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Vana Jeličić, diplg .

ACROSS Colloquium Combined power management methods in wireless networks of energy- hungry sensors. Vana Jeličić, dipl.ing. January 18, 2013. Content. Research area WSNs – distributed event detection Communication energy  Wake-up radio Energy -hungry sensors

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Vana Jeličić, diplg .

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  1. ACROSS ColloquiumCombined power management methodsinwirelessnetworksof energy-hungrysensors Vana Jeličić, dipl.ing. January 18, 2013

  2. Content • Research area • WSNs – distributed event detection • Communication energy  Wake-up radio • Energy-hungry sensors • Video surveillance and smart gas monitoring • Hierarchical, adaptive, event-driven sensing • Motivation and challenges • Problem approach • To-date results • Future research • Activities of AIG

  3. Researcharea • Wireless sensor networks • Wireless sensor node • Energy-efficiency • Communication! • Distributed sensing systems • Event detection, alarm generation • Video surveillance, gas monitoring • Energy-hungry sensors • Power management • Comm. unit (RX, TX, idle state – cca 20 mA!) • Sensing unit

  4. Power management • Duty-cycling (D) • Reducing activity: sensors & radio Maximal reaction time Critical event arrival worst case tactive<< T >> D = tactive/ T LATENCY ENERGY

  5. Eliminating radio idle time • “Classical” WSN problem! • One-channel wake-up radio • Wake-On Radio (WOR) – radio periodically wakes up from sleep modeand listens for incoming packets without MCU interaction. • TI CC1000, CC1101, CC1100E, CC2500, CC430 • MAC: B-MAC, S-MAC, X-MAC... • Optimization • delay– energy trade-off

  6. Wake-upreceiver (WURx) • Two-channel wake-up radio • Ultra-low-power; Continuously monitoring • No idle listening on main radio • Lin, Rabaey and Wolisz; “Power-efficient rendez-vous schemes for dense WSNs”, 2004, <50 uWto outperform one-channel radios! • Trade-offs • wake-up range vs. energy consumption • wake-up range vs. delay (multihops) • in-band vs. out-of-band wake-up radio

  7. WURx prototypes Jelicic et al. AnalyticComparisonofWake-upreceivers for WSNsandBenefitsovertheWake-on Radio Scheme. PM2HW2N 2012.

  8. WURx prototypes (2) • Commercially available (LF, 125 kHz) • Austriamicrosystems • Atmel • Addressingrequired • Addressingnotrequired

  9. WURx applications • ApplicationswithWURx – proposals • Building automation1, 3, 4 • Healthcare2 • No energy vs. latency trade-off! Still notused in WSNs! verypromising! 1) Zhanget al. ImprovingEnergy-Efficiency in Building Automation with Event-Driven Radio. WCSP 2011. 2) Marinkovic et al. Power EfficientNetworking Using a Novel Wake-up Radio. PervasiveHealth 2011. 3) Gammet al. Low Power Wireless Sensor Node for use in building automation. WAMICON 2011. 4) Gammet al. Smart Metering Using Distributed Wake-up Receivers. I2MTC 2012.

  10. Sensing power management • Fixed duty cycle  energy wasting • Adaptive duty cycle • Wake-up latency: ton ≥ twakeup + tacquire • Event-driven • Context-awareness • Energy-awareness

  11. HeterogeneousWSNs for eventdetection • Different sensing modalities • Hierarchy • Applications • Video surveillance: Camera + PIR • Gas monitoring: Gas sensor + PIR • High-consuming sensors

  12. Smart video surveillance • Reducing transmitted data size • Hierarchical, multi-tier, multimodal • Pyroelectric InfraRed (PIR) sensor • Energy-aware decisions

  13. Imagetransmission • Transmission of large amount of data • Only when really necessary • Increasing tactive • ZigBee not intended to that  Stack modificaton 1 • Image fragmentation – maximal frame filling • Disabled MAC acknowledgment  APL layer control • Today – low power WiFi modules • Avoiding transmitting large amounts of data  only event 1) Jelicic et al. Reducing Power Consumption of Image Transmission over IEEE802.15.4/ZigBee Sensor Network. I2MTC2010.

  14. Existing work – multimodal video networks • PIR sensor mounted on the camera board 1, 2, 3 • Same FOV; Dynamically changed sensitivity • Multi-tier Multimodal WSNs 4, 5, 6, 7, 8, 9 1) Magnoet al.A Solar-powered Video Sensor Node for Energy Efficient Multimodal Surveillance.DSD 2008. 2) Magno et al.Adaptive Power Control for Solar HarvestingMultimodal Wireless Smart Camera. ICDSC 2009. 3) Magno et al. Multimodalabandoned/removedobjectdetection for low power video surveillance systems. AVSS 2009. 4) Kulkarniet al.SensEye: A Multi–tier camera sensor network.ACM Multimedia 2005. 5) Prati et al. AnIntegratedMultiModalSensorNetwork for Video Surveillance. VSSN 2005. 6) He et al.Vigilnet: An integrated sensor network system for energy efficient surveillance.ACM Trans. Sen. Netw. 2006. 7) Lopeset al.On the Development of a Multi-tier, Multimodal Wireless Sensor Network for Wild Life Monitoring.IFIP Wireless Days 2008. 8) Magno et al. Energy Efficient Cooperative MultimodalAmbient Monitoring. EuroSSC 2010. 9) Jelicicet al.An energyefficient multimodal wireless video sensor network with eZ430-RF2500 modules. ICPCA 2010.

