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Emerging networked sensing and actuation technologies: end-to-end wireless systems design for mission critical applications

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Sensing and Actuation: End-to-end systems design for safety critical applications Dr. Elena Gaura, Reader in Pervasive Computing Director of Cogent Computing Applied ... – PowerPoint PPT presentation

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Title: Emerging networked sensing and actuation technologies: end-to-end wireless systems design for mission critical applications


1
Sensing and ActuationEnd-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_at_cove
ntry.ac.uk Dr. James Brusey, Senior Lecturer,
j.brusey_at_coventry.ac.uk
Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
2
Cogent Staff and PhD studentswww.cogentcomputing.
org
Tessa Daniel danielt_at_coventry.ac.uk Expertise Ap
plicative Query Mechanisms Information
Extraction in Wireless Sensor Networks.
John Kemp kempj_at_coventry.ac.uk Expertise Advance
d Sensing Sensing Visualisation Systems.
Tony Mo tony.mo_at_coventry.ac.uk Expertise Wireless
sensing for gas turbine engines
Michael Richards richardsm_at_coventry.ac.uk Expertis
e 3D CFD Modelling
Dr Elena Gaura e.gaura_at_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_at_coventry.ac.uk Expertise
Industrial Robotics and Automation Machine
Learning RFID Sensing Visualisation Systems.
Mike Allen allenm_at_coventry.ac.uk Expertise Desig
n and Deployment of Wireless Sensor Networks
Distributed Embedded Sensing.
Ramona Rednic rednicr_at_coventry.ac.uk Expertise Bo
dy sensor networks, Posture
Costa Mtagbe Expertise Environmental monitoring
Dan Goldsmith goldsmitd_at_coventry.ac.uk Expertise
Middleware design and test-beds for WSNs
Dr. Fotis Liarokapis f.liarokapis_at_coventry.ac.uk E
xpertise Mixed reality systems mobile
computing, virtual reality for entertainment and
education
Dr. James Shuttleworth j.shuttleworth_at_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 frontiers The 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 communication
s 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/p
    ackaging

Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
12
WSN challenges contd
  • Physical environment is dynamic and unpredictable
    (HwSw)?
  • 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!!!
  • Dont 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, butvery 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
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 1630 minsec 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 (n4 error
bars are omitted for clarity) FS-NCfull 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-NCfull suit, no cooling NO-Sno
suit Wwalking Uunloadin/loading weights
Ccrawling 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-NCfull suit, no cooling NSno
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
Temperature and Thermal Comfort
45
Temperature, Filters and Fusion Kalman Filtering
  • Why filter?
  • Basic measurements may be too noisy
  • Cant 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
Zhangs model
Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
48
Zhangs 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
HR and CO2
50
Posture
51
Posture
52
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
53
Part 5 Prototype implelentation
Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
54

Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
55
Platform and sensors
Picture of CO2 and HR
JOHN New DIAGRAM HERE
Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
56

Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
57
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
58
Temperature Component Data Flow
Figure 13. Data and information system flow
Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
59
Posture Component Data Flow
60

Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
61
Remote Monitoring
New pic from Ramona paper
New pic from John paper
  • Main information display panel includes
  • a 3D figure showing the interpolated temperature
    distribution across the subjects 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
62

Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
63
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
64
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
65
Evaluation and results
Figure 19. Predicted thermal sensation including
dynamic component of model
Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
66
(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
67
Enriching the system for larger informational
gain - posture monitoring
Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
68
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
69
Results and evaluation for posture monitoring
Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
70
(No Transcript)
71
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
72
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

Visions led
SENSE and SEND
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
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 relay to
base 10. Central post processing
Gaura, Brusey
ISWC, Pittsburgh, 01/10/2008
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