Transcript Emerging networked sensing and actuation technologies: end
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, [email protected]
Dr. James Brusey, Senior Lecturer, [email protected]
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Cogent Staff and PhD students www.cogentcomputing.org
Dr Elena Gaura
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
Expertise: Industrial Robotics and Automation; Machine Learning; RFID; Sensing Visualisation Systems.
Michael Richards
richardsm@coventr y.ac.uk
Expertise: 3D CFD Modelling
Tessa Daniel
Expertise: Applicative Query Mechanisms; Information Extraction in Wireless Sensor Networks.
Tony Mo
tony.mo@coventry.
ac.uk
Expertise: Wireless sensing for gas turbine engines
John Kemp
Expertise: Advanced Sensing; Sensing Visualisation Systems.
Dr. Fotis Liarokapis
Expertise: Mixed reality systems; mobile computing, virtual reality for entertainment and education Gaura, Brusey
Dr. James Shuttleworth
Expertise: 3D Graphics; data fusion and feature extraction, information visualization
Costa Mtagbe
Expertise: Environmental monitoring
Mike Allen
Expertise: Design and Deployment of Wireless Sensor Networks; Distributed Embedded Sensing.
Bremen, February 2009.
Ramona Rednic
Expertise: Body sensor networks, Posture
Dan Goldsmith
goldsmitd@coventry .ac.uk
Expertise: Middleware design and test-beds for WSNs
Gaura, Brusey
Talk Scope
• development cycle for a multi modal wearable instrument • system design decisions • embedding actuation and its consequences • hurdles encountered….
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
Gaura, Brusey
Part 1: Introduction and overview of the application
ISWC, Pittsburgh, 01/10/2008
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 Gaura, Brusey “out of the lab” ISWC, Pittsburgh, 01/10/2008 -social scientists are getting ready!
Attention!
Your spatio-temporal activities are recoded and analyzed by the 20000 sensors wide campus net
WSNs –reality
Market forecast: 2014- $50bil. , $7bil in 2010 (2004) 2014- $5-7 bil. sales (conservative) Infineon 2011-$1.6 bil. smart metering/ demand response tyre sensor 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!
Research: mainly newly enabled applications; “ “ microscopes macroscopes ”/ ” ; adventurous money Connecting 466 foil strain gages to a wing box
ISWC, Pittsburgh, 01/10/2008 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."
WSNs - pushing the frontiers The motivational square
Practical, application oriented research and deployments …forget about throwing them from the back of that plane!...
Making the most out of a bad situation
Visions
Research space Commercial endeavours
Research space Research/Adoption roadblocks
Internet able Microclimate, soil moisture, disease monitoring
Industrial needs
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Largest part of community Theoretical research for large scale networks
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
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 Low cost Self configuring Portable Robust Adaptive Self healing 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
Mote
Processors and Motes Hardware
Memory Communications Sensor device interface Processor Renee Mica 2 Mica2Dot MicaZ Intel mote Mezzanine card Atmel 8 bit 4 MHz 49 kB Mezzanine card sensors) (4 Analog Single sensor Analog Atmel 8 bit 8 MHz Atmel 8 bit 4 MHz 644 kB 644 kB Mezzanine card sensors) (4 Analog Digital interface Atmel 8 bit 8 MHz ARM 32-bit 12 MHz 644 kB 586kB 916MHz, software modulation 916/433MHz hardware modulation 19.2 kbps 484 mm 2 rectangle 1800 mm 2 rectangle 916/433MHz hardware modulation 19.2kbps
2.4GHz
ZigBee 255 mm 2 disc 1800 mm 2 rectangle 2.4GHz
Bluetooth Form factor 900 mm 2 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 challenge Facilitating the migration of pervasive sensing from future potential to present success Design space VLS networks as
Scientific instruments Permanent monitoring fixtures Gaura, Brusey
“The network is the sensor ”
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Care for the un-expert user – “beyond data collection systems”
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Robustness, fault tolerance
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Long life – across system layers and system components- in network processing &distribution
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Maintenance free systems – scalability, remote programming &generic components/ infrastructure
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 BSN
End-to-end system design approach The problems:
•Robustness of deployment •Technologies Integration •Fitness for purpose •Non-experts will use it!!!
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 solutions
Manufacturer solution: add a cooling system to the suit Inadequate: a) Inefficient use due to human factors b) Distraction Alternative: a) in-suit instrumentation and continuous monitoring b) 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 Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
Experimental data
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 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 4. Mean skin temperature responses (averaged over 4 subjects; error bars are omitted for clarity). FS-NC=full suit, no cooling; NS=no suit Gaura, Brusey Figure 7.Self-assessed thermal temperature for subject 1.
sensation compared with chest skin 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 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
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
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 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
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
Include posture CO2 thresholding
• Mission control alerts
HR
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Prediction models
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 Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Zhang’s model
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.
Gaura, Brusey Figure 10.Overall thermal sensation over time for a habituated subject with the full protective suit and no cooling.
ISWC, Pittsburgh, 01/10/2008
HR and CO2
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Posture
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Posture
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
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
Part 5: Prototype implelentation
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Platform and sensors
JOHN’ New
Picture of CO2 and HR
DIAGRAM HERE
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Networking
New pic from John and Ramona here
• 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 Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Temperature Component Data Flow
Gaura, Brusey Figure 13. Data and information system flow 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
New pic New pic from John from pape r Ramona paper
• 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 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
change
• Research into HCI issues badly needed Gaura, Brusey ISWC, Pittsburgh, 01/10/2008
Evaluation and results
Gaura, Brusey Figure 19. Predicted thermal sensation including dynamic component of model ISWC, Pittsburgh, 01/10/2008
(a) (b) (c) (d) (e) (f) 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.
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
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Scoping of large scale applications Complex problems solved for individual functional components/services Theoretical proofs and simulation only Lack of integrative work Visions led
1. Dust size- mm cube 2. Unplanned deployment 3. Distributed 4. Millions of 5. Re-configurable nets 6. Self-healing 7. Scalable 8. Autonomous 9. Information systems 10.Collaborative decisions
SENSE and SEND
1. Stack of quarters & miniaturization vs mote life trade-off 2. Planned, carefully measured; ID based 3. Gateway based – centrally controlled; backboned 4. Hundreds at most (ExScal) 5. Hard coded 6. Prone to failure (more than 50% usually) 7. Only through complete re-design 8. Tightly controlled 9. Data acquisition ISWC, Pittsburgh, 01/10/2008 – relay to base 10. Central post processing