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

Introduction: Topology Control

Topology Control/Clustering

■ Reduce structural complexity in a network.

■ Delegate complex/energy consuming activities to a subset of nodes in the network.

Topology Control ApproachesPower Control

• Most often used in wireless ad-hoc networks.• Reduce routing complexity.• Reduce wireless interference.• Preserve network capacity ? Connectivity ?

Topology Control ApproachesConnected Backbone

A

B

• Most often used in wireless ad-hoc networks.• Reduce routing complexity.• Reduce wireless interference.• Preserve network capacity ?

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.

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.

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.

barrier

Mica2 nodes

Mica2Dot nodes

observerG3

G2

G1

HEED experimental testbed FLOC experimental testbed

Algorithm and Analysis

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.

Algorithm - Overview

Cluster Head Election Algorithm

Time-line of a node, in rounds

Cluster Head Election Algorithm

Flip coin with probability p

0

Time-line of a node, in rounds

Cluster Head Election Algorithm

Flip coin with probability p

0

Time-line of a node, in rounds

Lose

Cluster Head Election Algorithm

Flip coin with probability p

0

Flip coin with probability kp

0

Time-line of a node, in rounds

Lose

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

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

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)

Cluster Head Election Algorithm

Flip coin with probability p

0

Time-line of a node, in rounds

Lose

Receive CHA – Become Member Node

Cluster Head Selection

Gateway Selection

Routing Phase

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.

Analysis Result – Energy Saving

Ratio

Ratio

Ratio

Ratio Ratio

Topology Control Operations

Experimental Results

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.

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.

Experimental Testbed – Connectivity

Experimental Testbed – Snapshot

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.

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

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.

Energy Efficiency – Low Data RateTopology Control B-MAC

2% Duty Cycle 5% Duty Cycle 10% Duty Cycle

Energy Efficiency – High Data RateTopology Control B-MAC

2% Duty Cycle 5% Duty Cycle 10% Duty Cycle

Throughput

B-MAC

Topology Control Topology

Control

B-MAC

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.