Talk at the 3rd International Disaster Management Workshop at KAIST, Daejeon, South Korea

1. getting Fog Computing to work
PATCHING MR. ROBOT
Mitigating IoT-related Cyber-Social-Disasters by
EUGENE SIOW

2. A hit TV-Series portraying realistic hacking and bleeding-edge technology
fsociety E CORP

3. Raspberry Pi Thermostat Hack
PROGRESSION OF HACKS
HVAC Hack
Wipe Debts
Jailbreak
Grand Theft Auto
Smart Home Hack
DDOS
72°F
200°F
Smart Home Hack

4. SMARTHOME HACK
WHATAM I SUPPOSEDTO DO?
NOTHINGISWORKING
UNPLUGWHAT?
EVERYTHING ISINSIDETHE WALLS

5. INSTEON HACK
NO OR DEFAULT
USERNAME & PASSWORD
FROM A NOW DISCONTINUED
INSTEON PRODUCT
CIRCUMVENT PASSWORD
BY GOING DIRECT TO PORT
E.G. http://ip/dash to
http://ip:port/console
REMOTELY SWITCHED
LIGHTS OFF
A PASSWORD ON THE PORT-
ACCESSED PORTAL THE NEXT DAY
COMPROMISED
“ALL YOUR BASE ARE BELONG TO
US”
CALLED AN INSTEON
CONSULTANT
HE INSISTED THAT THE PORTAL
WAS READ-ONLY AND PASSWORD
PROTECTED FOR ACTUATION
Forbes, 2013
GOOGLED A
PHRASE
FOUND A LIST OF
‘SMART HOMES’
FORBES
REPORTER
KASHMIR HILL
ACCESSED WEB PORTAL
CONTROLS FOR LIGHTS, HEATING,
PARENTAL CONTROLS, DOORS

6. Resource constrained sensors
& devices might be and
unable to store, processor
implement appropriate
security.
DEVICECONSTRAINTS
WHAT’SWRONG WITH THE IOT?
An IoT predominantly consisting of device-to-cloud setups
It can be prohibitively
expensive to move big data
through the Internet and to
store it on the cloud.
MOVING & STORING
“The IoT suffersfrom a lack of
interoperability…developers
are faced with data silos, high
costs and limited market
potential.” – W3C Web of
Things
DATASILOS
Can we trust vendors to keep
data private and secure on
public clouds? Encrypting the
data increases processing
required and decreases
interoperability.
CLOUD PRIVACY
Internet based transmissions
may increase the probability
of information leakage.
LARGERAREA FOR
LEAKAGESInternet access may be
unavailable, unreliable, and
slow e.g. natural disasters,
poor infrastructure,remote
areas.
CONNECTION ISSUES

7. APPLE
GOOGLE
HONEYWELL
CISCO
HUAWEI
GENERALELECTRIC
IBM
AMAZON
INTEL
LET’S TALKFOG COMPUTING
MICROSOFT

8. A REAL-WORLD
FOGCOMPUTINGINFRASTRUCTURE
Fog Computing utilises the space between the
“Ground” and “Cloud”
Irrigation Application
Soil Moisture
Analytics
Lightweight
ComputerHub
Data Stream
Environmental
Sensors
GROUND
National Disaster Monitoring Application
Weather
Data
State Inclement
Weather Planning
Application
CLOUD
Distributed Queries

9. OUR RESEARCH
Building ”Pillars”to support Fog Computing
Sustainable & Secure
INTEROPERABILITY
DISTRIBUTION
EFFICIENCY
Linked Data
Faster Queries
eugenesiow.github.io/iot

10. INTRODUCING
LINKED DATA
FOR INTEROPERABILITY
URI andontologies
Establish commondata structures& References
ENABLES RICH METADATA
what,where, WHEN,HOW of DATA
PERFORMANCE CHALLENGES
STORES DON’T SCALE & PERFORM WELLON WEB YET
Buil-Aranda, C., Hogan, A.: SPARQL Web-Querying Infrastructure: Ready for Action?
ISWC 2013
TRAFFIC SENSOR
POLLUTIONSENSOR
Semantic Sensor Ontology
EVENTS STREAM
Smart City Ontology
LOCATION
GeoNames Ontology

