Machine Learning for IoT and Telecoms

Copyright : 0 Futuretext Ltd. London
Future trends
IoT and Machine Learning
@ajitjaokar
ajit.jaokar@futuretext.com

Ajit Jaokar
-
Machine Learning for IoT and Telecoms
futuretext applies machine learning techniques to complex problems in the
IoT (Internet of Things) and Telecoms domains.
We aim to provide a distinct competitive advantage to our customers through
application of machine learning techniques
Philosophy:
Think of NEST. NEST has no interface. It’s
interface is based on ‘machine learning’ i.e. it
learns and becomes better with use. This will be
common with ALL products and will determine
the competitive advantage of companies. Its a
winner takes all game! Every product will have a
‘self learning’ interface/component and the
product which learns best will win!

Ajit Jaokar
-
www.futuretext.com
@AjitJaokar

Ajit Jaokar
-
Data is the new oil ...

Ajit Jaokar
-
The meek shall inherit the earth .. BUT not it’s mineral rights!
Data is out there and is free (Open data). It provides no competitive advantages.
Finding patterns in data is the holy grail (the oil!)

Copyright : Futuretext Ltd. London
Ajit Jaokar
-
Source: MIT / Smithsonian
http://www.smithsonianmag.com/innovation/better-traffic-light-timing-will-get-you-
there-faster-180952123/
a) State of Play b) IoT c) Machine Learning d) What is unique with IoT and Machine
Learning

Ajit Jaokar
-
www.opengardensblog.futuretext.com
World Economic Forum
Spoken at MWC(5 times), CEBIT, CTIA, Web 2.0,
CNN, BBC, Oxford Uni, Uni St Gallen, European
Parliament. @feynlabs – teaching kids Computer
Science. Adivsory – Connected Liverpool

Image source: Guardian
Image source: Guardian

Ajit Jaokar
IOT - THE INDUSTRY- STATE OF PLAY

Ajit Jaokar
State of play - 2014
• Our industry is exciting – but mature - Now a two horse race for
devices with Samsung around 70% of Android
• Spectrum allocations and ‘G’ cycles are predictable - 5G around 2020
• 50 billion connected devices by 2020
• ITU world Radio communications Conference, November 2015.
• IOT has taken off .. not because of EU and Corp efforts – but because of
Mobile, kickstarter, health apps and iBeacon and ofcourse NEST(acquired
by Google)

Ajit Jaokar
Stage One: Early innovation 1999 - 2007
Regulatory innovation – net neutrality - Device innovation (Nokia
7110 and Ericsson t68i) - Operator innovation (pricing, bundling,
Enterprise) - Connectivity innovation (SMS, BBM)
Content innovation (ringtones, games, EMS, MMS) - Ecosystem
innovation (iPhone)
Stage two: Ecosystem innovation - iPhone and
Android (2007 – 2010)
Social innovation - Platform innovation - Community
innovation - Long tail innovation - Application
innovation

Ajit Jaokar
Phase three: Market consolidation – 2010 - 2013
And then there were two ...
Platform innovation and consolidation
Security innovation
App innovation
Phase four – three dimensions – 2014 ..
Horizontal apps (iPhone and Android)
Vertical (across the stack) – hardware, security, Data
Network – 5G and pricing

Ajit Jaokar
Many of the consumer IOT cases will happen with iBeacon in the next
two years

Ajit Jaokar
And 5G will provide the WAN connectivity 5G - Source – Ericsson

Ajit Jaokar
Samsung Gear Fit named “Best Mobile Device” of Mobile World
Congress
Notification or Quantification? – Displays (LED, e-paper,
Mirasol, OLED and LCD) - Touchscreen or hardware controls? -
Battery life and charging

Ajit Jaokar
Hotspot 2.0

Ajit Jaokar
Three parallel ecosystems
IOT is connecting things to the Internet – which is not the same as
connecting things to the cellular network!
The difference is money .. and customers realise it
IOT local/personal (iBeacon, Kickstarter, Health apps)
M2M – Machine to Machine
IOT – pervasive(5G, Hotspot 2.0)
Perspectives
• 2014 – 2015(radio conf) – 2020(5G, 2020)
• 2014 – iBeacon (motivate retailers to open WiFi)
• Hotspot 2.0 – connect cellular and wifi worlds
• Default wifi and local world?
• Operator world – (Big)Data, Corporate, pervasive apps – really happen
beyond 2020
• So 5G will be timed well. The ecosystems will develop and they will be
connected by 5G

