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Topology ControlTopology ControlPresenter: Ajit WarrierPresenter: Ajit Warrier
With
Dr. Sangjoon Park (ETRI, South Korea),
Jeongki Min and
Dr. Injong Rhee (advisor)
North Carolina State University Networking Lab
http://netsrv.csc.ncsu.edu
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Introduction: Topology Control
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Topology Control/Clustering
■ Reduce structural complexity in a network.
■ Delegate complex/energy consuming activities to a subset of nodes in the network.
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Topology Control ApproachesPower Control
• Most often used in wireless ad-hoc networks.• Reduce routing complexity.• Reduce wireless interference.• Preserve network capacity ? Connectivity ?
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Topology Control ApproachesConnected Backbone
A
B
• Most often used in wireless ad-hoc networks.• Reduce routing complexity.• Reduce wireless interference.• Preserve network capacity ?
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Topology Control ApproachesClustering/Hierarchy
• Most often used in wireless sensor networks.• Reducing complexity not the issue, radio power consumption is !• Reduce radio transmissions/energy consumption.• Do not care (as much) about capacity.
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Topology Control – Pros/Cons
Pros■ Energy Efficient – Radio draws order of magnitude more energy than the sensing board.
■ Less radio interference.
■ Less routing complexity.
Cons■ Loss of routing selectivity.
■ Topology maintenance overhead.
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Motivation
Lots of theory/simulation – very few experimental results.
■ Complicated algorithms.
■ Assumptions in the algorithm difficult to realize in practice:
■ Wireless links usually vary in quality over time.
■ Wireless links not binary in nature.
■ Wireless links may be asymmetric.
■ Sensor nodes have low speed CPUs, may not be possible to run complex algorithms.
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barrier
Mica2 nodes
Mica2Dot nodes
observerG3
G2
G1
HEED experimental testbed FLOC experimental testbed
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Algorithm and Analysis
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Our Topology Control Algorithm - Overview■ Divide the sensor network into approximately equal regions called clusters.
■ Cluster Members Every node belongs to one cluster. Perform sensing, if an event occurs, transmit event to cluster
head.
■ Cluster Head Within radio range of all nodes of a cluster. Responsible for two activities:
Collect sensing reports from members. Route/forward sensing reports toward the sink.
■ Gateways Member nodes acting as connecting link between two clusters.
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Algorithm - Overview
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Cluster Head Election Algorithm
Time-line of a node, in rounds
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Cluster Head Election Algorithm
Flip coin with probability p
0
Time-line of a node, in rounds
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Cluster Head Election Algorithm
Flip coin with probability p
0
Time-line of a node, in rounds
Lose
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Cluster Head Election Algorithm
Flip coin with probability p
0
Flip coin with probability kp
0
Time-line of a node, in rounds
Lose
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Cluster Head Election Algorithm
Flip coin with probability p
0
Flip coin with probability kp
0
Time-line of a node, in rounds
Lose Lose
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Cluster Head Election Algorithm
Flip coin with probability p
0
Flip coin with probability kp
0
Flip coin with probability k2p
0
Time-line of a node, in rounds
Lose Lose
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Cluster Head Election Algorithm
Flip coin with probability p
0
Flip coin with probability kp
0
Flip coin with probability k2p
0
Time-line of a node, in rounds
Lose Lose Win – Become Cluster Head
Transmit Cluster Head Announcement (CHA)
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Cluster Head Election Algorithm
Flip coin with probability p
0
Time-line of a node, in rounds
Lose
Receive CHA – Become Member Node
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Cluster Head Selection
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Gateway Selection
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Routing Phase
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Data Transmission – Differential Duty Cycling
• Cluster heads, gateways responsible for routing/data forwarding => set radio to high duty cycle.
• Member nodes only responsible for sensing => set radio to low duty cycle (ideally to 0%).
• Ratio of duty cycle of member nodes to that of cluster heads/gateway nodes decides energy efficiency of network.
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Analysis Result – Energy Saving
Ratio
Ratio
Ratio
Ratio Ratio
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Topology Control Operations
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Experimental Results
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Experimental Platform
Platform:
• Motes (UC Berkeley)
• 8-bit CPU at 4MHz
• 128KB flash, 4KB RAM
• 916MHz radio
• TinyOS event-driven
The algorithm has been implemented on Mica2 sensor nodes running the TinyOS event-driven operating system.
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Experimental Testbed
■ 42 Mica2 sensor motes in Withers Lab.
■ Wall-powered and connected to the Internet via Ethernet ports.
■ Programs uploaded via the Internet, all mote interaction via wireless.
■ Links vary in quality, some have loss rates up to 30-40%.
■ Asymmetric links also present.
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Experimental Testbed – Connectivity
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Experimental Testbed – Snapshot
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Implementation Details
■ MAC Layer – B-MACCSMA-based.Duty Cycled.
■ Routing Layer – MintDSDV-like table driven, proactiveUses link level measurements to select routing parents.
■ Member nodes switch off their radio. (δ = 0)
■ Cluster heads tested with varying duty cycles (X = 2% - 45%)
■ Radio is 19.2 Kbps, packet payload of 36 bytes.
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Experimental Method
■ Every node transmits packets with probability α% per second.
■ α varied for two types of scenariosLow Data Rate Experiment
Nodes idle most of the time, brief periods of activity, e.g. Earthquake detection.
α = 0.1 – 1High Data Rate Experiment
Application scenarios with more periodicity, e.g. Temperature monitoring.
α = 10 – 100
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Algorithm Overhead
■ Total energy of 5 J is 0.03% of the total battery capacity.■ Half the time overhead is because of routing.■ Given time synch period of 10s, it is feasible to use a reclustering period of 17 hours.
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Energy Efficiency – Low Data RateTopology Control B-MAC
2% Duty Cycle 5% Duty Cycle 10% Duty Cycle
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Energy Efficiency – High Data RateTopology Control B-MAC
2% Duty Cycle 5% Duty Cycle 10% Duty Cycle
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Throughput
B-MAC
Topology Control Topology
Control
B-MAC
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Conclusion and Future Work
■ As a thumb rule, topology control can extend network lifetime by the network density divided by 4-8.
■ Topology control is not necessarily capacity conserving, may result in up to 50% loss in throughput. This is due to reduced routing selectivity.
■ Given the mathematical analysis, one may attempt to optimize the algorithm for some system performance metric, for instance throughput.
■ Need to develop robust algorithms for node failure resolution.