Predictive Analytics as a service for IoT


This post is a personal viewpoint based on my teaching (IoT and Machine Learning) at the City sciences program at UPM in Madrid – Technical University of Madrid and at Oxford University (with a mobile perspective).

Predictive Analytics are critical for IoT, but most companies do not have the skillsets to develop their own Predictive analytics engine.  The objective of this effort is to provide a predictive analytics interface for Hypercat. We aim to provide a solution accessed through a Hypercat API and a library. Whenever possible, we will use Open Source. We will also encapsulate industry best practices into the solution. The post is also related to extending the discussions at the event Smart cities need a Trusted IoT foundation

Data and Analytics will be the key differentiator for IoT.

A single sensor collecting data at one-second intervals will generate 31.5 million datapoints year (source Intel/WindRiver). However, the value lies not just in one sensor’s datapoints – but rather the collective intelligence gleaned for thousands (indeed millions) of sensors working together

As I discuss below, this information (and more specifically the rate of IoT based sensor information and its real time nature) will make a key difference for IoT and Predictive analytics.

IoT and predictive analytics will change the nature of decision making and will change the competitive landscape of industries. Industries will have to make thousands of decisions in near real-time. With predictive analytics, each decision will improve the model for subsequent decisions (also in near real time). We will recognize patterns, make adjustments and improve performance based on data from multiple people and sensors

IoT and Predictive analytics will enable devices to identify, diagnose and report issues more precisely and quickly as they occur. This will create a ‘closed loop’ model where the Predictive model improves with experience. We will thus go from identifying patterns to making predictions – all in real time  

However, the road to this vision is not quite straight forward. The two worlds of IoT and Predictive analytics do not meet easily

Predictive analytics needs the model to be trained before the model makes a prediction. Creating a model and updating it on a continuous real-time basis with streaming IoT data is a complex challenge. Also, it does not fit in the traditional model of map reduce and it’s inherently batch processing nature. This challenge is being addressed already (Moving Hadoop beyond batch processing and MapReduce) but will become increasingly central as IoT becomes mainstream.


IoT and Predictive analytics – opportunities

For IoT and Predictive analytics, processing will take place both in the Cloud but also more to the edge. Not all data will be sent to the Cloud at all times. The newly launched Egburt from Camgian microsystems is an example of this new trend.  Some have called this trend ‘Data gravity’ where computing power is brought to the data as opposed to processing Data in a centralized location.

In addition, the sheer volume of IoT data leads to challenges and opportunities. For example 100 million points per second in a time series is not uncommon. This leads to specific challenges for IoT (Internet of Things – time series data challenge)

Here are some examples of possible opportunities for IoT and Predictive analytics where groups of sensors work together:

  • We could undertake system wide predictive maintenance of offshore equipment like wind farms for multiple turbines (i.e. the overall system as opposed to a specific turbine).  If we predict a high likelihood of failure in one turbine, we could dynamically reduce the load on that turbine by switching to a lower performance.
  • Manage overall performance of a group of devices – again for the wind farm example – individual turbines could be tuned together to achieve optimal performance where individual pieces of equipment have an impact on the overall performance
  • Manage the ‘domino effect’ of failure – as devices are connected (and interdependent) – failure of one could cascade across the whole network. By using predictive analytics – we could anticipate such cascading failure and also reduce its impact

IoT and Predictive analytics – challenges

Despite the benefits, the two worlds of IoT and Predictive analytics do not meet very naturally

In a nutshell, Predictive analytics involves extracting information from existing data sets to identify patterns which help predict future outcomes and trends for new (unseen) scenarios.  This allows us to predict what will happen in future with an acceptable level of reliability.

To do this, we must

a)      Identify patterns from existing data sets

b)      Create a model which will predict the future


Doing these two steps in Real time is a challenge. Traditionally, data is fed to a system in a batch. But for IoT, we have a continuous stream of new observations in real time. The outcome (i.e. the business decision) also has to be made in real time. Today, some systems like Credit card authorization perform some real time validations – but for IoT, the scale and scope will be much larger.


So, this leads to more questions:

a)      Can the predictive model be built in real time?

b)      Can the model be updated in real time?

c)       How much historical data can be used for this model?

d)      How can the data be pre-processed and at what rate?

e)      How frequently can the model be retrained?

f)       Can the model be incrementally updated?


There are many architectural changes also for Real time  ex In memory processing, stream processing etc



According to Gartner analyst Joe Skorupa. “The enormous number of devices, coupled with the sheer volume, velocity and structure of IoT data, creates challenges, particularly in the areas of security, data, storage management, servers and the data center network, as real-time business processes are at stake,”

Thus, IoT will affect many areas: Security, Business processes, Consumer Privacy Data Storage Management Server Technologies Data Center Network etc

The hypercat platform provides a mechanism to manage these complex changes

We can model every sensor+actuator and person as a Digital entity. We can assign predictive behaviour to digital objects (Digital entity has processing power, an agenda and access to meta data). We can model and assign predictive behaviour to multiple levels of objects(from the while refinery to a valve)

We can model time varying data and predict behaviour based on inputs at a point in time.  The behaviour is flexible (resolved at run time) and creates a risk prediction and a feedback loop to modify behaviour in real time along with a set of rules

We can thus cover the whole lifecycle – Starting with discovery of new IoT services in a federated manner, managing security and privacy to ultimately creating autonomous, emergent behaviour for each entity

All this in context of a security and Interoperability framework


Predictive analytics as a service?

Based on the above, predictive analytics cannot be an API – but it would be more a dynamic service which can provide the right data, to the right person, at the right time and place. The service would be self improving(self learning) in real time.

