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Gaura, Brusey

Sensing and Actuation: End-to-end systems design for safety critical applications. Dr. Elena Gaura, Reader in Pervasive Computing Director of Cogent Computing Applied Research Centre, Coventry University, e.gaura@coventry.ac.uk Dr. James Brusey, Senior Lecturer, j.brusey@coventry.ac.uk.

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Gaura, Brusey

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  1. Sensing and Actuation:End-to-end systems design for safety critical applications Dr. Elena Gaura, Reader in Pervasive Computing Director of Cogent Computing Applied Research Centre, Coventry University, e.gaura@coventry.ac.uk Dr. James Brusey, Senior Lecturer, j.brusey@coventry.ac.uk Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  2. Bremen, February 2009. Cogent Staff and PhD studentswww.cogentcomputing.org Tessa Daniel danielt@coventry.ac.uk Expertise: Applicative Query Mechanisms; Information Extraction in Wireless Sensor Networks. John Kemp kempj@coventry.ac.uk Expertise: Advanced Sensing; Sensing Visualisation Systems. Tony Mo tony.mo@coventry.ac.uk Expertise: Wireless sensing for gas turbine engines Michael Richards richardsm@coventry.ac.uk Expertise: 3D CFD Modelling Dr Elena Gaura e.gaura@coventry.ac.uk Expertise: Advanced Sensing; Advanced Measurement Systems; Ambient Intelligence; Design and Deployment of Wireless Sensor Networks; Distributed Embedded Sensing; Intelligent Sensors; Mapping Services for Wireless Sensor Networks; MEMS Sensors Dr James Brusey j.brusey@coventry.ac.uk Expertise: Industrial Robotics and Automation; Machine Learning; RFID; Sensing Visualisation Systems. Mike Allen allenm@coventry.ac.uk Expertise: Design and Deployment of Wireless Sensor Networks; Distributed Embedded Sensing. Costa Mtagbe Expertise: Environmental monitoring Ramona Rednic rednicr@coventry.ac.uk Expertise: Body sensor networks, Posture Dan Goldsmith goldsmitd@coventry.ac.uk Expertise: Middleware design and test-beds for WSNs Dr. Fotis Liarokapis f.liarokapis@coventry.ac.uk Expertise: Mixed reality systems; mobile computing, virtual reality for entertainment and education Dr. James Shuttleworth j.shuttleworth@coventry.ac.uk Expertise: 3D Graphics; data fusion and feature extraction, information visualization Gaura, Brusey

