Download - Gaura, Brusey
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Sensing and Actuation:End-to-end systems design for
safety critical applications
Dr. Elena Gaura, Reader in Pervasive ComputingDirector of Cogent Computing Applied Research Centre,
Coventry University,[email protected]
Dr. James Brusey, Senior Lecturer, [email protected]
Gaura, Brusey
Cogent Staff and PhD studentswww.cogentcomputing.org
Tessa Daniel [email protected]:Applicative Query Mechanisms; Information Extraction in Wireless Sensor Networks.
Mike Allen [email protected]:Design and Deployment of Wireless Sensor Networks; Distributed Embedded Sensing.
John Kemp [email protected]:Advanced Sensing; Sensing Visualisation Systems.
Dan Goldsmith [email protected]:Middleware design and test-beds for WSNs
Dr Elena Gaura [email protected]: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 [email protected]:Industrial Robotics and Automation; Machine Learning; RFID; Sensing Visualisation Systems.
Dr. James [email protected]:3D Graphics; data fusion and feature extraction, information visualization
Dr. Fotis [email protected]:Mixed reality systems; mobile computing, virtual reality for entertainment and education
Tony [email protected]:Wireless sensing for gas turbine engines
Ramona [email protected]:Body sensor networks,Posture
Michael [email protected]:3D CFD Modelling
Costa Mtagbe
Expertise:Environmental monitoring
Bremen, February 2009.
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
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
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
Part 1: Introduction and overview of the
application
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
WSNs: research motivationStart 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
WSNs –realityMarket 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!
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
Infineon tyre sensor
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
WSNs - pushing the frontiers The motivational square
Practical, application oriented research and deployments
Theoretical research for large scale networks
Visions
Industrial needs
Research space
Research space
Making the most out of a bad situation
Commercial endeavours
Internet able Microclimate, soil moisture, disease monitoring
Research/Adoption roadblocks
Largest part of community
…forget about throwing them from the back of that plane!...
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Why is it all so hard?…the WSN design space (Ray Komer, ETH, 2004)
deploymentmobilitycost, size, resources and energyheterogeneitycommunications modalityinfrastructurenetwork topologycoverageconnectivitynetwork sizelifetimeother QoS requirements
Highly theoretical worksVspractical deployments
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
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 healingSelf configuring Globally query-able
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
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
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
Processors and Motes Hardware
MoteSensor device
interfaceProcessor 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 916/433MHzhardware modulation19.2 kbps
1800 mm2
rectangle
Mica2Dot Single sensorAnalog
Atmel 8 bit 4 MHz
644 kB 916/433MHzhardware modulation19.2kbps
255 mm2
disc
MicaZ Mezzanine card (4 sensors)
Analog
Atmel 8 bit 8 MHz
644 kB 2.4GHzZigBee
1800 mm2
rectangle
Intel mote Digital interface
ARM 32-bit 12 MHz
586kB 2.4GHzBluetooth
900mm2
rectangle
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
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
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
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
The Grand WSN challengeFacilitating 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
VLS networks asScientific instruments
Permanent monitoring fixtures
“The network is the sensor”
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
Designing for practical applications
BSNThe problems:•Robustness of deployment•Technologies Integration•Fitness for purpose•Non-experts will use it!!!
End-to-end system design approach
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Matching application requirements with available technology in a
safety critical application
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
Project aim: Increased safety of missions
through remote monitoring
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
Possible solutionsManufacturer solution: add a cooling system to the suitInadequate:a) Inefficient use due to human factorsb) Distraction
Alternative:a) in-suit instrumentation and continuous monitoringb) automated cooling actuation based on state
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
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
Part 2 : The deployment environment - a physiological perspective
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
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
Experimental data
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Figure 5. Core temperature responses (n=4; error bars are omitted for clarity) FS-NC=full suit, no cooling; NS= no suit
Experimental data
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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 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 7.Self-assessed thermal sensation compared with chest skin temperature for subject 1.
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Part 3: System design
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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 BSNThree tiers of comms
Sensors to nodeNode to nodeNode to base station
Two separate systems for:- posture monitoring Physiological ???