I welcome comments on the above. You can email me at ajit.jaokar at or post in the Hypercat LinkedIn forum






Small Data: A Deterministic and predictive approach


Image source: Daniel Villatoro 


In this blog/article, I expand on the idea of ‘Small data’.

I present a generic model for Small data combining Deterministic and Predictive components

Although I have presented the ideas in context of IoT(which I understand best) – the same algorithms and approach could apply to domains such as Retail, Telecoms, Banking etc

We could have a number of data sets which may be individually small but it is possible to find value at their intersection.  This approach is similar to the mobile industry/ foursquare scenario of knowing the context to provide the best service/offer etc to a customer segment of one. That’s a powerful idea in itself and a reason to consider Small Data. However, I wanted to extend the deterministic aspects of Small data (intersection of many small data sets) by also considering the predictive aspects. The article describes a general approach for adding a predictive component to Small data which comprises of three steps: a) A limited set of features are extracted, b) Their dimensionality is reduced(ex using clustering) and c) finally we use a classification and a recognition method like Hidden Markov Models to recognize a higher order metric (ex walking or footfall)


 Last week, I gave an invited talk on IoT and Machine Learning at the Bigdap conference organized by the Ontic project . The Ontic project is a EU FP7 project doing some interesting work on Big Data and Analytics mainly from a Telco perspective.

The audience was technical and was reflected in the themes of the event which (for example : Techniques, models and algorithms for Big data, Scalable Data Mining and Machine learning techniques and mechanisms, Big Data Security and Privacy challenges, Cleaning Big Data (noise reduction), acquisition & integration, Multidimensional Big Data, Algorithms for enhancing data quality.)

This blog post is inspired by some conversations following my talk with Daniel Villatoro (BBVA) and Dr Alberto Mozo (UPM/Ontic). It extends many of the ideas and papers I referenced in my talk.


In his talk, Daniel referred to ‘small data’ (image from Slides used with permission). In this context, as per slide, Small data refers to the intersection of various elements like customers, offers, social context etc in a small retailer context. Small data is an interesting concept and I wanted to explore it more. So, I spent the weekend thinking more about it.

When you have data elements, the concept of small data is a deterministic. It is similar to the mobile industry/ foursquare scenario of knowing the context to provide the best service/offer etc. Thus, given the right datasets, you can find value at the intersection. This works even if the individual Data sets are small as long as you find enough intersecting datasets to create a customer segment of one at their intersection.

That’s a powerful idea in itself and a reason to consider Small Data.

However, I wanted to extend the deterministic aspects of Small data (intersection of many small data sets) by also considering the predictive aspects. In the case of Predictive aspects, we want to infer insights from relatively limited data sets

In addition, I was also looking for a good use case to teach my students @citysciences. Hence, this blog will explore the predictive aspects of Small data in an IoT context

I believe the ideas I discuss could apply to any scenario (ex retail/banking) and indeed also to Big Data sets

A caveat:

The examples I have considered below strictly apply to Wireless Sensor Networks(WSNs). WSNs differ from IoT because there is potentially communication between the nodes. The topology of the WSNs can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network can be routing or flooding.  In contrast, IoT nodes do not necessarily communicate between each other in this way. But for the purposes of our example, the examples are valid because we are interested in the insights inferred from the Data.

Predictive characteristics of Small data

From a predictive standpoint, I propose that Small data will have the following characteristics:

1)      The Data is missing or incomplete

2)      The data is limited

3)      Alternatively, we have Large data sets which need to be converted to a smaller data set to make it more relevant(ex a small retailer)  to the problem at hand

4)      The need for inferred metrics i.e. higher order metrics derived from raw data

This complements the deterministic aspects of Small data i.e. finding a number of data sets to identify the value at their intersection even if each data set itself may be small(Small data)

So, based on papers I reference below, I propose three methodologies that can be used for understanding Small data from a predictive standpoint

1)      Feature extraction

2)      Dimensionality reduction

3)      Feature Classification and recognition

To discuss these in detail, I use the problem of monitoring physical activity for assisted living patients. These patients live in an apartment under a privacy-aware manner. Here, we use sensors and infer behaviour based on the sensor readings but yet want to protect the privacy of the patient

The papers I have referred to are (also in my talk):

  • Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey – Akin Avci, Stephan Bosch, Mihai Marin-Perianu, Raluca Marin-Perianu, Paul Havinga University of Twente, The Netherlands
  • Robust location-aware activity recognition: Lu and Fu 

This problem is a ‘small data’ problem because we have limited data, some of it is missing (not all sensors can be monitoring at all times) and we have to infer behaviour based on raw sensor readings. We will complement this with the deterministic interpretation of Small Data (where we accurately know a reading).

Small data: Assisted Living Scenario

source Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home Ching-Hu Lu, Student Member, IEEE, and Li-Chen Fu, Fellow, IEEE

In an assisted living scenario, the goal is to recognize activity based on the observations of specific sensors. Traditionally, researchers used vision sensors for activity recognition. However, that is very privacy invasive.  The challenge is thus to recognize human behaviour based on raw readings / activity from multiple sensors. In addition, in an assisted living system, the subject being monitored may have a disorder (for example Cognitive disorders or Chronic conditions).

The techniques presented below could also apply to other scenarios – ex to detect Quality of Experience in Telecoms or in general for any situation where we have to infer insights from relatively limited data sets(ex footfall)

The steps/methods for retrieving activity information from raw sensor data are: preprocessing, segmentation, feature extraction, dimensionality reduction and classification

 In this post, we will consider the last three i.e. feature extraction, dimensionality reduction and classification. We could use these three techniques for situations where we want to create a predictive component for ‘small data’


Small data: Extracting predictive insights

In the above scenario, we could extract new insights using the following predictive techniques (even when we have less data)

 1)      Feature extraction

Feature extraction takes inputs from raw data readings and finds find the main characteristics of a data segment that accurately represent the original data. The smaller set of features can be described as abstractions of raw data. The purpose of feature extraction is to transform large quantities of input data into a reduced set of features. This smaller set of Data is represented as an n-dimensional feature vector. This feature vector is then used as an input to a classification algorithm.

