© Egemen Çetinkaya ITTC Wireless Sensor Networks Understanding the Performance Metrics © 2006 Egemen K. Çetinkaya 22 December 2006 Egemen Çetinkaya Presentation for the ResiliNets Group Based on EECS 881 Presentation

© Egemen Çetinkaya ITTC 22 December 2006WSN2 Wireless 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 Çetinkaya ITTC 22 December 2006WSN3 WSNs Overview of WSNs Overview of WSNs WSN applications WSN components, platforms & comparison QoS metrics for WSNs Network Processors for WSNs RARS Conclusions

© Egemen Çetinkaya ITTC 22 December 2006WSN4 Overview of WSNs Introduction, 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

© Egemen Çetinkaya ITTC 22 December 2006WSN5 WSNs WSN Applications Overview of WSNs WSN applications WSN components, platforms & comparison QoS metrics for WSNs Network Processors for WSNs RARS Conclusions

© Egemen Çetinkaya ITTC 22 December 2006WSN6 WSNs WSN Applications from from from from * from research/exploratory/ wireless_sensors.htm #industrial research/exploratory/ wireless_sensors.htm #industrial

© Egemen Çetinkaya ITTC 22 December 2006WSN7 WSNs WSN Platforms & Comparison Overview of WSNs WSN applications WSN components, platforms & comparison QoS metrics for WSNs Network Processors for WSNs RARS Conclusions

© Egemen Çetinkaya ITTC 22 December 2006WSN8 WSNs WSN Components A typical sensor node is composed of: –Computing module –Communication module –Sensing module –Power module

© Egemen Çetinkaya ITTC 22 December 2006WSN9 WSNs WSN Platforms AppsμCμCMemoryRadioData Rate Tx. Power Mica2. Temp, Wearable comput. ATmega 128L 128K F 4K SRAM Compliant 38.4 kbps -20 to 10 dBm Tmote Sky Hum, light TI MSP430 48K F 10K RAM Compliant 250 kbps -3 to 0 dBm EM 250 Building home aut. XAP2b128K F 5K RAM Compliant 250 kbps -32 to 5 dBm Imote SN research ARM core512K F 64K SRAM Bluetooth+ 500 kbps up to 4 dBm

© Egemen Çetinkaya ITTC 22 December 2006WSN10 WSNs Comparison 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

© Egemen Çetinkaya ITTC 22 December 2006WSN11 WSNs QoS Metrics for WSNs Overview of WSNs WSN applications WSN components, platforms & comparison QoS metrics for WSNs Network Processors for WSNs RARS Conclusions

© Egemen Çetinkaya ITTC 22 December 2006WSN12 WSNs QoS Metrics for WSNs Application –Optimum number of sensor nodes in the field –Measurement accuracy –Coverage area Network –Collective BW –Collective latency –Collective packet loss

© Egemen Çetinkaya ITTC 22 December 2006WSN13 WSNs Network Processors for WSNs Overview of WSNs WSN applications WSN components, platforms & comparison QoS metrics for WSNs Network Processors for WSNs RARS Conclusions

© Egemen Çetinkaya ITTC 22 December 2006WSN14 WSNs Network Processors for WSNs FeaturesEnergy 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, , memory partitioning 100 µW (Michigan) Subthreshold voltage usage, RISC/CISC, designed for intraocular pressure monitoring 1.4 pJ/ins fJ/ins

© Egemen Çetinkaya ITTC 22 December 2006WSN15 WSNs Smart Dust Node/Processor Figure * The above figure is taken from the Smart-Dust paper [5], in the reference section

© Egemen Çetinkaya ITTC 22 December 2006WSN16 WSNs Reliability, Availability, Resiliency, Survivability Overview of WSNs WSN applications WSN components, platforms & comparison QoS metrics for WSNs Network Processors for WSNs RARS Conclusions

© Egemen Çetinkaya ITTC 22 December 2006WSN17 WSNs Reliability 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

© Egemen Çetinkaya ITTC 22 December 2006WSN18 WSNs Availability 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

© Egemen Çetinkaya ITTC 22 December 2006WSN19 WSNs Resiliency & 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

© Egemen Çetinkaya ITTC 22 December 2006WSN20 WSNs Survivability 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

© Egemen Çetinkaya ITTC 22 December 2006WSN21 WSNs Survivability 2 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

© Egemen Çetinkaya ITTC 22 December 2006WSN22 WSNs Conclusions Overview of WSNs WSN applications WSN components, platforms & comparison QoS metrics for WSNs Network Processors for WSNs RARS Conclusions

© Egemen Çetinkaya ITTC 22 December 2006WSN23 WSNs Conclusions 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

© Egemen Çetinkaya ITTC 22 December 2006WSN24 Acknowledgements Thanks to Dr. James Sterbenz for helpful comments

© Egemen Çetinkaya ITTC 22 December 2006WSN25 References novation2.htmlhttp://domino.watson.ibm.com/comm/research.nsf/pages/r.communications.in novation2.html 5.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 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, James P.G. Sterbenz & David Hutchison, ResiliNets