  15. Heterogeneous WVSN HOMOGENEOUS NWK HETEROGENEOUS NWK Camera + PIR onboard Further reducing radio activities Further reducing cameras’ activities Tier 2 Camera nodes Coordinator wakeup Tier 1 PIR nodes • Two-tiernetwork • WOR  duty-cycling! • Two-tiernetwork • WURx  NO duty-cycling

  16. Smart gas monitoring • Metal Oxide Semiconductor (MOX) • Small form factor • Fast response • Power-efficient • Heater Resistance change • Fabrication field • System-level field • TWO SEPARATED AREAS BY NOW!

  17. Related work • Fabricationfield1, 2 • Pulse mode (duty-cycling) • Temperature dependence • Wake-uplatency • 9 mW • System-levelapplication3, 4, 5 • Dutycycle • Still high energy consumption 1) Sayhanet al. Discontinuouslyoperated metal oxide gas sensors for flexibletagmicrolabapplications. IEEE Sensors J. 2008. 2) Rastrelloet al. ThermalTransientMeasurementsofanUltra-Low-Power MOX Sensor. J. ofSensors 2010. 3) Ivanov et al. Distributedsmartsensorsystem for indoorclimatemonitoring. KONNEX Sci. Conf. 2002. 4) Postolacheet al.SmartSensorsNetwork for Air QualityMonitoringapplications. IEEE Trans. on Instrum. andMeas. 2009. 5) Choiet al. Microsensornode for air pollutantmonitoring: HW and SW issues. Sensors 2009. 6) De Vito et al. WirelessSensorNetworks for Distributed Chemical Sensing: Addressing Power Consumption LimitsWith On-Board Intelligence. IEEE Sensors J. 2011.

  18. System-level application – our solution 1) Jelicic et al. Design, Characterization and Management of a WSN for Smart Gas Detection. IWASI 2011. 2) Jelicic et al. Context-Adaptive Multimodal WSN for Energy-Efficient Gas Monitoring.IEEE Sensors J. 2012. • Energy consumption reduction on 3 levels: Network Node • Sensor level • duty-cycling gas sensor • early detection of safe conditions • Node level • ultra low sleep current (8 uA) • duty-cycling sensor node • people presence detection (modifying duty cycle) • Network level • messages from neighbor nodes (modifying duty cycle) Sensor

  19. Early detection of safe conditions R [kΩ] Stable difference between clean air and contaminated air signals 1x103 Clean air – after long inactive time Clean air – after short inactive time Contaminated air – after short inactive time 1x102 1x103 1x101 1x102 A threshold 1x101 B 1x100 1x100 1x10-1 47 200 0 1x10-1 0 9000 10000 4000 7000 8000 3000 5000 1000 6000 2000 time [ms]

  20. Adaptive sampling rate (t_ON = 1s)

  21. Quality ratio: node lifetime / worst case reaction time

  22. Motivationandchallenge • Policies to reduce • Communication energy • Sensing energy • Combined methods • Reducing amount of wirelessly transmitted data • adaptive sampling, event detection • Reducing radio idle consumption • Wake-up receiver • Goal • Context- and energy-awareness • Good QoS

  23. To-date work and results • Proposed energy saving policies in WVSN & WGSN1, 2, 5 • multimodal (PIR nodes); adaptive duty-cycling • Reducing communication energy 3, 6 • Extensive study and comparison of WURx solutions 4 1) Jelicic et al. Design, Characterization and Management of a WSN for Smart Gas Detection. IWASI 2011. 2) Jelicic et al. Context-Adaptive Multimodal WSN for Energy-Efficient Gas Monitoring.IEEE Sensors J. 2012. 3) Jelicic et al. Reducing Power Consumption of Image Transmission over IEEE 802.15.4/ZigBee Sensor Network. I2MTC 2010. 4) Jelicic et al. AnalyticComparisonofWake-upReceivers for WSNsandBenefitsovertheWake-on Radio Scheme. PM2HW2N 2012. 5) Jelicicet al.An energyefficient multimodal wireless video sensor network with eZ430-RF2500 modules. ICPCA 2010. 6) Jelicicet al.MasliNET – A WirelessSensorNetworkbasedEnvironmentalMonitoringSystem. MIPRO 2011.

  24. AIG – WSN activities ACTIVITIES APPLICATIONS • Environmental monitoring • Asthma monitoring • Wheeze detection • Air quality monitoring • Berth monitoring • Fall detection • Sensors and sensor interfaces • HW (PCB) design • Measurements • Embedded systems • Microcontrollers • FPGA • Firmware • Wireless sensor networks • Power management • Signal processing

  25. Hvala na pažnji!

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