 2)      Dimensionality Reduction

Dimensionality reduction methods aim to increase accuracy and reduce computational effort. By reducing the features involved in the classification process, less computational effort and memory are needed to perform the classification. In other words, if the dimensionality of a feature set is too high, some features might be irrelevant and do not even provide useful information for classification.The two general forms of dimensionality reduction are: feature selection and feature transform.

 Feature selection methods select the features, which are most discriminative and contribute most to the performance of the classifier, in order to create a subset of the existing features. For example: SVM-Based Feature Selection select several most important features and conclude that 5 attributes would be enough to classify daily activities accurately. K-Means Clustering is a method to uncover structure in a set of samples by grouping them according to a distance metric. K-means clustering algorithms rank individual features according to their discriminative properties and their co-relationships.

 Feature Transform Methods : Feature transform techniques try to map the high dimensional feature space into a much lower dimension, yielding fewer features that are a combination of the original features. They are useful in situations where multiple features collectively provide good discrimination but individually, those features would provide poor discrimination. Principal Component Analysis (PCA) PCA is a well known and widely used statistical analysis method and can be used to transform the original features into a lower dimensional space.

 3)     Classification and Recognition: The selected or reduced features from the dimensionality reduction process are used as inputs for the classification and recognition methods.  

For example: Nearest Neighbor (NN) algorithms are used for classification of activities based on the closest training examples in the feature space. (ex k-NN algorithm)

 Naïve Bayes is a simple probabilistic classifier based on Bayes’ theorem which can be used for Classification.

 Support Vector Machines (SVMs) are supervised learning methods used for classification. In the assisted living scenario, SVM based activity recognition system using objects attached with sensors can be used to recognize drinking, phoning, and writing activities

 Hidden Markov Models (HMMs) are statistical models that can also be used for activity recognition. I used a simple analogy to explain hidden markov analysis from a paper which explained HMM for inferring temperature in the distant past based on tree ring sizes

 Gaussian Mixture Models (GMMs) can be used to recognize transitions between activities

 Artificial Neural Networks can also be used to detect occurrences – ex falls.

 Thus, we get a scenario as below











sensors(adapted from Activity Recognition Using Inertial Sensing for Healthcare,Wellbeing and Sports Applications: A Survey)

activity (adapted from Robust location-aware activity recognition: Lu and Fu  )

Small Data: Complementing the Deterministic by the predictive

To conclude:

Small Data could be a deterministic problem when we know a number of datasets and value lies at the intersection of these data sets. This strategy is possible with Mobile context based services and Location based services. The results so achieved could also be complemented by a predictive component of Small data.

In this case,  a limited set of features are extracted, their dimensionality is reduced(ex using clustering) and finally we use a classification and a recognition method like Hidden Markov Models to actually recognize a higher order metric (ex walking, retail footfall etc)

I believe that these ideas could be adapted to many domains. Data science is engineering problem. It’s like building a Bridge where there is no fixed solution in advance. Every Bridge is different and will present a unique set of challenges.  I like the blog post – Machine Learning is not a Kaggle competition . The author(Julia Evans) correctly emphasizes that we need to understand the business problem first. So, I think the above approach could apply to many business scenarios – ex in Retail (footfall), Healthcare, Airport lounges etc by inferring predictive insights from data streams


Ardusat, Countdown Institute at CTIA connected for Good event (part of super mobility week) in Las Vegas

In October, we fully launch the Countdown Institute in Miami (lab Miami) for STEM education

Countdown is based on using Ardusat technology which allows you to conduct experiments in space on a live Cubesat based satellite

Essentially, the Ardusat is based on Cubesat and contains Arduino sensors which allows us to learn Computer Science in context of Space exploration experiments

Sunny Washington President of Ardusat is speaking at the CTIA connected for good event (part of the Super Mobility week) in Las Vegas today

It’s great to see this

The talk reflects the hard work our team in Miami has been putting in working with Ardusat (Richard, Jessica, Alex and also the faculty Nelson, Willie and Patrick)

If you are at CTIA – say Hi to the Ardusat team!

New futuretext web site is now live

Over the last two years, I have been refocussing my work and much of that is now complete


Have a look at the new futuretext site which reflects my emphasis on Machine Learning and IoT – both for projects and teaching


Why I signed a petition in favour of Amazon at







I supported this change,org petition in favour of Amazon – Stop fighting low prices and fair wages with the following comment

While I may not agree everything Amazon does, I think Amazon has created a level playing field for a whole set of new content creators. In that sense, in future – it will serve new content creators better and  lead to more innovation. Existing publishers can never do that. I also agree with the ebook pricing argument from Amazon. Also, as a customer of Amazon – they have my goodwill and trust. I cannot say the same of any other traditional publisher(with the exception of O Reilly – who are very non traditional also). Thus, I believe – from the past record – Amazon will continue to innovate and serve its content creators and customers better than existing publishers  

My slides for IoT and machine learning – Computational Intelligence conference #CIUUK14

I spoke at the Computational intelligence  on Sat at BT HQ in St Paul Londonand it was a very interesting event

I was surprised to see more than 300 people in London on a sunny afternoon for what is essentially a VERY geeky topic!
My talk (IoT and Machine Learning) got a lot of +ve feedback as per
Thura Z. Maung @thuramg 11h Enjoyed the talks #CIUUK14 today, particularly Artificial Super Intelligence and IoT/Machine Learning…
Brett Hutley @hitechnomad 12h I enjoyed the conference #CIUUK14 my favourite talk was probably the Internet of Things and Machine Learning
Robert Thomas @dizzybanjo 13h Arrived at #CIUUK14 interesting talk about machine
Diogo Neves @DiogoSnows 13h .@AjitJaokar what a great talk you just gave! thanks!!!!
Joe Da Silva @joemagicdevelop Brilliant talk by Ajit Jaokar on #MachineLearning applied to the #enterprise and #gov #CIUUK14
Pls sign up at futuretext
I am working on a larger paper on IoT and Machine Learning
shall email it when its released

IoT and Machine Learning – participation, proceedings, case studies etc








The IoT and Machine Learning workshop at the IOT world event promises to be a truly special event.