  3. Talk Scope • development cycle for a multi-modal wearable instrument • system design decisions • embedding actuation and its consequences • hurdles encountered…. Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  4. Pointers • Timeliness: BSNs and WSNs are becoming commercial in their simpler forms; also coming out of research labs in elaborate versions; • Task Difficulty: Designing such systems needs teams of applications specialists, electronics engineers (most often) and definitely Computer Scientists; • Usefulness: proven, but, apart from being very useful, BSNs are a lot of fun to develop! Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  5. Talk Structure • Part 1: Introduction and overview of the application • Part 2 : The deployment environment - a physiological perspective • Part 3 : System design • Part 4 : Enabling actuation - on-body processing • Part 5 : Implementation - software and hardware support • Part 6: Results analysis and evaluation Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  6. Part 1: Introduction and overview of the application Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  7. WSNs: research motivation • Start point: • -Smart Dust (1998) – Pister • ($35,000) vision of “millions of tiny wireless sensors (motes) which would fit on the head of a pin” • -sharing “intelligent” systems features (self –x) pushed to XLscale – millions of synchronized, networked, collaborative components • Today: • -Dust Networks - $30 mil venture (2006); • -TinyOS – the choice for 10000 developers • -make the news and popular press • - fashion accessory & easy lobbying • - big spenders have committed already (BP, Honeywell, IBM, HP)‏ • -technologies matured (digital, wireless, sensors)‏ • -first working prototypes; • -getting towards “out of the lab” • -social scientists are getting ready! Attention! Your spatio-temporal activities are recoded and analyzed by the 20000 sensors wide campus net Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  8. WSNs –reality Market forecast: 2014- $50bil. , $7bil in 2010 (2004)‏ 2014- $5-7 bil. sales (conservative)‏ 2011-$1.6 bil. smart metering/ demand response Industrial Markets-old and new; mostly wired replacements; generally continuous monitoring systems with “data-made-easy” features and internet connected Prompted by regulations and drive towards process efficiency or else… the “cement motes” from Xsilogy come with 30 min warranty! Infineon tyre sensor Connecting 466 foil strain gages to a wing box Invensys asked a Nabisco executive what was the most important thing he wanted to know. The reply came without a moment's delay: "I'd like to know the moisture content at the centre of the cookie when it reaches the middle of the oven." Research: mainly newly enabled applications; “macroscopes”/ “microscopes” ; adventurous money savings ideas Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  9. WSNs - pushing the frontiersThe motivational square …forget about throwing them from the back of that plane!... Practical, application oriented research and deployments Visions Making the most out of a bad situation Research space Research space Commercial endeavours Research/Adoption roadblocks Internet able Microclimate, soil moisture, disease monitoring Largest part of community Theoretical research for large scale networks Industrial needs Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  10. Why is it all so hard? …the WSN design space(Ray Komer, ETH, 2004)‏ deployment mobility cost, size, resources and energy heterogeneity communications modality infrastructure network topology coverage connectivity network size lifetime other QoS requirements Highly theoretical works Vs practical deployments Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  11. WSN challenges • Application specific (deployment, size, weight, etc)‏ • System specific – the network is the SENSOR • Distributed processing- system infrastructure • Information extraction • Scalability • Robustness • Node specific – hardware integration/fabrication/packaging Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  12. WSN – challenges cont’d • Physical environment is dynamic and unpredictable (Hw&Sw)‏ • Small wireless nodes have stringent energy, storage, communication constraints (Hw mainly)‏ • In-network processing of data close to sensor source provides (Sw, systems design)‏ • Scalability for densely deployed sensors • Low-latency for in situ triggering and adaptation • Embedded nodes collaborate to report interesting spatio-temporal events (Sytems design)‏ Embeddable Portable Adaptive Low cost Robust Self healing Self configuring Globally query-able Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  13. Application related challenges • User requirements definition – novel technology hence this is hard • Capability/expectations mitigation • Lack of comparison measure at end-to-end systems level • !!!Consequence!!! • Don’t underestimate the role of cyclic requirements/development/demonstration methodology Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  14. Data acquisition phase • Sensors availability – MEMS technologies are just maturing - many physical sensors available • Digital or analogue output - Digitization required • Sensors compatibility with other systems components • SENSORS CALIBRATION, DRIFT AND FAULTS- Mostly uncalibrated, but…very cheap • Integration sometimes a problem Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  15. Processing and comms challenges • Nodes size, weight, energy resources and processing capabilities – contrary constrains which need mitigating • Unreliability