11. THE SHAPE OFIOT TIME-SERIES DATA
{
timestamp : 1467673132,
temperature : {
max: 22.0,
min: 15.0,
current: 17.0,
error: {
percentage: 5.0
}
}
}
FLAT
{
timestamp : 1467673132,
temperature : 32.0,
wind_speed : 10.5,
pressure : 1016
}
COMPLEX
20kUNIQUE DEVICES
dweet.io
99.5%FLAT SCHEMATA
0.5%COMPLEX SCHEMATA
1
2,3
4
5
6+
Width
{
timestamp : 1467673132,
temperature : 32.0,
humidity : 10.5,
pressure : 1016,
light: 120.0,
}
1
2
3
4

12. EFFICIENTQUERIESWITH
TIME-SERIES
DATA
THING
TEMPERATURE OBS
HUMIDITY OBS
WIND SPEED OBS
13.0
2016-01-0106:00:00
CELCIUS
93.0
2016-01-0106:00:00
PERCENT
10.5
2016-01-0106:00:00
MPH
LOCATION
produces
produces
located
produces
has value
unit
time
RDF
GRAPH
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference

13. THING
TEMPERATURE OBS
HUMIDITY OBS
WIND SPEED OBS
13.0
LOCATION
produces
produces
located
produces
has value
THING
THING
THING
TEMPERATURE OBS
timeTEMPERATURE OBS 2016-01-0106:00:00
unitTEMPERATURE OBS celcius
93.0has valueHUMIDITY OBS
timeHUMIDITY OBS 2016-01-0106:00:00
unitHUMIDITY OBS PERCENT
10.5has valueWIND SPEED OBS
timeWIND SPEED OBS 2016-01-0106:00:00
unitWIND SPEED OBS MPH
EFFICIENTQUERIESWITH
TIME-SERIES
DATA
RDF
TRIPLES
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference

14. OUR
APPROACH
EFFICIENTQUERIESWITH
TIME-SERIES
DATA
THING
TEMPERATURE OBS WIND SPEED OBS
CELCIUS PERCENT MPH
LOCATION
produces
located
HUMIDITY OBS
unit
TEMPERATURE HUMIDITY WIND SPEED
13.0 93.0 10.5
TIME
2016-01-01 06:00:00
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference

15. DESIGNING OURENGINE
THING
TEMPERATURE OBS WIND SPEED OBS
CELCIUS PERCENT MPH
LOCATION
produces
located
HUMIDITY OBS
unit
TEMPERATURE HUMIDITY WINDSPEED
13.0 93.0 10.5
TIME
2016-01-01 06:00:00
Table1
TABLE1.TEMPERATURE
has value has value
TABLE1.HUMIDITY
has value
TABLE1.WINDSPEED
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference

16. DESIGNING OURENGINE
THING
TEMPERATURE OBS WIND SPEED OBS
CELCIUS PERCENT MPH
LOCATION
produces
located
HUMIDITY OBS
unit
TEMPERATURE HUMIDITY WINDSPEED
13.0 93.0 10.5
TIME
2016-01-01 06:00:00
Table1
TABLE1.TEMPERATURE
has value has value
TABLE1.HUMIDITY
has value
TABLE1.WINDSPEED
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference

17. DESIGNING OURENGINE
THING
TEMPERATURE OBS
CELCIUS PERCENT
produces
loc
HUMIDITY OBS
unit
TEMPERATURE HUMID
13.0 93.0
TIME
2016-01-01 06:00:00
TABLE1.TEMPERATURE
has value has va
TABLE1.H
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference
𝞹
𝞬 (max ( ))?TEMPERATURE
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom BGP

18. DESIGNING OURENGINE
TEMPERATURE OBS
CELCIUS
TEMPERATURE
13.0
TABLE1.TEMPERATURE
has value
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
𝞹
𝞬 (max ( ))?TEMPERATURE
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference
BGP

19. SPARQL
DESIGNING OURENGINE
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
SELECT
MAX( )?TEMPERATURE
?OBS ?TEMPERATURE ?uom
TABLE1.TEMPERATURE CELCIUSNODE_TEMP
𝞹
𝞬 (max ( ))?TEMPERATURE
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom BGP
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference

20. SPARQL
DESIGNING OURENGINE
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}
SQL SELECT MAX( )TEMPERATURE FROM TABLE1
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference

21. EVALUATIONWITH BENCHMARKS
SRBENCH
~20,000 Stations
100 – 300k triples
Wind, Rainfall, etc.
10 SRBench Queries
Zhang, Y, et al. (2012) "SRBench: a streaming RDF/SPARQL
benchmark.”The 11th International Semantic Web Conference.
SMART HOME BENCH
Siow, E., Tiropanis, T., Hall, W. (2016). "Interoperable and Efficient:
Linked Data for the Internet of Things." The 3rd International
Conference on Internet Science.
3 months, 1 home
~30k triples
Motion, energy, environment
4 Analytics Queries
GraphDB (OWLIM)
Ontop
Our Approach (S2S)
TDB
G
Morph
O
S
M
T