We have some attendee passes(with a discount code which allows you to attend the day only) and opportunities for case studies/presentations

If you are interested in attending with the discount code or contributing – please contact me at ajit.jaokar at


Countdown: Coding for the Stars – By Ajit Jaokar and Aditya Jaokar – learn the Raspberry Pi and Arduino through space exploration












Here is more about our book  (co-authored by me and my 10 year old son)

Extending the ideas in the book, working with Alex De Carvalho and the Lab Miami , we are also setting up an accompanying Multimedia research center in Miami for kids to learn Programming and Computer Science using Space technology

The book will be launched as a Kickstarter project in June 2014. A research center will also be based in Miami on the ideas for this book. The center will enable kids to learn about the Raspberry Pi and Arduino through Space technology

We will also hold learning to code sessions in Miami in the week beginning June 9

If you are interested to know more – please email me at ajit.jaokar at


Idea originally inspired by a NASA scientist who said ‘Space unites humanity .. ‘

A group of kids who are based globally decide to collaborate and launch a Satellite in space.

The Satellite is based on the Raspberry Pi, Arduino and other open source technologies

In doing so, they learn about specific technologies like Raspberry Pi and Arduino in context of space exploration.

Each child has expertise and is based globally (and often has some limitations/quirks).

The protagonist (a boy aged 10 based in London) – has the idea to launch a grand plan – a satellite in space based on Arduino / Raspberry Pi

He creates a group on social media – and asks to see who wants to join to help him create this satellite

A group of kids globally respond:

A boy from China who has great mechanical abilities
A girl from Philippines who is good at programming
A girl living in Miami who is originally from Brazil who is into design
A boy from Germany who is good at hardware
A boy from Russia who is also good at technology

The story is about this group of kids who collaborate to launch the satellite in space.

The book is a series of three books

a)      The plan – Design (book launched in Oct 2014 in Miami)

b)      The build – How to make the satellite

c)       Blast off – the launch

The vision

The world will be like this in future

Science and skill will unite humanity.

Talent will be found all over the world and people(even kids) will collaborate to create something amazing

Technically the idea of launching an Arduino based satellite into space is very much possible. We use this to teach design and programming to kids

The technology

Apart from the story line, from a technical perspective – the basic idea is:

You could teach kids about a temperature sensor in isolation OR you could teach them the same idea of a temperature sensor but in context of a satellite. Which one is better?

The idea is not as far-fetched as it may sound i.e. this is very much doable. The NASA Elana program provides a possibility to explore these ideas based on the  Cubesat standardexplained more here (NASA Elana program with Cubesat standard)

So, we have a story (A group of kids who collaborate globally to launch a Satellite in Space) – and the idea is to teach kids about programming and design using the Pi / Arduino using the NASA Elana program

So, while our story is fictional – its a great way for kids to learn about real programming in context. The book will also include real examples, exercises and code about the venture

Thoughts and comments welcome ..

Launch and Research center

The book will be launched as a Kickstarter project in June 2014. A research center will also be based in Miami on the ideas for this book. The center will enable kids to learn about the Raspberry Pi and Arduino through Space technology

We will also hold learning to code sessions in Miami in the week beginning June 9

We are grateful for the help and feedback from NASA and the European Space Agency for this project

We also thank Alex De Carvalho and the Lab Miami for their help in this project

If you are interested to know more – please email me at ajit.jaokar at

PS – I just saw this Tiny KickSat Sprite satellites hitch ride into orbit . Hence, our idea of teaching kids to code using a story in context of Space is nearer than we thought!

PPS: We can even fork the code on github

Using Satellites to teach Programming, Raspberry Pi, Arduino and engineering ..







I have been exploring this idea before – in the form of a fictional book

The story line of our book – for kids to learn Raspberry Pi and Arduino by learning how to design and launch a satellite 

Apart from the story line, from a technical perspective – the basic idea is:

You could teach kids about a temperature sensor in isolation OR you could teach them the same idea of a temperature sensor but in context of a satellite. Which one is better?

The idea is not as far-fetched as it may sound i.e. this is very much doable and at least – and hence it is a story based on fact

The NASA Elana program provides a possibility to explore these ideas based on the  Cubesat standard explained more here (NASA Elana program with Cubesat standard)

(NASA’s Kennedy Space Center in Florida is adapting the Poly-Picosatellite Orbital Deployer, or PPOD, to put these CubeSats into orbit. This deployment system, designed and manufactured by the California Polytechnic State University in partnership with Stanford University, has flown previously on Department of Defense and commercial launch vehicles.)

