Patching Mr Robot: Mitigating IoT-Related Cyber-social Disasters by getting Fog Computing to work

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

Raspberry Pi Thermostat Hack
HVAC Hack
Wipe Debts
Jailbreak
Grand Theft Auto
Smart Home Hack
DDOS
72°F
200°F
Smart Home 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

Resource constrained sensors
& devices might be and
unable to store, process or
implement appropriate
security.
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.
“The IoT suffers from a lack of
interoperability… developers
are faced with data silos, high
costs and limited market
potential.” – W3C Web of
Things
Can we trust vendors to keep
data private and secure on
public clouds? Encrypting the
data increases processing
required and decreases
interoperability.
Internet based transmissions
may increase the probability
of information leakage.
Internet access may be
unavailable, unreliable, and
slow e.g. natural disasters,
poor infrastructure, remote
areas.

Fog Computing utilises the space between the
“Ground” and “Cloud”
Irrigation Application
Soil Moisture
Analytics
Lightweight
Computer Hub
Data Stream
Environmental
Sensors
National Disaster Monitoring Application
Weather
Data
State Inclement
Weather Planning
Application
Distributed Queries

Building ”Pillars” to support Fog Computing
Sustainable & Secure
Linked Data
Faster Queries
eugenesiow.github.io/iot

Buil-Aranda, C., Hogan, A.: SPARQL Web-Querying Infrastructure: Ready for Action?
ISWC 2013
Semantic Sensor Ontology
Smart City Ontology
GeoNames Ontology

{
timestamp : 1467673132,
temperature : {
max: 22.0,
min: 15.0,
current: 17.0,
error: {
percentage: 5.0
}
}
}
{
timestamp : 1467673132,
temperature : 32.0,
wind_speed : 10.5,
pressure : 1016
}
UNIQUE DEVICES
dweet.io
FLAT SCHEMATA COMPLEX SCHEMATA
{
timestamp : 1467673132,
temperature : 32.0,
humidity : 10.5,
pressure : 1016,
light: 120.0,
}
1
2
3
4

produces
produces
located
produces
has value
unit
time
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

produces
produces
located
produces
has value
time
unit
has value
time
unit
has value
time
unit

produces
located
unit
13.0 93.0 10.52016-01-01 06:00:00

produces
located
unit
13.0 93.0 10.52016-01-01 06:00:00
has value has value has value

produces
loc
unit
13.0 93.02016-01-01 06:00:00
has value has va
a
has value
has unit
{
}
𝞹
𝞬
a
has value
has unit
BGP

13.0
has value
a
has value
has unit
{
}
𝞹
𝞬
a
has value
has unit
BGP

a
has value
has unit
{
}
𝞹
𝞬
a
has value
has unit
BGP

a
has value
has unit
{
}

~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.
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
Morph

Get the rainfall observed in a particular
hour from all stations
Q01 with an optional clause
on unit of measure
x5
x3
x13
x4k
x2
x4
x4
x5k

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

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

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

Temperature aggregated by hour on a
specified day
Minimum and maximum temperature
each day for a particular month
x7
x29
x3
x9

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

sparql2stream
Same engine and
mappings but translates
to EPL instead of SQL
2
Stream Window
SPARQL query specifying
stream window size
1
Stream Sockets
Supports multiple
platforms and streams
with ZeroMQ
3
Real-time analytics
4

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.
>99% <1ms latency increasing from 1 to 1000 rows/ms
33.5million rows, projected ~2.5 billion triples!

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
Apps
sparql2stream
sparql2sql
Metadata

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
Query Broadcast, Nodes manage distributed processing
No single point of failure. Any RPi can serve
as a broker. ‘Best effort’ for source nodes
ResultSet

What are your latency-sensitive, security/privacy-sensitive, or
geographically constrained applications & scenarios?

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

Patching Mr Robot: Mitigating IoT-Related Cyber-social Disasters by getting Fog Computing to work

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Patching Mr Robot: Mitigating IoT-Related Cyber-social Disasters by getting Fog Computing to work