© Egemen Çetinkaya ITTC Wireless Sensor Networks Understanding the Performance Metrics © 2006...

© Egemen ÇetinkayaITTCWireless Sensor Networks

Understanding the Performance Metrics

© 2006 Egemen K. Çetinkaya22 December 2006

Egemen Çetinkaya

Presentation for the ResiliNets Group

Based on EECS 881 Presentation

[email protected]

http://wiki.ittc.ku.edu/resilinets_wiki/index.php/Main_Page

22 December 2006 WSN 2

© Egemen ÇetinkayaITTCWireless Sensor Networks

(WSNs)Outline

• Overview of WSNs• WSN applications• WSN components, platforms & comparison• QoS metrics for WSNs• Network Processors for WSNs• Reliability,Availability,Resiliency,Survivability

(RARS) • Conclusions


© Egemen ÇetinkayaITTC

WSNsOverview of WSNs

• Overview of WSNs• WSN applications• WSN components, platforms & comparison• QoS metrics for WSNs• Network Processors for WSNs• RARS• Conclusions



Overview of WSNsIntroduction, History & Motivation

• WSNs are special type of network:– Wireless, distributed, multihop, small size, energy

constraint

• History:– Early efforts 1978– 1980s-1990s military centric research– 1990s, explosion in the research efforts– 2000s, small size sensors, NPs

• Motivation:– One of the 10 emerging technology that will change

the near future, by Technology Review



WSNsWSN Applications




WSNsWSN Applications

from http://dtsn.darpa.mil/ixo/programs.asp?id=60

from http://domino.watson.ibm.com/comm/research.nsf/pages/r.communications.innovation2.html

* from http://www.intel.com/research/exploratory/wireless_sensors.htm#industrial

from http://www.eecs.harvard.edu/~mdw/proj/codeblue/

* from http://www.intel.com/research/exploratory/wireless_sensors.htm#industrial



WSNsWSN Platforms & Comparison




WSNsWSN Components

• A typical sensor node is composed of:– Computing module– Communication module– Sensing module– Power module

Sensing Unit&

ADC

Microprocessor/controller

&Memory

CommunicationModule

Power Module



WSNsWSN Platforms

Apps μC Memory

Radio DataRate

Tx.Power

Mica2. Temp,Wearable comput.

ATmega128L

128K F4K SRAM

802.15.4 Compliant

38.4kbps

-20 to 10 dBm

TmoteSky

Hum, light

TI MSP430

48K F10K RAM

802.15.4 Compliant

250kbps

-3 to 0 dBm

EM 250

Building home aut.

XAP2b 128K F5K RAM

802.15.4 Compliant

250 kbps

-32 to 5 dBm

Imote SNresearch

ARM core

512K F64K SRAM

Bluetooth

+ 500kbps

up to 4 dBm



WSNsComparison of The Platforms

• Microcontroller/Microprocessor– Clock frequency– Memory size

• Radio– Tx. power– Data rate

• Measurement accuracy of the sensing module• No cost comparison• Applications differ, i.e. hard to use the above

metrics



WSNsQoS Metrics for WSNs




WSNsQoS Metrics for WSNs

• Application– Optimum number of sensor nodes in the field– Measurement accuracy– Coverage area

• Network– Collective BW– Collective latency– Collective packet loss



WSNsNetwork Processors for WSNs




WSNsNetwork Processors for WSNs

Features Energy Dissipation

SNAP/LE (Cornell)

Async., no clock, no OS, event-driven, no overhead compared to TinyOS

24 pJ/ins

Smart Dust (Berkeley)

16mm3 size, solar powered, (FSOC)

12 pJ/ins

(Harvard) Interrupts called events, event-driven, 802.15.4, memory partitioning

100 µW

(Michigan) Subthreshold voltage usage, RISC/CISC, designed for intraocular pressure monitoring

1.4 pJ/ins - 600 fJ/ins



WSNsSmart Dust Node/Processor Figure

* The above figure is taken from the Smart-Dust paper [5], in the reference section


© Egemen ÇetinkayaITTCWSNs

Reliability, Availability, Resiliency, Survivability




WSNsReliability

• In reliability theory:– Reliability is the probability of a component or a

system under certain conditions and predefined time, to perform its required task

– Quantitatively:

• Required indices:– MTTF=1/ λ ; where λ is failure rate– MTTR=1/μ ; where μ is repair rate– MTBF=MTTF+MTTR



WSNsAvailability

• In reliability theory:– Availability is the probability of finding the component

or system in the operating state at some time in the future

– Quantitatively:

• No WSN component/platform reliability/availability numbers were found

• No definition specific for WSNs are found• Hard to compare since applications are different



WSNsResiliency & Survivability

• Resilience:– “Resilience is the ability of the network to provide

and maintain an acceptable level of service in the face of various challenges to normal operation”

• Survivability:– “The capability of a system to fulfill its mission, in a

timely manner, in the presence of threats such as attacks or large-scale natural disasters. Survivability is a subset of resilience”

* The above definitions are taken from Resilinets web page at http://wiki.ittc.ku.edu/resilinets_wiki/index.php/Main_Page



WSNsSurvivability

• Without defining Survivability for WSNs, can we say?

– Increasing life time of a sensor node (e.g. several tens of years instead of months) via novel hardware design techniques (e.g. partitioned memory, exploiting subthreshold voltage levels)

– Added redundancy & diversity at the component design level (e.g. wireless and FSOC communication)

– Novel design of algorithms to find the alternate routes from nodes to the sink in the case of failed routes



WSNsSurvivability2

• Without defining Survivability for WSNs, can we say?– Novel software design approaches (e.g. event-

driven) that reduces energy dissipation, thus increased life time of the WSN

– Security mechanisms against attacks to increase survivability of the WSN

– Redundant data in the WSN systems can be used to improve the reliability, availability, resiliency and survivability



WSNsConclusions




WSNsConclusions

• Data rate vs. Power in energy constraint WSNs

• Life time of sensor platforms should be considered as a performance measure for WSNs, due to limited resource nature of WSNs

• Network processors for WSNs are made possible

• Lack & difficultness of establishing performance metrics



Acknowledgements

• Thanks to Dr. James Sterbenz for helpful comments



References

1. http://dtsn.darpa.mil/ixo/programs.asp?id=602. http://www.intel.com/research/exploratory/

wireless_sensors.htm#industrial3. http://www.eecs.harvard.edu/~mdw/proj/codeblue/4. http://domino.watson.ibm.com/comm/research.nsf/pages/

r.communications.innovation2.html5. B. A. Warneke and K. S. Pister. An ultra-low energy microcontroller for

smart dust wireless sensor networks. In Proceedings of the IEEE International Solid-State Circuits conference on (ISSCC 2004), San Francisco, CA, USA 2004 pp 316-317

6. Chen, D. and Varshney, P.K. "QoS Support in Wireless Sensor Networks: A Survey," In Proceedings of the International Conference on Wireless Networks (ICWN 2004), Las Vegas, Nevada, USA, June 21-24, 2004.

7. James P.G. Sterbenz & David Hutchison, ResiliNets http://wiki.ittc.ku.edu/resilinets_wiki/index.php/Main_Page

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