of wireless communications • Lack of debugging tools and wireless technology immaturity • Off-the-shelf comms encapsulation; unlexible protocols • Processing with little on much data Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  16. Processors and Motes Hardware Mote Sensor device interface Processor Memory Communications Form factor Renee Mezzanine card Atmel 8 bit 4 MHz 49 kB 916MHz, software modulation 484 mm2 rectangle Mica 2 Mezzanine card (4 sensors)‏ Analog Atmel 8 bit 8 MHz 644 kB 916/433MHz hardware modulation 19.2 kbps 1800 mm2 rectangle Mica2Dot Single sensor Analog Atmel 8 bit 4 MHz 644 kB 916/433MHz hardware modulation 19.2kbps 255 mm2 disc MicaZ Mezzanine card (4 sensors)‏ Analog Atmel 8 bit 8 MHz 644 kB 2.4GHz ZigBee 1800 mm2 rectangle Intel mote Digital interface ARM 32-bit 12 MHz 586kB 2.4GHz Bluetooth 900 mm2 rectangle Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  17. Information extraction challenges • Timeliness of acquired data • Time synchronization • Data storage • Information extraction at source • Co-opertive behaviour • Global vs local treatment of the challenge • Mitigating energy vs quality/detail vs timeliness vs system cost, size, etc Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  18. Information delivery challenges • Raw data is too much saying too little • Huge range of user requirements motivated by – conservativeness of some engineering fields (ref- Energy sector, aerospace, defence)‏ • Ease of interpretation by human in the loop – hard to accommodate with limited resources • Range of useful options continuously growing presently Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  19. Actuation enablers • Are still in its infancy • Much to be gained from any breakthroughs here • Enabling actuation has serious consequences in the overall system design Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  20. User satisfaction • Usually unknown/unpredictable till the development ends • Trail and error as the favourite methods presently • Huge range of reported work which failed to satisfy for all possible resons • Unreliability of the put-together systems is damaging to the filed Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  21. The Grand WSN challenge Facilitating the migration of pervasive sensing from future potential to present success • Design space • Care for the un-expert user – “beyond data collection systems” • Robustness, fault tolerance • Long life – across system layers and system components- in network processing &distribution • Maintenance free systems – scalability, remote programming &generic components/ infrastructure “The network is the sensor” VLS networks as Scientific instruments Permanent monitoring fixtures Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  22. Software - design features • designing for information visualization • designing for robustness and long life - Fault Detection and management • designing for practical applications • designing for robust services support • designing for information extraction- Complex Querying Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  23. Designing for practical applications • The problems: • Robustness of deployment • Technologies Integration • Fitness for purpose • Non-experts will use it!!! BSN End-to-end system design approach Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  24. Matching application requirements with available technology in a safety critical application Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  25. Project history • Commissioned late 2005 • Externally funded • Client: NP Aerospace Plc - protective clothing manufacturer for Defence - mostly for bomb disposal missions, de-mining, etc • PhD student project Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  26. Project aim: Increased safety of missions through remote monitoring Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  27. The problem: the suit Environment • Increased heat production and reduced ability to remove heat results in storage • Thermoregulatory system becomes unable to correctly regulate core temperature • This may result in physical and psychological impairment • Increased risk of making an avoidable error and jeopardising the mission Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  28. Possible solutions Manufacturer solution: add a cooling system to the suit Inadequate: Inefficient use due to human factors Distraction Alternative: in-suit instrumentation and continuous monitoring automated cooling actuation based on state Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  29. Architecture • Sense-model-decide-act architecture • Two control loops • Rapid feedback to autonomously adjust cooling • Support for modifications to mission plans and investigation into the construction of the suit. Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  30. Instrument Requirements • provide detailed physiological measurement - better insight into what is happening • support on-line and real-time thermal sensation estimates • report of useful information (rather than data) to a remote station and the operative • enable rapid assessment of hazardous situations • allow the provision of thermal remedial measures through control and actuation Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  31. Part 2 : The deployment environment - a physiological perspective Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  32. UHS and Suit Trials • UHS- the thermoregulatory system is unable to defend against increases in core body temperature • UHS - associated with significant physical and psychological impairment • Trials activity regime -four 16:30 min:sec cycles • treadmill walking • unloading and loading weights from a kit bag • crawling and searching • arm cranking • standing rest • seated physical rest Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  33. Experimental data • Measurands- wired instrumentation • Heart rate • rectal temperature • skin temperatures (arm, chest, thigh and calf )‏ • Assessment • Subjective thermal sensation – twice per cycle, per segment and overall • Comfort – as above • Measurands - wireless • Skin temperature - 12 sites (symetrical + neck +abdomen)‏ • Acceleration - 3D - 9 sites • Pulse oximetry, heart rate, CO2, galvanic Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  34. Experimental data Figure 5. Core temperature responses (n=4; error bars are omitted for clarity) FS-NC=full suit, no cooling; NS= no suit Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  35. Experimental data Figure 6. Skin and rectal temperature over time for a subject wearing the full suit with no cooling. Note how core temperature rises with thigh temperature after the two merge. This experiment needed to be terminated as the subject could not continue. Figure 3. Typical heart rate response to EOD activity simulation (based on a single subject trial). FS-NC=full suit, no cooling; NO-S=no suit; W=walking; U=unloadin/loading weights; C=crawling and searching; A= arm exercise; R= seated rest. NB. Two of four subjects were not able to complete four activity cycles. Figure 4. Mean skin temperature responses (averaged over 4 subjects; error bars are omitted for clarity). FS-NC=full suit, no cooling; NS=no suit Figure 7.Self-assessed thermal sensation compared with chest skin temperature for subject 1. Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  36. Part 3: System design Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  37. Constraints and design choices- I • Suit related • Mix of wired and wireless • Multiple sensors to each node • Wires in suit • Size, power and weight a concern • Suit modularity accounted for – multi-node BSN • Three tiers of comms • Sensors to node • Node to node • Node to base station Two separate systems for:- posture monitoring Physiological ??? Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  38. Constraints and design choices- II • Application related • Intermittent comms - jammers, obstacles • Maintaining autonomous operation - key • Two modes of wireless comms • In-suit, on body - short range, near field • External to mission control - long range • Buffering - avoid overflow • Priority transmission • Information extraction in-suit Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  39. Constraints and design choices-III • Safety critical • Cooling actuation • Operative alerts • Mission alerts • Hardware redundancy • Information extraction in-network - major design implications • Fault isolation and management Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  40. Constraints and design choices-IV • Instrument scope-dual • In field • In the lab - for physiological research and manufacturer research • User led choice of operation • In field • max infromation output - thermal sensation, cooling status, trends, alerts x2 • Data on demand - temperature and other selected • In the lab • Data output - continuous - all including accel • Information output - continuous Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  41. Part 4: In-network modeling Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  42. Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  43. Processing • Basic filtering performed on sensor node • Allows rejection of invalid data and generation of alarms • Additional filtering using a Kalman filter on the processing nodes • Smooths data as well as providing estimates of error • Modelling of thermal sensation • Operative alerts • Mission control alerts Include posture CO2 thresholding HR Prediction models Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  44. ISWC, Pittsburgh, 01/10/2008 Temperature and Thermal Comfort

  45. Temperature, Filters and Fusion – Kalman Filtering • Why filter? • Basic measurements may be too noisy • Can’t estimate gradient meaningfully otherwise • Why fuse measurements? • Two measurements are more reliable than one • Allow for / detect faulty sensors Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  46. Thermal sensation Modelling • Takes skin temperature (and optionally core temperature) readings as input • Provides an estimation of thermal sensation, both per body segment and globally, as output • The main part of the model is a logistic function based on two main parameters: • the difference between the local skin temperature and its “set” point (the point at which the local sensation is neutral) • the difference between the overall skin temperature and the overall set point • Thermal sensation is given in the range −4 to 4, with −4 being very cold and 4 being very hot Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  47. Zhang’s model Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  48. Zhang’s model evaluation Figure 9.Overall thermal sensation over time during the activity regime with the full suit and with no cooling. Figure 8. Overall thermal sensation over time during the activity regime with no suit. Figure 10.Overall thermal sensation over time for a habituated subject with the full protective suit and no cooling. Gaura, Brusey ISWC, Pittsburgh, 01/10/2008

  49. ISWC, Pittsburgh, 01/10/2008 HR and CO2

  50. ISWC, Pittsburgh, 01/10/2008 Posture

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