So, we have a story (A group of kids who collaborate globally to launch a Satellite in Space) – and the idea is to teach kids about programming and design using the Pi / Arduino using the NASA Elana program

The ardusat project used Arduino with cubesat and used the following sensors

The Arduino processors may sample data from the following sensors  :

one digital 3-axis magnetometer (MAG3110)

one digital 3-axis gyroscope (ITG-3200)

one 3-axis accelerometer (ADXL345)

one infrared temperature sensor with a wide sensing range (MLX90614)

four digital temperature sensors (TMP102) : 2 in the payload, 2 on the bottomplate

two luminosity sensor (TSL2561) covering both infrared and visible light : 1 on the bottomplate camera, 1 on the bottomplate slit

two geiger counter tubes (LND 716)

one optical spectrometer (Spectruino)

one 1.3MP camera (C439)

 So, while our story is fictional – its a great way for kids to learn about real programming in context ex you could teach temperature sensors in isolation – or in context of a satellite in space. Which is better!

Thoughts and comments welcome ..

Image source: NASA

A fantastic lineup for the forumoxford conference on Friday (which I co-chair with Tomi)

A fantastic lineup for the forumoxford conference on Friday (which I co-chair with Tomi)



ForumOxford is pleased to have the support of Distimo in 2014.

See below for further details of the talks at ForumOxford



James Elles

Member of the European Parliament


I am a Member of the European Parliament for the South-East Region, with special responsibility for the Conservative Party in Berkshire, Buckinghamshire and Oxfordshire. Now in my sixth term, I am a member of the European Parliament’s Budgets committee, and also a substitute member of both the Foreign Affairs committee and the EU-US delegation. I am the founder of the Transatlantic Policy Network, which I currently chair, and a co-founder of the European Internet Foundation.

Karim Lesina

Vice President, AT&T


Karim Antonio Lesina is the Vice President of AT&T, covering International External Affairs for the European Union, Caribbean, Central and Latin America Regions and in charge of the Trans-Atlantic Relations. In this role he leads AT&T’s advocacy in those regions. AT&T is a premier global communications company, providing wholesale services and mobile roaming services to over 220 countries and territories, and providing business enterprise services to countries representing over 99 percent of the world’s economy.

In addition to developing and implementing market access strategies to enable AT&T’s global expansion to satisfy customer needs, other responsibilities for Mr Lesina include ensuring compliance with international telecom regulations, and advocacy on a wide range policy matters related to the stable growth, innovation and investment by the information and communications technology sector.

Mr Lesina is based in AT&T’s Brussels (Belgium) office. He is an active member in several industry and community organizations, including current service as: Chair of the Presidency Group of the American Chamber of Commerce to the EU. He is a Board Member of the European Internet Foundation and of the Transatlantic Business Council. He also represents AT&T in different associations such as ETNO, GSMA, ECTA, TPN, etc.
Prior to joining AT&T, Mr Lesina held senior positions with another leading US-headquartered ICT company (Intel Corporation), and a number of leading public affairs agencies in Brussels. Born in Dakar (Senegal) Mr Lesina is an Italian-Tunisian national and has a Master Degree in Economics of development at the Catholic University of Louvain-la-Neuve in Belgium.


Mobilising our world: Driving investment and innovation in the wireless revolution



Haydn Shaughnessy

  • Contributor, Forbes
  • Haydn is the author of The Elastic Enterprise, an account of how stellar companies prospered during the recent recession. His next book is The Fluid Core, How Technology Is Creating A New Hierarchy of Need and How Smart Companies Respond.

    He writes on innovation and competitiveness issues for where his audience regularly exceeds 500,000 monthly. He has also written for The Wall St Journal and other leading outlets. He used to write the Convergence Culture column in The Irish Times.

    In the late 1980s he became involved in the EU’s attempt to create an ARPA-type unit, similar to the Advanced Research Projects Agency in the USA. That was the RACE programme. He caught the tech bug from that experience, discussing and writing about 3G mobile and broadband applications more than a decade before they became commonplace.

    He was educated at The London School of Economics and Oxford University and is a fellow at the Paul Merage School of Business, University of California at Irvine, and of the Society for New Communications Research and an adviser to organizations in transition.


3 degrees of Separation – How the Relentless March of Connectivity Transforms Markets


The theme is that connectivity is highly prized by consumers more so than we usually realise and over time that has led to an increase in scale-free or power law economics, making it increasingly important for companies to incorporate consumers/customers are every stage of business.


Chris Book

Developer Relations Manager, Distimo


Chris Book is a Developer Relations Manager at Distimo, where he advocates for Distimo products to the developer community, showing both current and future clients how to gain value from their own app store data.


Show Me the Money! Learning from mobile games monetisation – how to create a successful app


Dr Catherine Mulligan

Research Fellow, Innovation and Entrepreneurship group, Imperial College London


Dr Catherine Mulligan is a Research Fellow in the Innovation and Entrepreneurship group. She is Principal Investigator on two RCUK Digital Economy Program grants: Sustainable Society Network+ and Scaling the Rural Enterprise.

In addition, Catherine is a researcher on WP4 (Business Models) for the Digital City Exchange and Co-Investigator on the “Unleashing the Value of Big Data” projects.

Catherine has 15 years international experience in the Mobile Telecommunications and ICT industries, including 10 years at Ericsson in Stockholm, Sweden. Working on a variety of cutting edge technologies, Catherine experienced first-hand the complexities of successfully taking innovation to market.

Her research interests lie in the area of new economic and business models enabled by the digital economy. In particular, Catherine is interested in the role that technologies play in the creation of citizen-centric smart/sustainable cities.


From M2M to IoT – the impact on Smart Cities

Digital technologies are often suggested as a panacea for the development of “smart cities” – cities that in some form integrate a digital infrastructure with the physical city in order to reduce environmental impact while improving quality of life and economic prospects. While these sorts of concepts have been around for several decades, the recent advent of smartphones and cheaper sensor technology means that digitally enabled, or “smart,” cities are fast becoming a real-world possibility.


