Energy Efficient Routing

LAMAR UNIVERISTY

Computer Science Department

By Rui Luo

Supervisor: Dr. Lawrence J. Osborne

Committee Member: Dr. Chung-Chih Li

Committee Member: Dr. Bo Sun

Fall 2005

Agenda Background

Sensor, WSNs, TinyOS, Applications Current Routing Protocols In WSNs

Reviews, Compare Algorithm Design

Target Model, Principle, Algorithm Implementation

Platform, Issues, Architecture Testing

Methodology, Simulator, Chat Conclusion and Future Work

Sensor and WSNs Poor powered

Battery, Ambient Energy (solar cell) Constrained computing resource

Memory space CPU

Limited Communication abilities Low bandwidth Transmission range

Agenda

Applications Habitat Monitoring

(Event-driven, Randomized Deployment) Environment Observation and Forecasting

(EOFS) (Time-driven, Query-driven)

Health Applications (Event-driven, Time-driven)

Structure Health Monitoring (SHM) (Deterministic Deployment)

Home, Office Applications, and OtherAgenda

WSNs vs. MANETs

WSNs

MANETs

Application Dependent

Routing is known

Low Bandwidth

Weak Nodes

Usually Stationary

Powerful Nodes, High Bandwidth, Application Independent, Routing created on Demand, Nodes come and go

Limited Transmission Range

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WSNs Design Issues Challenges

Global address scheme Data need to be routed to a particular BS Constrained abilities of nodes Stationary & Mobility Application dependent Position awareness Redundancy data

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Tiny OS Component Based

Rapid development, Small size binary Event Driven

Save more energy Multi-Hardware platform

Berkeley/Crossbow mica2: 3rd generation, wireless reprogramming

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Routing Protocol Review By network structure

Flat Network, Hierarchical Networks, Geographic Information Based

By protocol operation Negotiation based, Multi-Path based, QoS

based, Coherent based

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Routing Protocol Comparison Small Minimum Energy Communication

Network (MECN) GPS, has to compute relay region

Our protocol GPS free, no relay region computation

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Target Model Over densed deployment Stationary Nodes send data to the root node Battery changing is reasonable Demand lifetime of the whole network is

much longer than a single life time of each sensor.

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Principle Radio Propagation Model

Near field zone Signal strength is strong, but very short

Free space path loss zone 20 dB/decade, radio travel through the air

Excess path loss zone. 20-50 dB/decade, affected by the ground

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Principle Cont. Friis equation:

GTx = transmitter antenna gain

GRx = receiver antenna gain

λ = wavelength (same units as d) d = distance separating Tx and Rx antennas L = system loss factor (≥ 1)

Ld

GGPP RxTxTxRx 22

2

16

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Principle Cont. Key idea:

To reach the distance two times far away, we need four times transmission power.

Note: In read world, the index is not

constant and usually between 2 to 4.

2dPP RxTx

v

RxTx

dPP

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Principle Cont. Shown by graph Two ways to get D from S

Send to D directly Less transmission delay Use more energy More chance collision

Hop by R More transmission delay Use less energy Less chance collision

S

R

D

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Principle Cont. Most Energy Efficient Region (MEER)

Definition: A region in which a relay node is preferred if total energy is the concerned metric.

Shape: Draw the shape according to the definition in Matlab

Note: Not necessary to be in 2-D space.

}|),{( ),(),( dsdyxyxs EEEyx

MEER

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Algorithm: Principle Cont. Shape of

MEER for attenuation rate index equal to 3

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Algorithm Problem:

Limited transmission range

GPS is expensive. In real world, space is

twisted.

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Protocol Approximate by

considering one hop

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Implementation: Platform Red Hat Linux 9.0 Tiny OS 1.1.0 ncc 1.1.1 (source)

1.1.2 is not compatible with gcc-3.2.2 gcc 3.2.2 IBM-JDK 1.3 for Linux

Tiny OS cannot be installed normally for 1.4 Atemu 0.4 (source)

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Implementation Issues Memory Constrain

Mica2: 4k RAM, 512K ROM Ram usage (byte):

TinyDB: less than 3100, Moté: 849 Our program: about 1k ram, most of them are used for outgoing

buffer

Transmission Power Network density is controllable Max=150, reaches 100m in Atemu PM start=1, increment=10

Agenda

Implementation Issues Cont. Packet loss

Causes: exposed node, Outgoing buffer full Solution

Avoid packet loss by a big buffer in test In real application, the buffer can be small

Dynamic memory management? Advantages: more adaptive, flexible Disadvantages: more CPU load Choose: Static memory allocation

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Implementation Issues Cont. AM message or Low level Comm.?

