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Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
PDT (™): Personal Data from Things,
and its provenance
Paolo Missier
School of Computing Science
Newcastle University
The SRC-IoT Workshop:
Systems Research Challenges in the Internet of Things
Northumberland, January 11-12, 2016
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
The Internet of Things is Many Things
The IEEE IoT initiative
Revision 1– 27 MAY 2015
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
One of the possible stacks
Source: Towards a definition of the Internet of Things (IoT) IEEE Internet Initiative Iot.ieee.org
Telecom Italia S.p.A. Roberto Minerva, Abyi Biru, Domenico Rotondi, May 2015
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
It’s all about connectivity
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Evolution of the Internet (according to ETSI)
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Evolution of the Internet (according to Google)
information graph  connection to content
social graph  connections amongst people
physical graph  connections amongst things
TheInternetOfThingscreatesthephysicalgraph->that
changeshowweinteractwithobjectsandenvironments
Totalaccessandubiquityof
content
Adaptive,selfregulating
environmentsthatunderstand
contextandadjustaccordingly
TheInternetcreatedtheinformationgraph->thatchanged
howweproduce,access,shareandgenerateknowledge
Socialmediacreatedthesocialgraph->thatchangedhow
weestablishandfosterrelationshipwithothers
Enablingpowertothecrowd
3)Findtherightrelevantobjectsandconnections
Source: IEEE Internet of Things Vint Cerf, Google - December 15th 2015
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Use cases – at different scales
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
IoT and Smart-*
50 Sensor Applications for a Smarter World
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
The connected washing machine example
1) Re-imagine statics object with the power of the web
W ashing Machine
Single purposed
No user customization
Rudimentary notification system
Not aware of its energy
consumption
Net
Context
Learning
Access to services and API’s
Access to other devices
+ + =
W ashing Machine
Adaptive
Efficient
Optimized
Personalized
Connectivity
New features
New services
New business models
web
1) Re-imagine isolated objects with the power of the net
Understanding the implications of what happens when an
‘ordinary’ object is connected to the net
Source: IEEE Internet of Things Vint Cerf, Google - December 15th 2015
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Role of metadata and provenance for IoT: three angles
• IoT ∩ Science
• IoT ∩ People  Personal Data from Things (PDT)
• Things that make decisions
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
IoT ∩ Science
Sensor-based science
- Pervasive / ubiquitous computing,
human/animal behaviour analysis,
climate science, …
Some well known issues:
- Sensor reading quality – QA, outliers, false readings
- What we have: Metadata / context
- About the sensors  id, type, calibration, parameter settings
- About the data readings  timestamp
- About the quality assessed through QA processes
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
IoT ∩ Science  Metadata
This requires capturing and managing
provenance and other metadata
Provenance: a record of data derivation through multiple process
transformations
- Complementary to descriptive metadata
- enables reasoning about the findings, validation
• How was the data collected?
• How was it processed?
• Who was responsible?
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
drafting commenting
paper1
paper2
used
draft
v1
wasGeneratedBy used draft
comments
wasGeneratedBy
Alice
Bob
wasAssociatedWith
actedOnBehalfOf
Remote past Recent past
distribution=internal
status=draft
version=0.1
ex:role=main_editor
type=person
ex:role=sr_editor
prov:role=editor
time=...
time=...
PROV
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
And its human-readable, formal representation
prefix ex <http://example.org/>
// what happened?
entity(ex:docDraft, [ prov:type="paper", ex:version="v.01", ex:status="draft"
])
activity(ex:drafting, 2013-03-16T10:00:00, 2013-03-17T10:00:00)
wasGeneratedBy(ex:docDraft, ex:drafting, 2013-03-18T10:00:01)
entity(ex:paper1, [ prov:type="paper", ex:doi="..."])
entity(ex:paper2, [ prov:type="paper", ex:doi="..."])
used(ex:drafting, ex:paper1, -)
used(ex:drafting, ex:paper2, -)
// who was responsible?
agent(ex:Bob, [ ex:firstName="Robert", ex:lastName="Thompson",
prov:type="ex:seniorEditor" ])
//agent(ex:Alice, [ ex:firstName="Alice", ex:lastName="Cooper",
prov:type="ex:chiefEditor" ])
wasAssociatedWith(ex:drafting, ex:Bob, -) // no plan
// delegation
actedOnBehalfOf(ex:Bob, ex:Alice) // global activity scope
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Provenance pattern for sensor data
Key issue: managing data/process granularity
Volume, complexity of transformations P1, P2, ….  black/grey/white box provenance
- how much detail do we need?