Ajit Jaokar
IOT – INTERNET OF THINGS

As the term Internet of Things implies (IOT) – IOT is about Smart
objects
For an object (say a chair) to be ‘smart’ it must have three things
- An Identity (to be uniquely identifiable – via iPv6)
- A communication mechanism(i.e. a radio) and
- A set of sensors / actuators
For example –
the chair may have a pressure sensor indicating that it is occupied
Now, if it is able to know who is sitting – it could co-relate more data by
connecting to the person’s profile
If it is in a cafe, whole new data sets can be co-related (about the venue,
about who else is there etc)
Thus, IOT is all about Data ..
IoT != M2M (M2M is a subset of IoT)

Sensors lead to a LOT of Data (relative to mobile) .. (source David
wood blog)
By 2020, we are expected to have 50 billion connected devices
To put in context:
The first commercial citywide cellular network was launched in Japan by NTT
in 1979
The milestone of 1 billion mobile phone connections was reached in 2002
The 2 billion mobile phone connections milestone was reached in 2005
The 3 billion mobile phone connections milestone was reached in 2007
The 4 billion mobile phone connections milestone was reached in February
2009.
Gartner: IoT will unearth more than $1.9 trillion in revenue before 2020; Cisco thinks
there will be upwards of 50 billion connected devices by the same date; IDC estimates
technology and services revenue will grow worldwide to $7.3 trillion by 2017 (up
from $4.8 trillion in 2012).

So, 50 billion by 2020 is a large number
Smart cities can be seen as an application domain of IOT
In 2008, for the first time in history, more than half of the world’s
population will be living in towns and cities.
By 2030 this number will swell to almost 5 billion, with urban growth
concentrated in Africa and Asia with many mega-cities(10 million +
inhabitants).
By 2050, 70% of humanity will live in cities.
That’s a profound change and will lead to a different management approach
than what is possible today
Also, economic wealth of a nation could be seen as – Energy +
Entrepreneurship + Connectivity (sensor level + network level +
application level)
Hence, if IOT is seen as a part of a network, then it is a core component of
GDP.

Ajit Jaokar
Machine Learning

What is Machine Learning?
Mitchell's Machine Learning
Tom Mitchell in his book Machine Learning “The field of machine learning is c
oncerned with the question of how to construct computer
programs that automatically improve with experience.”
formally:
“A computer program is said to learn from experience E with respect to
some class of tasks T and performance measure P, if its performance at
tasks in T, as measured by P, improves with experience E.”
Think of it as a design tool where we need to understand:
What data to collect for the experience (E)
What decisions the software needs to make (T) and
How we will evaluate its results (P).
A programmers perspective:
Machine Learning involves:
a) Training of a model from data
b) Predicts/ Extrapolates a decision
c) Against a performance measure.

What Problems Can Machine Learning Address? (source Jason
Brownlee)
● Spam Detection:
● Credit Card Fraud Detection
• Digit Recognition:
● Speech Understanding:
● Face Detection:
• Product Recommendation:
● Medical Diagnosis:
● Stock Trading:
• Customer Segmentation
• Shape Detection
.

Types of Problems
•Classification: Data is labelled meaning it is assigned a class, for example
spam/nonspam or fraud/nonfraud. The decision being modelled is to
assign labels to new unlabelled pieces of data. This can be thought of as a
discrimination problem, modelling the differences or similarities between groups.
•Regression: Data is labelled with a real value rather
than a label. Examples that are easy to understand are time series data like the price of
a stock over time. The decision being modelled is the relationships between
inputs and outputs.
Clustering: Data is not labelled, but can be divided into groups based on
similarity and other measures of natural structure in the data.
An example from the above list would be organising pictures by faces without
names, where the human user has to assign names to groups, like iPhoto on the Mac.
●Rule Extraction: Data is used as the basis for the extraction of
propositional rules (antecedent/consequent or ifthen).
Often necessary to work backwards from a Problem to the algorithm and then work with
Data. Hence, you need a depth of domain experience and also algorithm experience

What Algorithms Does Machine Learning Provide?
Regression
Instance-based Methods
Decision Tree Learning
Bayesian
Kernel Methods
Clustering methods
Association Rule Learning
Artificial Neural Networks
Deep Learning
Dimensionality Reduction
Ensemble Methods

Ajit Jaokar

Basic idea of machine learning is to build a mathematical model based on
training data(learning stage) – predict results for new data(prediction
stage) and tweak the model based on new conditions
What type of model? Predicitive, Classification, Clustering, Decision
Oriented, Associative
 On one hand - IoT creates a lot of contextual data which complements existing
processes
 On the other hand – the Sheer scale of IoT calls for unique solutions
Types of problems:
• Apply existing Machine Learning algorithms to IoT data
• Use IoT data to complement existing processes
• Use the scale of IoT data to gain new insights
• Consider some unique characteristics of IoT data (ex streaming)
28

