Category Archives: GeoLocation

RFID, Raspberry Pi & LBS

The experimentation with Raspberry Pi goes on. RFID is the next stage in effective Indoor Location Based Services and to understand this the following is a small project that brings RFID to the Raspberry Pi.

A brief history of the Raspberry Pi

The first batch of 10,000 Raspberry Pi’s went on sale in February 29th, 2012. And towards the end of 2011, the SD card image for it had already been downloaded more than 50,000 times. Eden Upton designed them for education – specifically python, hence the “Pi” part of the name.

But the tiny board has caught the eye of already experienced programmers and electronic hackers. This has caused a return to a DIY culture with the Raspberry Pi at the forefront of hackers, makers and the curious to understand what is happening “under the hood”.

Location Based Services and RFID

One of the key developments in LBS technology in 2014, is the standards based RF Pattern Matching, based on RFID. This network-based positioning method is based on radio link measurements collected from the network and/or the device. In short, it uses the device’s own radio signals to identify its location, eliminating any dependency on satellites or other network hardware, which is traditional in LBS..

This technology works extremely well in dense urban and indoor environments, and is being used in both mission-critical public safety applications as well as commercial deployments in the US.

In the Internet of Things, truly smart objects essentially are embedded with both an RFID tag and a sensor to measure data. This is easily illustrated using the Raspberry Pi.

The project

For a detailed explanation, check http://skpang.co.uk/blog/archives/946

This project uses the SL030 RFID module with the Raspberry Pi. This will allow the Pi to read the 13.56MHz Mifare RFID cards. The SL030 modules uses the I2C interface at 3.3v.

Software Setup

Install the i2c driver.

Install the Quick2Wire Python library.

Change to the directory where the Quick2wireWire is installed. In our case (your setup might be different):

cd git/quick2wire-python-api/

Download the example python script (thanks to @whaleygeek whaleygeek.co.uk) by:

wget http://www.skpang.co.uk/dl/rfid.py

Change the file permission to allow execute:

chmod +x rfid.py

Start the Python script:

sudo ./rfid.py

Swipe a RFID card or tag onto the reader and the information on the card should be displayed

Hacking Location Based Services & IoT with the Raspberry Pi

Introduction

There are very exciting topics that influenced me over the past few years namely Location Based Services (LBS), The Internet of Things (IoT) and the humble Raspberry Pi.  But first a brief background – I am not a technical person by discipline as my background is in sales in marketing related to the Conference and Events Industry, which I have been involved in for the past 7 years.

Location is personal passion for me to the extent that I chose this as the title for my MA thesis. The following is my personal journey in linking, apparently disconnected technologies that surround this subject.

When I studied this I did not have any idea in regards the technical aspect surrounding LBS but I did identify key people in this arena and interviewed them to find out what their thoughts were for the future of this technology. But I wanted to understand Location on a more primitive level, in which I could create a project that would allow me to experience this.

My problem was a lack of technical knowledge.

Defining Location Based Services

There are various definitions of Location based services and they all agree on the following:

  • LBS it is a software application;

  • for a IP enabled mobile device;

  • which requires knowledge of where the mobile is located;

  • they can be query based services;

  • providing the end-user with useful information an example of which is “Where is the nearest ATM?”;

  • or they can be ‘push-based’ by delivering coupons or other marketing information to customers who are in a specific geographic location.

Location Based Services is not new technology. It is based on military technology entitled Geographic Information Systems which has been around for 14 years or so.  With the advent of the mobile phone and especially the smartphone, Location Based Services has steadily gained acceptance and a foothold in the market place.

Originally the spread of the use of LBS had been mainly via users ‘checking in’ into location based service applications, not unlike checking in via airport booking. This was a time where LBS was in the very early adopter phase; for example, Foursquare and Gowalla, both check in services, were were launched around that time.

The combination of clever Smart devices and social apps, such as Facebook have introduced applications which revolve around Location.

Juniper Research has forecast the mobile location-based services market to exceed $12 billion in 2014, driven by increased app store usage, smartphone adoption and new hybrid positioning technologies.

Enter Internet of Things (IoT)

I started to attend ‘the’ IoT Meetup in London which introduced me to projects that were created by both experts and novices alike. These meetings seeded the possibility of understanding location on a more hands on approach.

According to Gartner there will be nearly 26 billion devices on the Internet of Things by 2020.