22. STORAGESIZE
3ook
HurricaneIke
1ook
NEVADABLIZZARD
3ok
SMARTHOME
OUR APPROACH(s2S)
TDB
x15
x68
x112
GraphDB x9
x1352
x453

23. Get the rainfall observed in a particular
hour from all stations01
02
SRBENCH QUERYRESULTS
Q01 with an optional clause
on unit of measure
x5
S2S
S
TDB GraphDB
Ontop Morph
x3
x13
x4k
x2
x4
x4
x5k

24. 03
04
05
Detect if a hurricane has been observed
Get the average wind speed at the stations
where the air temperature is >32
Join between wind observation and temperature
observation subtrees time-consuming in low resource
environment (Raspberry Pi)
Detect if a station is observing a blizzard
x3
x6
x6
x88
x3
x3

25. 06
07
08
Get the stations with extremely low visibility
Detect stations that are recently broken
Get the daily minimal and maximal air
temperature observed by the sensor at a
given location
x2
x14
x4
x6
x6
x5
x2

26. 09
10
Get the daily average wind force and direction
observed by the sensor at a given location
Get the locations where a heavy snowfall has
been observed
Our Approach (s2s) is shown to be faster on all queries
in the Distributed Meteorological System with SRBench
Join between wind force and wind direction observation
subtrees is time-consuming in low resource
environment (Raspberry Pi)
x3
x3k
x2
x7

27. Temperature aggregated by hour on a
specified day01
02
SMARTHOME RESULTS
Minimum and maximum temperature
each day for a particular month
S2S TDB GraphDB
x7
x29
x3
x9

28. 03
04
Energy Usage Per Room By Day
Diagnose unattended appliances consuming
energy with no motion in room
Our Approach (s2s) is shown, once again, to be faster on
all queries for Smart Home Analytics
Involves motion and meter data (much larger set), with
space-time aggregations and joins between motion and
meter tables/subgraphs.
Involves meter data (larger set), with space-time
aggregations.
x69
x13
x4

29. RDF STREAMPROCESSING
sparql2stream
Same engine and
mappings but translates
to EPL instead of SQL
TRANSLATE
QUERY
2
Stream Window
SPARQL query specifying
stream window size
REGISTER
QUERY
1
Stream Sockets
Supports multiple
platforms and streams
with ZeroMQ
STREAMDATA
3
Real-time analytics
RECEIVE PUSH
RESULTS
4

30. STREAMPROCESSING EFFICIENCY
SMART HOME BENCHSRBench
100to
106
100to
200
CQELS
Performance Improvement Over
Le-Phuoc, D., et al. (2011) "A native and adaptive approach for unified processing of
linked streams and linked data.” The 10th International Semantic Web Conference.
VELOCITY
>99% <1ms latency increasing from 1 to 1000 rows/ms
VOLUME
33.5million rows, projected ~2.5 billion triples!
SCALABILITY

31. PERSONAL IOT REPOSITORY
Siow, E., Tiropanis, T. and Hall, W. (2016) PIOTRe: Personal Internet of Things Repository: The 15th International Semantic Web Conference P&D
github.com/eugenesiow/piotresparql2streamsparql2sql github.com/eugenesiow/sparql2sql
PIOTRE
Apps
sparql2stream
sparql2sql
Metadata

32. FOG RSP
Siow, E., Tiropanis, T. and Hall, W. (2017) A Fog Computing Framework for RDF Stream Processing.
Sensors
Node
Data Stream
Broker
Subscribe(URI_1)
Client
Publish ([Query_p1,Q_p2])𝞹
Push (Select_Stream),
Access Control,
Bandwidth Control
Inverted pub-sub
Query Broadcast, Nodes manage distributed processing
WORKLOAD DISTRIBUTION
No single point of failure. Any RPi can serve
as a broker. ‘Best effort’ for source nodes
ResultSet

33. MITIGATING CYBER-SOCIALDISASTERS
LESS
DEPENDENCY
ON CLOUD
MORE ROBUST
REPOS FORFOG
COMPUTING
HUMAN STILL
VUNERABLE
GOOD UI,
SECURITYBY
DEFAULT
What are your latency-sensitive, security/privacy-sensitive, or
geographically constrained applications & scenarios?

34. “Until they become conscious they will never rebel and until after
they have rebelled they cannot become conscious.”
1984 by George Orwell
@eugene_siow