When to stop?
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Rate Change Illustration
τh
Time
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Rate Change Illustration
τh
Hear Newer Metadata
Time
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Rate Change Illustration
τh τl
Hear Newer Metadata
Time
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Rate Change Illustration
τh τl
2τl
Hear Newer Metadata
Time
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Rate Change Illustration
τh τl
2τl
τh τh
Hear Newer Metadata
2 Time