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getting Fog Computing to work
PATCHING MR. ROBOT
Mitigating IoT-related Cyber-Social-Disasters by
EUGENE SIOW
A hit TV-Series portraying realistic hacking and bleeding-edge technology
fsociety E CORP
Raspberry Pi Thermostat Hack
PROGRESSION OF HACKS
HVAC Hack
Wipe Debts
Jailbreak
Grand Theft Auto
Smart Home Hack
DDOS
72°...
SMARTHOME HACK
WHATAM I SUPPOSEDTO DO?
NOTHINGISWORKING
UNPLUGWHAT?
EVERYTHING ISINSIDETHE WALLS
INSTEON HACK
NO OR DEFAULT
USERNAME & PASSWORD
FROM A NOW DISCONTINUED
INSTEON PRODUCT
CIRCUMVENT PASSWORD
BY GOING DIRECT...
Resource constrained sensors
& devices might be and
unable to store, processor
implement appropriate
security.
DEVICECONST...
APPLE
GOOGLE
HONEYWELL
CISCO
HUAWEI
GENERALELECTRIC
IBM
AMAZON
INTEL
LET’S TALKFOG COMPUTING
MICROSOFT
A REAL-WORLD
FOGCOMPUTINGINFRASTRUCTURE
Fog Computing utilises the space between the
“Ground” and “Cloud”
Irrigation Appli...
OUR RESEARCH
Building ”Pillars”to support Fog Computing
Sustainable & Secure
INTEROPERABILITY
DISTRIBUTION
EFFICIENCY
Link...
INTRODUCING
LINKED DATA
FOR INTEROPERABILITY
URI andontologies
Establish commondata structures& References
ENABLES RICH ME...
THE SHAPE OFIOT TIME-SERIES DATA
{
timestamp : 1467673132,
temperature : {
max: 22.0,
min: 15.0,
current: 17.0,
error: {
p...
EFFICIENTQUERIESWITH
TIME-SERIES
DATA
THING
TEMPERATURE OBS
HUMIDITY OBS
WIND SPEED OBS
13.0
2016-01-0106:00:00
CELCIUS
93...
THING
TEMPERATURE OBS
HUMIDITY OBS
WIND SPEED OBS
13.0
LOCATION
produces
produces
located
produces
has value
THING
THING
T...
OUR
APPROACH
EFFICIENTQUERIESWITH
TIME-SERIES
DATA
THING
TEMPERATURE OBS WIND SPEED OBS
CELCIUS PERCENT MPH
LOCATION
produ...
DESIGNING OURENGINE
THING
TEMPERATURE OBS WIND SPEED OBS
CELCIUS PERCENT MPH
LOCATION
produces
located
HUMIDITY OBS
unit
T...
DESIGNING OURENGINE
THING
TEMPERATURE OBS WIND SPEED OBS
CELCIUS PERCENT MPH
LOCATION
produces
located
HUMIDITY OBS
unit
T...
DESIGNING OURENGINE
THING
TEMPERATURE OBS
CELCIUS PERCENT
produces
loc
HUMIDITY OBS
unit
TEMPERATURE HUMID
13.0 93.0
TIME
...
DESIGNING OURENGINE
TEMPERATURE OBS
CELCIUS
TEMPERATURE
13.0
TABLE1.TEMPERATURE
has value
MAX( )?TEMPERATURESELECT
?OBS TE...
SPARQL
DESIGNING OURENGINE
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}...
SPARQL
DESIGNING OURENGINE
MAX( )?TEMPERATURESELECT
?OBS TEMPERATURE OBSa
has value?OBS ?TEMPERATURE
has unit?OBS ?uom
{
}...
EVALUATIONWITH BENCHMARKS
SRBENCH
~20,000 Stations
100 – 300k triples
Wind, Rainfall, etc.
10 SRBench Queries
Zhang, Y, et...
STORAGESIZE
3ook
HurricaneIke
1ook
NEVADABLIZZARD
3ok
SMARTHOME
OUR APPROACH(s2S)
TDB
x15
x68
x112
GraphDB x9
x1352
x453
Get the rainfall observed in a particular
hour from all stations01
02
SRBENCH QUERYRESULTS
Q01 with an optional clause
on ...
03
04
05
Detect if a hurricane has been observed
Get the average wind speed at the stations
where the air temperature is >...
06
07
08
Get the stations with extremely low visibility
Detect stations that are recently broken
Get the daily minimal and...
09
10
Get the daily average wind force and direction
observed by the sensor at a given location
Get the locations where a ...
Temperature aggregated by hour on a
specified day01
02
SMARTHOME RESULTS
Minimum and maximum temperature
each day for a pa...
03
04
Energy Usage Per Room By Day
Diagnose unattended appliances consuming
energy with no motion in room
Our Approach (s2...
RDF STREAMPROCESSING
sparql2stream
Same engine and
mappings but translates
to EPL instead of SQL
TRANSLATE
QUERY
2
Stream ...
STREAMPROCESSING EFFICIENCY
SMART HOME BENCHSRBench
100to
106
100to
200
CQELS
Performance Improvement Over
Le-Phuoc, D., e...
PERSONAL IOT REPOSITORY
Siow, E., Tiropanis, T. and Hall, W. (2016) PIOTRe: Personal Internet of Things Repository: The 15...
FOG RSP
Siow, E., Tiropanis, T. and Hall, W. (2017) A Fog Computing Framework for RDF Stream Processing.
Sensors
Node
Data...
MITIGATING CYBER-SOCIALDISASTERS
LESS
DEPENDENCY
ON CLOUD
MORE ROBUST
REPOS FORFOG
COMPUTING
HUMAN STILL
VUNERABLE
GOOD UI...
“Until they become conscious they will never rebel and until after
they have rebelled they cannot become conscious.”
1984 ...
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Patching Mr Robot: Mitigating IoT-Related Cyber-Social-Disasters by getting Fog Computing to Work

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

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Patching Mr Robot: Mitigating IoT-Related Cyber-Social-Disasters by getting Fog Computing to Work

  1. 1. getting Fog Computing to work PATCHING MR. ROBOT Mitigating IoT-related Cyber-Social-Disasters by EUGENE SIOW
  2. 2. A hit TV-Series portraying realistic hacking and bleeding-edge technology fsociety E CORP
  3. 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. 4. SMARTHOME HACK WHATAM I SUPPOSEDTO DO? NOTHINGISWORKING UNPLUGWHAT? EVERYTHING ISINSIDETHE WALLS
  5. 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. 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. 7. APPLE GOOGLE HONEYWELL CISCO HUAWEI GENERALELECTRIC IBM AMAZON INTEL LET’S TALKFOG COMPUTING MICROSOFT
  8. 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. 9. OUR RESEARCH Building ”Pillars”to support Fog Computing Sustainable & Secure INTEROPERABILITY DISTRIBUTION EFFICIENCY Linked Data Faster Queries eugenesiow.github.io/iot
  10. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22. STORAGESIZE 3ook HurricaneIke 1ook NEVADABLIZZARD 3ok SMARTHOME OUR APPROACH(s2S) TDB x15 x68 x112 GraphDB x9 x1352 x453
  23. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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