The role of the Internet of Things (IoT) in cities is one that will only increase as the pressure on cities to deliver services at reduced costs for an expanding population increases. Many examples exist, including water management, transport management, and waste management. Due to the complexity in cities, the best way for them to achieve the desired outcomes of smart cities is to utilize an information marketplace approach, which allows them to combine together the data from extensive M2M and IoT investments and utilize positive externalities associated with technology in order to reduce environmental impact while creating jobs and economic prosperity for citizens.  Dr Mulligan will present some insights from her upcoming book “From M2M to IoT: An Introduction to a New Age of Intelligence”, published by Elsevier.


David Rogers

Founder, Copper Horse Solutions Ltd

  • David (@drogersuk) is a mobile phone security expert who runs Copper Horse Solutions Ltd, a software and security company based in Windsor, UK. He also chairs the Device Security Steering Group at the GSM Association and teaches the Mobile Systems Security course at the University of Oxford. He has worked in the mobile industry for over 15 years in security and engineering roles. Prior to this he worked in the semiconductor industry.

    David’s articles and comments on mobile security topics have been regularly covered by the media worldwide including The Guardian, The Wall Street Journal and Sophos’ Naked Security blog. His book ‘Mobile Security: A Guide for Users’ was published in 2013.

    David holds an MSc in Software Engineering from the University of Oxford and a HND in Mechatronics from the University of Teesside.

    David blogs from


The Future of Mobile Device Security


The mobile device is changing – is it a handset, a watch or an electricity meter? Is it secure? How do you secure it? What if a software update bursts my home’s hot water pipes? The ever-increasing complexity of the mobile eco-system is a potential nightmare for users yet security and safety remains an after-thought for some companies.


This talk will look at what is really happening and what should happen to ensure consumers remain safe and secure.



Dr Patricia Timoner

Strategy Consultant, Mobile Digital and Interactive Marketing South America Operations, GrowVC


With over 15 years of work experience in mobile, media and high technology, Dr Patricia Timoner has spent the last nine years as a Strategy Consultant in Mobile Digital and Interactive Marketing. Some of her most notable high tech media/marketing consulting projects include developing the pilot for Sony to use the Playstation Portable (PSP) as a mobile engagement and interactive platform; the strategic marketing plan for OpenSearch the online advertising software; and a major marketing campaign for Smirnoff that achieved 16% gain in market share. With major high tech related work on four continents, Dr Timoner is a co-founder of Mobile Monday (MoMo) Sao Paulo and sits on its Board, as well as assisting several MoMo chapters in Latin America.

An expert on mobile business and media, with focus areas on social networking on mobile, advertising and the emerging opportunity of ‘engagement marketing’, Patricia has contributed to the evolution of the thought leadership through speakerships, workshops, guest lecturing and writing. She has contributed a chapter to the Pagani’s Encyclopedia of Multimedia Technology and Networking, as well as a chapter to Kotler’s Marketing (6th Edition). Being an internatinoally respected authority on high tech, Dr Timoner has served as a judge on prestigious international competitions such as the World Championships of E-Business and Asia Pacific IT and T Awards.

Dr Timoner has completed major consulting projects such as the marketing strategy for Reniar AB, the customer relationship management project for the Clinica Medica General of Los Angeles, and the business development of the ANZMAC conference. She has helped in international expansion projects with companies such as HealthRider, Spinning, and Williams Worldwide Television. Due to rare experience managing numerous intercontinental high tech projects between Australia, Brazil, China and the USA, Dr Timoner is often engaged to projects of exceptional international issues. One of her most recognized achievements was the business model for the international OPTO VLSI project between Germany, Israel, South Korea, the UK and Australia. Dr Timoner was recognized as crucial to the project’s success.

Before her consulting, writing and lecturing career Patricia gained actual hands-on employment experience in the 1980s and 1990s at the executive level as the Managing Director of T-Connection Sao Paulo; at the operational level as Marketing Manager of Physician Care Management Los Angeles California and Marketing Manager at Queens Commercio e Industria in Brazil. Dr Timoner began her marketing and advertising career learning the basics as Junior Brand Manager at Heublein do Brasil, and Media Coordinator at MPM Propaganda.

A popular speaker seen at international conferences on five continents, Patricia chaired the Mobile Commerce Track at IRMA San Diego and the Electronic Marketing Track at the We-b Conference. She has presented to such events as the Mobile Content World, Digital Marketing and Media Summit, IABE Las Vegas, TeleViva Movel, European Conference on Information Systems, Mobile 2.0 and the M Payment and M Banking Congress. During her consulting career, Dr Timoner has been a frequent guest lecturer on mobile, digital and media topics at major universities such as ESPM and FAAP Universities of Sao Paulo, Liaoning University China, Trisakti University of Jakarta, and University of Western Sydney. Patricia Timoner holds a Ph D in Mobile Commerce from Edith Cowan University Perth Australia; with an MBA Degree specializing on E-Commerce; a Master’s Degree on Marketing; and a Bachelor’s Degree in Social Communications and Advertising.


Brazil – Mobile Market Overview


Caribbean. Mobile penetration is upward of 132% and still growing by about 7% annually. Yet the market has some very unique characteristics.


This presentation aims to provide an overview of the Brazilian mobile market and user habits, as well as scenario forecasts for the years 2015 and 2020.


  • Sean Kane
  • Co-Founder,
  • is a very dynamic community with a UK origin but a global following. Sean Kane has been active in growing web and mobile companies for 15 years in the media and social space, such as (SVP), Intercasting (GM) and Bebo (VP Mobile). He is also Co-Founder of Springboard Accelerator and assists numerous Startup Programs around the globe. He has been honoured to work with amazing founders across the world, has been fortunate to be involved in several IPOs and acquisitions and is an active mentor for entrepreneurial programs. Sean has done coursework at Harvard, MIT and ITAM.