IoT : from traditional computing to ..
Gone from making Smart things smarter(traditional computing) to
a) Making dumb things smarter .. and
b) living things more robust
3 Domains:
Consumer, Enterprise, Public infrastructure
1) Consumer – bio sensors(real time tracking), Quantified self – focussing on
benefits
2) Enterprise – Complex machinery (preventative maintenance), asset efficiency –
reducing assets, increasing efficiency of existing assets. More from transactions to
relationships(real time context awareness).
3) Public infrastructure(Dynamically adjust traffic lights). Dis-economies of
scale(bad things also scale in cities) – Thanks John Hagel III

Three key areas:
a) Move from exception handling to patterns of exceptions over time.(are
some exceptions occurring repeatedly? Do I need to redsign my product, Is that a
new product?) –
b) Move from optimization to disruption – ownership to rental ship (Where are all
these dynamic assets?)
c) Move to self learning: Robotics: From assembly line to self learning
robots(Boston Dynamics), autonomous helicopters
Four examples of differences:
Sensor fusion - Deep Learning - Real time - Streaming

Sensor fusion
 Sensor fusion is the combining of sensory data or data derived from
sensory data from disparate sources such that the resulting information
is in some sense better than would be possible when these sources were
used individually. The data sources for a fusion process are not specified
to originate from identical sensors. Sensor fusion is a term that covers a
number of methods and algorithms, including: Central Limit Theorem,
Kalman filter, Bayesian networks, Dempster-Shafer
Example: http://www.camgian.com/ http://www.egburt.com/
31

Deep learning
 Google's acquisition of DeepMind Technologies
 In 2011, Stanford computer science professor Andrew Ng founded
Google’s Google Brain project, which created a neural network trained
with deep learning algorithms, which famously proved capable
of recognizing high level concepts, such as cats, after watching just
YouTube videos--and without ever having been told what a “cat” is.
 A smart-object recognition algorithm that doesn’t need humans
http://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humans
A feature construction method for general object recognition (Kirt Lillywhite,
Dah-JyeLee n, BeauTippetts, JamesArchibald)
32

Real time:
Beyond ‘Hadoop’ (non hadoopable) the BDAS stack
BDAS Berkeley data analytics stack
Spark – an open source, in-memory, cluster computing framework.
Integrated with Hadoop(can work with files stored in HDFS)
Written in Scala

Real time (Stream processing)

http://jvns.ca/blog/2014/06/19/machine-learning-isnt-kaggle-competitions/
Machine learning isn't Kaggle competitions
Kaggle(a site where you compete to solve machine learning problems)
Understand the business problem
If you want to predict flight arrival times, what are you really trying to do?
Some possible options:
• Help the airline understand which flights are likely to be delayed, so they
can fix it.
• Help people buy flights that are less likely to be delayed.
• Warn people if their flight tomorrow is going to be delayed

• How accurate does my model really need to be? What kind of false
positive rate is acceptable?
• What data can I use? If you’re predicting flight days tomorrow, you can
look at weather data, but if someone is buying a flight a month from now
then you’ll have no clue.
Choose a metric to optimize
Let’s take our flight delays example. We first have to decide
whether to do classification (“will this flight be delayed for at least
an hour”) or regression (“how long will this flight be delayed for?”).

Decide what data to use
Let’s say I already have the airline, the flight number, departure airport,
plane model, and the departure and arrival times.
Should I try to buy more specific information about the different plane
models (age, what parts are in them..)? Really accurate weather data? The
amount of information available to you isn’t fixed! You can get more!
Clean up your data
Once you have data, your data will be a mess. In this flight search
example, there will likely be: airports that are inconsistently named -
missing delay information all over the place - weird date formats - trouble
reconciling weather data and airport location

Clean up your data
Once you have data, your data will be a mess. In this flight search example,
there will likely be
• airports that are inconsistently named
• missing delay information all over the place
• weird date formats
• trouble reconciling weather data and airport location
Build a model!
Put your model into production
Measure your model’s performance

Ajit Jaokar
-
Source: MIT / Smithsonian
http://www.smithsonianmag.com/innovation/better-traffic-light-timing-will-get-you-
there-faster-180952123/
“Standard” optimization approaches minimize costs while meeting demand
• Additional environmental objectives – Minimize carbon footprint – Meet pollution
reduction targets • Additional challenge – capturing uncertainty, such as: Population
growth and urban dynamics – Rainfall – Renewable energy sources
– Energy costs Types of decisions: • The routes to create or expand • The combination
of transport modes • The capacity of each route “How sensitive is the investment plan
to Population growth (“How will a 10% increase in population affect our carbon
footprint?”)?”

Ajit Jaokar
-
www.futuretext.com
@AjitJaokar

Machine Learning for IoT and Telecoms

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a Machine Learning for IoT and Telecoms

Semelhante a Machine Learning for IoT and Telecoms (20)

Último

Último (20)

Machine Learning for IoT and Telecoms