Goal Make the program easy to be used Allow other protocols run in one application

Choose AM message with direct control to radio mode.

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Implementation: Architecture

EnergyEfficiencyRoutingC

LoggerC

UARTM

SimpleApp

CC1000Control LedsCGenericComm TimerC

Other Tiny OS Components Needed by SimpleApp

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Implementation: Debugging Problem:

Atemu logs the low level events Good for simple program debugging Not good for a distributed protocol debugging

Logger

UART

ATEMU port I/O simulator I/O sniffer

Log File

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Implementation: Log File Sample

000 -- system timer: 0 4 000 <pmack> to 7 006 <pmack> to 1 007 parent ETOR power 0 21 21 001 parent ETOR power 6 32 21 006 <pmack> to 7 007 drop pmack from 6 008 <pm> 31 007 <pm> 31

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Implementation: Log File Analysis Unix text tool

Example: Show final topology #!/bin/bash for i in `cut -b1-3 $1 | sort -u` do grep ^$i $1 | grep parent | tail -n 1 done

Eegraph Will be told later

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Implementation: Auto ID Problem

Node ID is hard coded into programs TOSSIM does substitution automatically Atemu does not.

Solution Modify the source code of Tiny OS Modify the default Makefile A bash script is used to iterate the node id

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Testing: Methodology Simulator

TOSSIM Simple, no high fidelity

Ns2 Classical, not feasible for low level simulation

Atemu: High fidelity, slow, no future support

Avrora New, high fidelity, fast Current not support IBM-JAVA

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Testing: Methodology Cont. Visual tool:

eegraph Functionalities

Visualize the log file

Output data for chart

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Testing: Metrics Topology

Total ETOR Cost for data transmission

Number of the node in the network (N)

Total energy consumption (TEC) Cost for this protocol

N

iiETORETORtotal

1

_

ii ETORnN |

current

t tPTEC0

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Testing: Topology – Starting Power

S=1

S=40

Node 14,2,6 Rough detection of 2,6

Node 15,13,17 Direct reachable to 17

Conclusion Height of the tree Transmission delay

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Testing: Constructing – Starting Po...

Conclusion No obvious relationship between constructing time

and starting power

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Testing: total ETOR-Starting Power

Conclusion Higher starting power results in higher total ETOR

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Testing: TEC – Starting power

Conclusion A higher starting power results in less total energy

consumption. However, this effect is not obvious at the beginning.

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Testing: Topology - Increment Conclusion

Height of the tree Transmission delay

i=10

i=60

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Testing: Constructing – Increment

Conclusion Higher increment results in fast network

constructing.

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Testing: Total ETOR-Increment

Conclusion A higher increment results in higher total ETOR

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Testing: TEC - Increment

Conclusion Higher increment results in lower TEC

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Testing: Topology - Fixed Fixed transmission

power is equal to the view of network density

Topology for different densities

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Testing: Constructing - Fix

Conclusion Higher transmission power results in fast

network construction. Network density is one of the most important

factors.Agenda

Testing: total ETOR - fix

Conclusion Either blue or green is not good Yellow is preferred Network density is one of the most important factors

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Testing: TEC - Fix

Conclusion Higher trans. power results in higher TEC

A modified protocol can be used to stop using energy after the network setting up, hence trans. power has less relationship with TEC.

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Future Work How to use the protocol in a hierarchical

network Adaptive TPC period More testing in real world applications

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Conclusion The algorithm is quite applicable in an

over-densed network. The algorithm can behave differently to

meet the transmission delay metric. Compare with the shortest path algorithm,

the new algorithm saves more energy and introduces more transmission delay.

References Mica2 Mote Datasheet Jamal N. Al-Karaki, Ahmed E. Kamal. ”Routing

Techniques in Wireless Sensor Networks: A Survey” Ning Xu, “A Survey of Sensor Network

Applications” H.T. Friis, “Introduction to radio and radio

antennas” Jonathan Polley, Dionysys Blazakis, Jonathan

McGee, Dan Rusk, John S. Baras, “ATEMU: A Fine-grained Sensor Network Simulator”

V. Rodoplu and T. H. Meng, “Minimum Energy Mobile Wireless Networks"

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Questions?

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