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Constraints and design choices- II
Application relatedIntermittent comms - jammers, obstaclesMaintaining autonomous operation - key
Two modes of wireless commsIn-suit, on body - short range, near fieldExternal to mission control - long rangeBuffering - avoid overflowPriority transmissionInformation extraction in-suit
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
Constraints and design choices-IV
Instrument scope-dual– In field– In the lab - for physiological research and manufacturer research
User led choice of operationIn field
max infromation output - thermal sensation, cooling status, trends, alerts x2
Data on demand - temperature and other selectedIn the lab
Data output - continuous - all including accelInformation output - continuous
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Part 4: In-network modeling
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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 postureCO2 thresholdingHRPrediction models
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Temperature and Thermal Comfort
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
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
Zhang’s model
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Zhang’s model evaluation
Figure 8. Overall thermal sensation over time during the activity regime with no suit.
Figure 9.Overall thermal sensation over time during the activity regime with the full suit and with no cooling.
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
HR and CO2
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Posture
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Posture
Follow-up• New model needed
• Activity needs monitoring – posture
• Other physiological parameters have to be tried out –HR, galvanic response, heat flux
• Model needs to predict not estimate/assess
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Part 5: Prototype implelentation
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Platform and sensors
JOHN’ New DIAGRAM HERE
Picture of CO2 and HR
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Networking
• Wireless links between actuation / processing nodes• Wireless link between actuation node and remote
monitoring point• Data/information buffered in case of link failure - may
be uploaded at future point
New pic from John and Ramona here
Temperature Component Data Flow
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Figure 13. Data and information system flow
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Posture Component Data Flow
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Remote Monitoring
• Main information display panel includes:– a 3D figure showing the interpolated temperature
distribution across the subject’s skin– the current average skin temperature, and – the current thermal sensation level
• Other panels show the location and status of the sensors and the history of the incoming data
New pic from John paper
New pic from Ramona paper
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Actuation• Reinforcement Learning algorithms (such as
SARSA) can be used to develop a “policy” for controlling the cooling fan based on the “state” of the user
• Action is to turn fan on or off and regulate volume
• Utility is based on maintaining good comfort levels over time
• Takes account of battery depletion, likely mission duration, posture, as well as current thermal comfort
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Operative alerts• Framework in place• Data and information processing flows readily
available (piggy back on mission control)• Avoid false alarms - link to robustness and fault
management• Sound considered at this stage but tactile
sounds good too• Research into HCI issues badly needed
Elena to change
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Evaluation and results
Figure 19. Predicted thermal sensation including dynamic component of model
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Figure 10. Skin temperature over time for (a) arm, (b) neck, (c) abdomen, (d) chest, (e) thigh, and (f) calf sites. The two leg sensors (thigh and calf positions) were placed on the right leg only. For several skin sites, temperature values were also obtained using a wired-in data logger (denoted "Logger"). The vertical lines in each graph show the start and end of activities. Each activity is represented by a number.
(a) (b)
(c) (d)
(e) (f)
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Enriching the system for larger informational
gain - posture monitoring
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Aim and postures• Dual aim
– Direct activity information to mission control for• Supervision of mission - health hazards/colapse/restrains• Technical assessment - problems - controller expertise• Inferrence of abstract info by controllers
– Parameter for thermal state prediction• 8 postures required: stand, walk, crawl, sitting,
lying down (up, down, side x2)
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Results and evaluation for posture monitoring
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Review of tutorial and summary
• exposition of design techniques and design choices• focus on an example
• BSN- neither large nor widely distributed but there are a number of fundamental requirements– the size of the nodes, wearability of the instrumentation, robustness,
reliability and fault-tolerance, etc• they dictate the majority of the design and implementation choices.
• Pursuing application driven design processes will enable the development of industrially strong systems which will increase confidence in the technology and contribute to its adoption in near future.
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
WSN – theoretical wonders- Scoping of large scale applications- Complex problems solved for individual
functional components/services- Theoretical proofs and simulation only- Lack of integrative work
1. Dust size- mm cube
2. Unplanned deployment3. Distributed
4. Millions of5. Re-configurable nets6. Self-healing7. Scalable8. Autonomous9. Information systems10.Collaborative decisions
1. Stack of quarters & miniaturization vs mote life trade-off2. Planned, carefully measured; ID based3. Gateway based – centrally controlled; backboned4. Hundreds at most (ExScal)5. Hard coded6. Prone to failure (more than 50% usually)7. Only through complete re-design8. Tightly controlled9. Data acquisition – relay to base10. Central post processing
Visions led SENSE and SEND