collection tree protocol presentation
TRANSCRIPT
Collection Tree Protocol
References :- a. Omprakash Gnawali, Rodrigo Fonseca, Kyle Jamieson, David Moss, and Philip Levis, “Collection Tree Protocol”, In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (SenSys 2009), Berkeley, CA, USA, November 2009
b. Ugo Colesanti, Silvia Santini, ”The Collection Tree Protocol for the Castalia Wireless Sensor Networks Simulator”, Technical Report No. 729, Department of Computer Science, ETH Zurich, Zurich, Switzerland, June 2011
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Collection
• Anycast route to the sink(s)– Used to collect data from the network
to a small number of sinks (roots, base stations)
– Network primitive for other protocols• A distance vector protocol
sink
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Desired
• Reliability – delivery ratio 90% - 99.99%• Robustness – no tuning required• Efficiency – mimimum # of packets• Hardware independence – without assuming
specific radio chip features.
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Common causes of poor performance
• Link Dynamics– Fast more agile network, but costly– Slow slower-to-adapt network, but cheap
• Transient Loops– topology repairs happen at the timescale of
control plane maintenance
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Common Architecture
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Router
ForwarderLink Estimator
Link Layer
Application
Control Plane Data Plane
FwdTable
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Prior Work
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Link Layer
Control Plane Data Plane
ETX, MT, MultiHopLQI, EAR, LOF, AODV, DSR, BGP, RIP, OSPF, Babel
Flush, RMST, CODA, Fusion,
IFRC, RCRT
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Wireless Link Dynamics
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0.9
1s
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Control and Data Rate Mismatch
• Can lead to poor performance
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Link Layer
Control Plane Data Plane
10 pkt/s1 beacon/30s 0 pkt/s1 beacon/s
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CTP
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Router
ForwarderLink Estimator
Link Layer
Application
Control Plane Data Plane
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CTP Approach
• Enable control and data plane interaction
• Two mechanisms for efficient and agile topology maintenance– Datapath validation– Adaptive beaconing
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ControlPlane
DataPlane
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Additional mechanisms
• Re-transmit timers• Hybrid queue• Per Client queueing• Transmit cache for duplicate suppression
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Summary of Results
• 90-99.9% delivery ratio– Testbeds, configurations, link layers
• Compared to MultihopLQI– 29% lower data delivery cost– 73% fewer routing beacons– 99.8% lower loop detection latency
• Robust against disruption• Cause for packet loss vary across testbeds
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Outline• Collection• Datapath validation• Adaptive beacons• Re-transmit timers• Hybrid queue• Per-client queueing• Transmit cache• Evaluation• Conclusion
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Datapath validation
• Use data packets to validate the topology– Inconsistencies– Loops
• Receiver checks for consistency on each hop– Transmitter’s cost is in the header
• Same time-scale as data packets– Validate only when necessary
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Routing Loops
– Cost does not decrease
D A
B
8.1
4.6
6.3
3.2
5.8
XC
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Routing Consistency
• Next hop should be closer to the destination• Maintain this consistency criteria on a path
• Inconsistency due to stale state
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ni ni+1 nk
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Detecting Routing Loops• Datapath validation
– Cost in the packet– Receiver checks
• Inconsistency– Larger cost than
on the packet• On Inconsistency
– Don’t drop the packets– Signal the control plane
D A
B
C 8.1
4.6
6.3
3.2
5.8
X
4.6
6.3
8.1
5.8
4.6 < 6.3?
3.2 < 4.6?
5.8 < 8.1?
4.6<5.8?
4.6
8.1 < 4.6?
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Outline
• Collection• Datapath validation• Adaptive beacons• Re-transmit timers• Hybrid queue• Per-client queueing• Transmit cache• Evaluations• Conclusion
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How Fast to Send Beacons?
• Using a fixed rate beacon interval– Can be too fast– Can be too slow– Agility-efficiency tradeoff
• Agile+Efficient possible?
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Routing as Consistency
• Routing as a consistency problem– Costs along a path must be consistent
• Use consistency protocol in routing– Leverage research on consistency protocols– Trickle
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Trickle
• Detecting inconsistency– Code propagation: Version number mismatch– Does not work for routing: use path consistency
• Control propagation rate– Start with a small interval– Double the interval up to some max– Reset to the small interval when inconsistent
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Control Traffic Timing
• Extend Trickle to time routing beacons• Reset the interval
• ETX(receiver) >= ETX(sender) • Significant decrease in gradient• “Pull” bit
TX
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Adaptive Beacon Timing
Infrequent beacons in the long run
~ 8 min
Tutornet
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Adaptive vs Periodic Beacons
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Time (mins)
Less overhead compared to 30s-periodic
1.87beacon/s
0.65beacon/s
Tutornet
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Node Discovery
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Time (mins)
A new node introduced
Efficient and agile at the same time
Path established in < 1s
Tutornet
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Outline
• Collection• Datapath validation• Adaptive beacons• Re-transmit timers• Hybrid queue• Per-client queueing• Transmit cache• Evaluation• Conclusion
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CTP Routing Frame
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CTP Data Frame
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Additional mechanisms• Transmit timers
– Prevent self-interference– Depends upon the packet rate
• Per client queueing– Prevents isolation– One-deep packet queue
• Hybrid send queue– Route-through & locally generated traffic– C + F length
• Transmit cache– Origin address, origin sequence number & THL of N most recently
forwarded packets
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Experiments
• 12 testbeds• 20-310 nodes• 7 hardware
platforms• 4 radio
technologies• 6 link layers
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Variations in hardware, software, RF environment, and topology
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Reliable, Efficient, and Robust
Testbed Delivery RatioWymanpark 0.9999Vinelab 0.9999Tutornet 0.9999NetEye 0.9999Kansei 0.9998Mirage-MicaZ 0.9998Quanto 0.9995Blaze 0.9990Twist-Tmote 0.9929Mirage-Mica2dot 0.9895Twist-eyesIFXv2 0.9836Motelab 0.9607
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High end-to-end delivery ratio(but not on all the testbeds!)
Retransmit
False ack
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Reliable, Efficient, and Robust
High delivery ratio across time(short experiments can be misleading!)
Tutornet
0.98
5 10 15 20 25 30 35Time (hrs)
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Reliable, Efficient, and Robust
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Low data and control cost
Tutornet CTP Noe
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Reliable, Efficient, and Robust
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0
0.2
0.4
0.6
0.8
1
CSMA BoX-1s LPP-500ms
Link Layer
Low duty-cycle with low-power MACs
0.028 0.066
Motelab, 1pkt/5min
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Reliable, Efficient, and Robust
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Time (mins)
10 out of 56 nodesremoved at t=60 mins
No disruption in packet delivery
Tutornet
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Nodes reboot every 5 mins
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Reliable, Efficient, and Robust
Delivery Ratio > 0.99
Routing Beacons
High delivery ratio despite serious network-wide disruption(most loss due to reboot while buffering packet)
~ 5 min
Tutornet
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CTP Performance Summary
• Reliability– Delivery ratio > 90% in all cases
• Efficiency– Low cost and 5% duty cycle
• Robustness– Functional despite network disruptions
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CTP on Castalia
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LE header & footer
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Conclusion
• Wireless routing benefits from data and control plane interaction
• Lessons applicable to distance vector routing– Datapath validation & adaptive beaconing
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