Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.

1. Mehr und schneller
ist nicht
automatisch besser
Dr. Boris Adryan
@BorisAdryan

2. Dr. Boris Adryan
• with Zühlke Engineering since September 2016
• longstanding IoT enthusiast
• Founder of thingslearn Ltd.
• Board Member & Strategic Advisor for Pycom
(microcontrollers), BioSelf (biosensors) and
OpenSensors (IoT platform)
• before: research group leader for data analytics
and machine learning at University of
Cambridge, England
@BorisAdryan

3. Vision 2020
Planung,
Umsetzung,
Betrieb
Ihr Partner für
Business Innovation
Vernetzte System,
neue Geschäftsmodelle

4. 39% of survey participants
are worried about the cost
of an industrial IoT
solution.
“Why aren’t you doing IoT?”

5. IoT cost expectations
many sensors +
complicated analytics +
expensive infrastructure
——————————————
IoT has little benefit
“…because my data scientist said the more the better ”

6. peanuts:
“a spoon full”
How many peanuts are that on average?
0 50 100
“on average”
3 samples

7. Do I get more peanuts at Maxie Eisen
or at Logenhaus?
0 50 100
“on average”
Maxie Eisen 3 samples
“on average”
Logenhaus

8. 0 50 100
4 samples
Do I get more peanuts at Maxie Eisen
or at Logenhaus?
“on average”
Maxie Eisen
“on average”
Logenhaus

9. 0 50 100
n samples
statistical power through
large numbers of samples
deviation
Do I get more peanuts at Maxie Eisen
or at Logenhaus?
“on average”
Maxie Eisen
“on average”
Logenhaus

10. Statisticians and data scientists LOVE
larger sample sizes!
…but if sampling costs time and resources, we need a
compromise.

11. Zühlke Data Analytics Framework

12. precision and accuracy
that can be achieved
theoretically
Sampling strategy
precision and accuracy
that is needed to get
a job done
accurate
and precise
not accurate,
but precise
accurate,
not precise
not what
you want

13. • how to cut down on
hardware costs
• how to cut down on
software costs
Sweetening IoT for your customer
A few recommendations from the trenches:
many sensors +
complicated analytics +
expensive infrastructure
——————————————
IoT has little benefit
less
reasonable

14. Westminster Parking Trial
https://www.westminster.gov.uk/new-trial-improve-conditions-disabled-drivers
IoT solution
Service company
~750 independent parking
lots with a total of
>3,500 individual spaces
access to

15. Humans don’t scale that well…
labour:
expensive
sensor:
cheap
While the cost of the sensors is falling (and follows Moore’s
Law), digging them in and out for deployment and
maintenance is a significant cost factor.

16. Can we learn an optimal
deployment and sampling pattern?
•sampling rate of 5-10 min
•data over 2 weeks in May 2015
•overall 2.6 million data points
Can we make customers’ budget go further by
• reducing the number of sensors in a geographic area?
• lowering the sampling rate for better battery life?

17. A quick glimpse into the raw data

18. Correlation and clustering
0
5
10
15
20
0 3 6 9 12
“correlated”
0
5
10
15
20
0 3 6 9 12
“anti-correlated”
0
5
10
15
20
0 3 6 9 12
“independent”
lorry
coach
car
bike
skateboard
hierarchical clustering on
the basis of a feature matrix

19. Good news: temporal occupancy
pattern roughly predicts neighbours
lots in Southampton
lots around
the corner of
each other
750 parking lots

20. A caveat: Is a high-degree of correlation
a function of parking lot size?
finding two lots of 20
spaces that correlate
finding two lots of 3
spaces that correlate
0:00 12:00 23:59
0:00 12:00 23:59
“more likely”
“less likely”

21. Bootstrapping in DBSCAN clusters
Simulation: Swap the occupancy vectors between parking
lots of similar size and test per grid cell if these lots still
correlate

22. What makes a good spatial cluster?

23. Density-Based Spatial Clustering of
Applications with Noise (DBSCAN)
https://en.wikipedia.org/
wiki/DBSCAN#/media/
File:DBSCAN-Illustration.svg
2 parameters:
epsilon (distance)
minPoints (in cluster)
A - core points
B, C - corner points
N - noise point

24. Stratification strategy
3 lots with cc > 0.5
2 spaces
4 spaces
4 spaces
Test:
1. Take occupancy profile of
ONE random 2-space parking
lot and TWO random 4-space
parking lots.
2. Determine cc.
3. Repeat n times and get a cc
distribution for that parking lot
combination.

25. Combining stats with street knowledge

26. Verdict: In some grid cells the level of the occupancy of
one parking lot predicts the occupancy of most parking
spaces.
x
x
x
x
x
x
x
x
x x x
x
x
x
x
x
Better for navigation
We suggested that about 60% of the
sensors may be sufficient.
Better predictive power

27. Suggested technology for trials
A temporary survey would have allowed us to make
the same recommendation, including the insight that
the provided 5’ resolution is probably not required.

28. • how to cut down on
hardware costs
• how to cut down on
software costs
Sweetening IoT for your customer
A few recommendations from the trenches:
many sensors +
complicated analytics +
expensive infrastructure
——————————————
IoT has little benefit
less
reasonable

29. My current pet hate: Deep Learning
Deep learning has delivered impressive
results mimicking human reasoning,
strategic thinking and creativity.
At the same time, big players
have released libraries such
that even ‘script kiddies’ can
apply deep learning.
It’s already leading to unreflected use
of deep learning when other methods
would be more appropriate.

30. “I need to do real-time analytics!”
microseconds
to seconds
seconds to
minutes
minutes
to hours
hours to
weeks
on
device
on
stream
in batch
am I falling?
counteract
battery level
should I land?
how many
times did I
stall?
what’s the best
weather for
flying?
in process
in database
operational insight
performance insight
strategic insight
e.g. Kalman filter
e.g. with machine learning
e.g. rules engine
e.g. summary stats

31. Can IoT ever be real-time?
zone 1:
real-time
[us]
zone 2:
real-time
[ms]
zone 3:
real-time
[s]

32. Edge, fog and cloud computing
Edge
Pro:
- immediate compression from raw
data to actionable information
- cuts down traffic
- fast response
Con:
- loses potentially valuable raw data
- developing analytics on embedded
systems requires specialists
- compute costs valuable battery life
Cloud
Pro:
- compute power
- scalability
- familiarity for developers
- integration centre across
all data sources
- cheapest ‘real-time’
option
Con:
- traffic
Fog
Pro:
- same as Edge
- closer to ‘normal’ development work
- gateways often mains-powered
Con:
- loses potentially valuable raw data

33. Some of our examples for
real-time analytics
Choosing the appropriate
method and toolset on
every level.

34. Options for real-time in cloud
some features can cost a bit, especially
when you don’t really know what
you’re doing and want to ‘try it out’.
a badly configured
SMACK stack on your
own commodity
hardware can be slow
and unreliable
your pre-trained
classifier

35. Dr. Boris Adryan
@BorisAdryan
‣ Preliminary surveys and data analysis can help to
minimise the number of sensors and develop an
optimal deployment strategy and sampling schedule.
‣ Super-fast analytics and state-of-the-art methods are
not automatically the most useful solution.
‣ A good understanding on the type of insight that is
required by the business model is essential.
Zühlke can advise on options around IoT and
data analytics, and provide complete
solutions where needed.
Summary