Raising Money for Mobile Startups: Online and Crowdfunding



  • Jeanette Carlsson
  • CEO & Founder, @newmedia2.0
  • Jeanette is CEO and Founder of @newmedia2.0 (, the leading, independent digital media and insight consultancy, advising Brands and TMT companies on how to deliver improved business performance and growth through better use of digital and strategic user insights. She is also an advisor to the world’s largest mobile marketing company; UKTI’s ‘Catalyst programme, UK ‘Tech City ‘and US tech start-ups; and guest lecturer on digital, innovation & entrepreneurship at University of Oxford and University of Warwick.

    Jeanette has over 20 years’ professional experience leading, growing and advising companies from start-ups to large corporates on how to deliver improved business performance and growth through better strategy and use of digital communications, working with boards and senior executives of the world’s leading brands in the UK, Nordics, Western Europe and the US. Prior to setting up @newmedia2.0, Jeanette was MD of a leading Marketing Analytics company, and before that, spent 10 years at IBM leading and growing UK, European and global businesses in the TMT space. Jeanette has published widely on key strategic communications & digital issues facing businesses and is a frequent speaker at leading industry events.

    Jeanette has a postgraduate degree in Advanced Strategy from the University of Oxford; an MA and BSc in Economics from University College London and a BA in English from University of Copenhagen. Jeanette is bilingual Danish/English and also speaks Swedish, Norwegian, German and French.


What is your data worth?


As consumers consume content and interact with businesses across multiple platforms, companies collect a vast and growing bank of user data from multiple touchpoints from offline media to website traffic, mobile/tablet apps, social media, digital content, reader offers, transaction data etc. Turning this data into strategic, actionable user insights holds significant potential business value to companies in terms of empowering decisions and monetising strategies, so is increasingly becoming a strategic priority. Today, few companies capture or extract maximum value from this hidden ‘goldmine’. In this talk, Jeanette Carlsson, CEO @newmedia2.0 – the leading digital and user insight consultancy, looks at the data opportunity and shares her perspective on how best to leverage user data to develop a true understanding of users and their value with associated business benefits.



  • Dan Appelquist
  • Open Web Advocate, Telefónica Digital and blogger at Torgo
  • Dan is an American Ex-Pat living in London. He’s a father of two and husband of one. Dan is the Open Web Advocate for Telefónica Digital, focusing on the Open Web Device. Dan founded Mobile Monday London, Over the Air and the Mobile 2.0 conference series. Dan is an advocate for the open Web and for Web Standards.


East Side, West Side, Peace: How App Developers Should Leverage the Web


The web is 25 years old. During that time it has evolved from a system for sharing academic information to a ubiquitous, distributed application platform used by businesses, organisations and individuals the world over, and it has become one of the greatest engines of innovation the world has ever seen.


The evolution of this open platform has been unlikely and convoluted, and throughout its development it has been subject to attacks parties whose interests are better served by closed, controlled platforms. The rise of app stores and native mobile applications is another such development. Don’t worry, though.


This talk is not going to be about apps vs. web. As apps platforms continue to mature, it’s becoming clearer how apps and the web should work together – how we can and should be leveraging the web to do what it’s good at and using native apps to do what they’re good at – ultimately to provide the best user experience across all platforms.


I’ll talk about the latest and greatest in mobile web application platforms (such as Firefox OS), how best to use the Web alongside of native apps across platforms. I’ll examine some (currently) broken user experiences and point the way forward to a world where apps and the web can live together.


Lilach Bullock

Founder, Socialable


Lilach is the founder and driving force behind Socialable, and highly regarded on the world speaker circuit. Forbes and Number 10 Downing Street have even been graced by her presence! In a nutshell, she’s a hugely connected and highly influential serial entrepreneur – the embodiment of Digitelligence.

Listed in Forbes as one of the top 20 women social media power influencers and likewise as one of the top social media power influencers, she is one of the most dynamic personalities in the social media market, she actively leverages ethical online marketing for her clients and for Socialable.

After launching her first business within three years of becoming a mother, her financial success was recognised by being a finalist at the Best MumPreneur of the Year Awards, presented at 10 Downing Street. Following a resultant offer and wishing to spend more time with her daughter, she sold her first business to focus on social media, developing a multi-site blog and online marketing portfolio that generates in excess of 600,000 + page views per month.

A business owner, social media consultant, internet mentor and genuine digital guru, Lilach is consulted by journalists and regularly quoted in newspapers, business publications and marketing magazines (including Forbes, The Telegraph, Wired, Prima Magazine, The Sunday Times, Social Media Today and BBC Radio 5 Live). What’s more, her books have achieved No 1 on Amazon for Sales and Marketing and Small Business and Entrepreneurship.

When Lilach isn’t working she enjoys spending time with her family and is an avid fan of Zumba.



Lee Omar

CEO and Founder, Red Ninja

Lee is the founder of Red Ninja, a high growth design led innovation company. He is an experienced application developer and smart city practitioner. He has developed real time Internet of Things data driven apps for ARM, BBC, TUC, Network Rail, Merseytravel, National Museums and Liverpool City Council. He developed his first geo-locational mobile social network app in 2009.


Lee works directly with Chief Scientific Advisor to UK Government Sir Mark Walport in his Foresight Future Cities project, which is looking at the role of cities in 2065 and advising on policy changes that should be implemented to get there. He is currently working with National Health Service to develop mobile ambient assisted living technologies that enable people to live in their homes longer.

An expert in generating value from big data sets and the Internet of Things and is currently advising Connected Digital Economy Catapult to set their funding strategy for the cultural sector around data. He sits on the British Standards Institute advisory group that defines the framework for smart cities for local governments and also the ontology. Lee is the Entrepreneur in Residence at XJLTU University (Souzou/Shanghai).