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
IoT ∩ Science
Typical uses for provenance:
• impact analysis (forwards)
• cause analysis (backwards)
Note on reproducibility: Observational data is generally not reproducible!
How much provenance is needed?
Impact analysis:
Suppose a sensor is later determined to be faulty (false readings)
How does that impact the experimental findings?
Cause analysis:
These conclusions seem implausible. What went wrong along the process?
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
IoT ∩ People  Personal Data from Things (PDT)
IoT vision: devices (“smart washing machine”) will make our lives better
They often also produce data that is also personal
As per the Data Protection Act 1998
• Are people aware of the trade-offs between privacy and benefits?
1. Ownership:
• What is “my” data? (who owns the utility consumption figures in my
house? Or an activity trace collected using a “smart shoe”?)
• Who else has access to it? To what extent?
2. Awareness of third party use of personal data:
• Who has been doing what with my data?
• How much of the data used in a certain computation is my data??
• What has its contribution been to the analytics?
3. Control. How much control can I have on the data that devices
produce on my behalf?
Ownership + awareness + control  Trust
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Two recent publications
Mashhadi, Afra, Fahim Kawsar, and Utku Gunay Acer. “Human Data Interaction in IoT: The
Ownership Aspect.” In Internet of Things (WF-IoT), 2014 IEEE World Forum on, 159–162,
2014.
Vescovi, Michele, Corrado Moiso, Fabrizio Antonelli, Mattia Pasolli, and Christos
Perentis. “Building an Eco-System of Trusted Services through User Transparency,
Control and Awareness on Personal Data Privacy.” In Procs. W3C Workshop on Privacy
and User–Centric Controls. Berlin, Germany, 2014.
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
IoT ∩ People  Personal Data from Things (PDT)
Example:
SPHERE - a Sensor Platform for HEalthcare in a Residential Environment
(EPSRC, 2013-2018, Bristol, Prof. Ian Craddock)
http://irc-sphere.ac.uk/
Zhu. N, Diethe. T, Camplani. M, Tao. L, Burrows. A, Twomey. N, Kaleshi. D, Mirmehdi. M, Flach. P, Craddock. I, Bridging
eHealth and the Internet of Things: The SPHERE Project. IEEE Intelligent Systems 30 (4), 39-46. (doi: 10.1109/MIS.2015.57)
All about sensing, wearables, & detecting people’s activities
Instrumented “SPHERE house”
— scaling up to 100 homes by 2017 lots of data collection, data mining challenges
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Activity detection pattern
Accelerometry data
Indoor location data
Activity
Detection
Accelerometry data
Indoor location data
Activity
Detection
Aggregate
Analytics
03 January 2016 10:16
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Activity detection: provenance pattern
Key issue:
Distributed, fragmented provenance
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Identity management
1)Data&IdentityManagement
Iknowmydatais
secure.
Ihavecontrolovermydata
digitalidentityanddataper
sensor,peraccount,per
product,perhome
Iknowmydeviceswillask
beforesharinganydatawith
otherdevices.
Devicehardwareand
softwareconsidersafety
firstandandautoupdate
bymanufacturer
1)Data&IdentityManagement
Ausercontrolledsystemwithmanagementcontrolswillelicitgreatertrustand
adequateprivacywhilesecurityandsafetycanbehandledmostlybyserviceprovider.
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
IoT Standards –smart objects
Smart objects identity and privacy
Source: IoT Standards: The Next Generation
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
IoT is really about M2M!
Example: V2V (Vehicle-to-Vehicle coordination)
And the IoV (Internet of Vehicles)
Source: Mario Gerla, "Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds”,
IEEE IoT forum, Dec. 2015, Keynote
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
What is M2M?
Data communication among the physical things which do not need
human interaction.
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Things that make decisions
Some challenges:
Provenance patterns for streaming, message passing: “V1 sent sij to V2”
How much “provenance” does each sensor reading need to carry? How
does this fit with M2M protocols?
Provlets: embed in messages vs stored separately in a repository
(indexed by key: <S.id, t>)
- M2M means more in-network provenance
- The data remains at the edge of the network
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Metadata management in the IoT architecture – oneM2M model
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
SenML
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Fog and Cloud
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Cloud vs Fog computing
19©2013-2014Ciscoand/oritsaffiliates.Allrightsreserved.
DataCentre/Cloud
SmartObjects
IoTandFogComputingArchitecture
DataPoints,Variety&Velocity,Security,Resiliency,Latency
FogNetwork
CoreNetwork
TensofMillionstoBillions
EmbeddedSystems&Sensors
Lowpower,lowbandwidth
TensofThousandstoMillions
Multi-ServiceEdge
2G/3G/LTE/WiFi/RFMesh/PLC
TSN:TimeSensitiveNetworks
6TiSCH
Thousands
Backhaul
IP/MPLS,Sec.,QoS,Multicast
Hundreds
DataCentre/Cloud
HostingIoTAnalytics
Sensing
Control
Correlation
Millsecond/secondsresponse
Transactionalresponsetimes
KB-GB
GB-TB
TB-PB
Infinite
ref.:
Datta, S.K.; Bonnet, C.; Haerri, J., "Fog Computing architecture to enable consumer centric Internet of
Things services," in Consumer Electronics (ISCE), 2015 IEEE International Symposium on, pp.1-2, 24-
26 June 2015
Preparedfor:
SystemsResearchChallengesintheInternetofThings
Newcastle,Jan.2016
Key points for provenance in the IoT context
Provenance for M2M at the edge
• Embedding / associating metadata with M2M messages
• Generating provlets in a Fog architecture
• Reconstructing a coherent provenance graph from the fragments
• Provenance / metadata analysis in the cloud

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PDT: Personal Data from Things, and its provenance

Notas do Editor

  1. ETSI - European Telecommunications Standards Institute
  2. Funding 11M Types of sensors SPHERE envisages sensors, for example: 1) That employ video and motion analytics to predict falls and detect strokes so that help may be summoned. 2) That uses video sensing to analyse eating behaviour, including whether people are taking their prescribed medication. 3) That uses video to detect periods of depression or anxiety and intervene using a computer-based therapy. The SPHERE IRC will take a interdisciplinary approach to developing these sensor technologies, in order that: 1) They are acceptable in people's homes (this will be achieved by forming User Groups to assist in the technology design process, as well as experts in Ethics and User-Involvement who will explore issues of privacy and digital inclusion). 2) They solve real healthcare problems in a cost-effective way (this will be achieved by working with leading clinicians in Heart Surgery, Orthopaedics, Stroke and Parkinson's Disease, and recognised authorities on Depression and Obesity). 3) The IRC generates knowledge that will change clinical practice (this will be achieved by focusing on real-world technologies that can be shown working in a large number of local homes during the life of the project).
  3. Configuration Storage Layer 􏰅 Contains “Configuration Storage API”. 􏰅  The smart devices directly connect to this API during the bootstrap phase 􏰅  It extracts the resource descriptions from the devices or (proxies in case of legacy devices). 􏰅 The layer houses a database and stores the device, endpoint and configuration resources in separate tables. 􏰅 The API translates the CoRE Link based descriptions to appropriate storage format. This layer also keeps track of the configuration “lifetime” attribute. 􏰅 During that period, if it does not receive an announcement that the device is still present or configuration update, it will delete that device configuration.
  4. Sensor measurement alone has no value – Need additional side information like unit, timestamp, type of sensor