  • Tineka Smith
  • Media Relations Specialist, Weber Shandwick Technology UK
  • Tineka Smith is a media relations specialist within the technology landscape. Her experience includes working with Weber Shandwick Technology while providing media relations expertise and content creation across a range of clients which have included Microsoft, BAE Systems, Capgemini, Gartner, Veracode and Ricoh Europe.

    Prior to Weber Shandwick Technology, Tineka was a reporter and junior news editor for Computer Business Review covering a range of technology topics including big data, mobile, security and social media. Tineka also worked as a tech contributor for the New Statesman business blog and previously worked for local newspapers and TV in the United States. Tineka has a BA (Hons) in Communications, French Studies and a Masters in International Journalism.


How Mobile is Changing the Media Landscape


This will take a look at how journalism has changed over the past few years due to the increasing use of mobile devices in reporting and how it could potentially affect the future. Mobile has also spurred the increase of citizen journalism and has placed much of the power in how/which news is delivered into the hands of the public. In the coming years mobile may also affect how TV news is delivered as more and more journalists are switching to using cameras on their tablets to conduct interviews for news organisations.



Patrick Bergel

CEO and Founder, Animal Systems



Karen Barber?



Afternoon panellists



  • Peggy Anne Salz
  • Chief Analyst and Founder, MobileGroove


Peggy Anne Salz is lead analyst and founder of MobileGroove, a top 50 ranked influential destination that produces and promotes custom research, strategic thought leadership and knowledge resources for the global mobile industry. Her work, which includes 300+ articles on mobile marketing, mobile search, social media and mobile industry news and developments, has appeared in The International Herald Tribune, The Wall Street Journal (Europe & Asia editions), TIME, and in the Agile Minds column in EContent magazine, among many more.

Peggy is also a Gigaom Research mobile analyst, where her focus is mobile loyalty, mobile messaging and mobile retail. Her most recent report, Managing The Complete Customer Experience: Encouraging Engagement with Mobile and Apps, helps businesses understand and harness mobile to re-imagine the customer experience and super-charge sales and service channels.

Peggy has written nine books about mobile, both as a lead author and in partnership with global companies in the industry. Her most recent book, Apponomics: The Insider’s Guide to a Billion Dollar App Business (InMobi, 2014) provides actionable insights into how companies can market and monetize their apps. It builds on the success of her first book on mobile apps, The Everything Guide to Mobile Apps: A Practical Guide to Affordable Mobile App Development for Your Business (F+W Media, Inc, 2013), a practical, crowd-sourced book providing businesses and developers with insights on how to make, monetize and market mobile apps. She has also edited and produced the Mobile Operator Guide 2013: The Evolution of Mobile Services: Challenges, Strategies, Opportunities and the Mobile Commerce Guide 2013: Engage Customers & Build Loyalty in Developed and Emerging Markets. She is currently working on a new industry resource e-book that explores the impact of ultra-broadband mobility and how it can lay the groundwork for the ultra-connected society of the future.

Graduating with honours from the University of Pittsburgh, Peggy earned a B.A. in Philosophy of Science, Political Science, and Economics. She is a Fulbright fellow and a member of the International Who’s Who of Professionals.


Mick Rigby

CEO, Yodel Mobile Ltd

Mick founded Yodel Mobile in 2007, the first specialist mobile marketing agency headquartered in the UK.

Yodel Mobile is a strategically-led full service agency that offers best in class strategy, development and delivery for organisations looking to incorporate mobile successfully into their business.

Its clients include Kobo, The Daily Mail, IPC, Dennis Publishing, Hastings Direct, The Economist, Wall Street Journal and Sage.

Prior to Yodel Mobile, Mick was the managing partner of a communications planning agency, he has over 20 years’ experience in the marketing and advertising sector and worked at some of the biggest media and advertising agencies.

He is passionate about mobile technology, especially how it can be used to enhance brand and consumer experiences.

Tony Pearce

Founder, gamesGRABR

Coming from a 20 year background in senior management in the entertainment and gaming industry Tony has an excellent track record as a successful entrepreneur and CEO.

Over the past 10 years Tony has raised over £15m in VC funding, started 5 companies and had 2 successful exits. Most recently Tony founded GamesGRABR an innovative platform (web, mobile and tablet) that marries the power and usability of a contemporary ‘pinboard-style’ interface allowing the user to curate their own collections, discover, engage, play and share them with other users with similar interests.

Previously Tony founded a games company called TeePee Games and developed a technology that allowed users to get recommendations on games across social networks such as Facebook.

Tony was the CEO and Co-Founder of Player X and spearheaded the company from a two person start-up in 2004 and developed it into Europe’s largest mobile games distributor. The company was named by Library House as the fourth fastest growing VC backed company in the UK before it was acquired by Spanish mobile content company ZED in April 2009.

Tony is also the co-founder of an executive networking event called the Centurions which is aimed at the digital entertainment industry and takes place every two months in London along with events in New York, Istanbul and Munich.


Richard Downey

Director, The Mobile House


 Martin Wrigley

General Manager Europe, App Developers Alliance



Martin heads up the European arm of the Alliance as General Manager, Europe. With more than 25 years of experience in telecoms and IT and a wide background of development, solutions architecture and delivery.


Martin lead the app developer services area for mobile operator, Orange, between 2004 and 2012.  He has since been actively involved with IT integrators, developers and European institutions, and is also Executive Director of AQuA, – the App Quality Alliance.


The need for a Developer Industry Association – The App Developers Alliance


The talk addresses the need of the industry to have an association that can not only help in terms of business education, best practice information sharing, peer-to-peer global networking but also in representation into policy makers worldwide.  It introduces the App developers Alliance that has been fulfilling this need in the USA for the past few year as and is now expanding into Europe.