According to ABI Research more than 30 billion devices will be wirelessly connected to the Internet of Things (Internet of Everything) by 2020

Two very separate figures but still, this a lot of interconnected devices. The best definition that encapsulate IoT is the following;

Companies and organizations explain the Internet of Things in various ways, but the Internet of Things, or IoT, is most commonly described as an ecosystem of technologies monitoring the status of physical objects, capturing meaningful data, and communicating that information through IP networks to software applications.

The recurring themes in all definitions of the Internet of Things include smart objects, machine to machine communication, RF technologies, and a central hub of information.

How are these two disciplines related?

So what is the common factor for these two apparent unrelated technologies?

Radio Frequency Identification – RFID

 So,

The Internet of Things requires a few necessary components to enable communication between devices and objects. Truly smart objects essentially are embedded with both an RFID tag and a sensor to measure data.

And,

One of the key developments in LBS technology in 2014, is the standards based RF Pattern Matching, based on RFID. This network-based positioning method is based on radio link measurements collected from the network and/or the device. In short, it uses the device’s own radio signals to identify its location, eliminating any dependency on satellites or other network hardware, which is traditional in LBS..

This technology works extremely well in dense urban and indoor environments, and is being used in both mission-critical public safety applications as well as commercial deployments in the US.

So effectively Location is that much closer than before..

The final piece in the puzzle.

That is where the Raspberry Pi comes in..

Raspberry Pi is a $25 credit-card-sized microcomputer, which was originally created to encourage kids to learn how to program in the United Kingdom. However it has tapped into a huge pool of interest among hackers who wanted to use the low-cost hardware to connect devices together in interesting ways..

 I have used this little device to gain an awareness and understanding of how Location fits in all of this with out learning indepth code.

 The Project (outline)

 The aim of the project was to identify Access Points in any given geographic location in which the Raspberry Pi is taken.

 Essential hardware;

 The programs used are:

If a Raspberry Pi is not accessible then this can be duplicated on a laptop.  This can be achieved because the operating system for the Raspberry Pi, entitled Raspbian, and the one for the laptop, entitled Linux Mint, share the same foundation – Debian.

Outline of process

Connect the GPS and Wifi modules to the Raspberry Pi.

Run a session on Kismet which is a Wifi-Spectrum and Traffic Analyzer that relies on the Wifi adapters ability to enter in “monitor mode” – essentially this is gathering on the surrounding AP in any geographic location.

The GPS gadget is essentially gathering co-ordinates of the location of the device. This has been configured to work with Kismet.

The data gathered by Kismet is logged. This data is massaged into shape by running it through a “python Script”, ready to be shown in a Google Earth or Google Map session not unlike the map below.

Map showing AP's
Map showing AP’s

In conclusion, this is an exciting development and can be adapted by implementing RFID and the relevant programs which are available within Debian.  All this with an essential understanding of Linux with a certain depth of knowledge of CLI and a bit of imagination.

To follow – setting up the Raspberry Pi for LBS

Locomizer – a solution to Location Based Services’ privacy concerns

The following interview was carried with Dr. Alexei Poliakov and Alexei “Alex” Poliakov, co-founders of Locomizer, on 13th September 2013 at 20 Orange Street, London.

Dr. Alexei Poliakov – a microbiologist with over 10 years of scientific research on spatial pattern formations in mixtures of cells. Alexei is experienced in leading scientific and entrepreneurial teams. His Nature-inspired algorithm is the core of our innovative approach to location data analytics at locomizer.

Alexei “Alex” Poliakov –  holds a strong background in Corporate Biz Dev by building alliances & partnerships in such companies like Fujitsu and Rakuten. He also has experience in hardware & software product planning & management. Alex received Diploma in Computer Info Systems at UC Berkeley & MA in Japanese from FESU, Russia. At locomizer, Alex is responsible for business development/customer development, product strategy/management, and daily operations.

 

With the growth in Location Based Services what makes Locomizer different from the other companies out there?

First of all the following questions must be considered:

Does your company take advantage of location data to improve marketing and if the answer is yes then Locomizer is definitely for you. If the answer is no, then you should really reconsider the use of geo-data because places we visit or come nearby in our real like define our real life interests.

The idea is very simple but significant because “location is our identity”. Location data is generated across multiple verticals like social media, mobile telecoms and credit card payments for example. We all implicitly or explicitly leave digital footprints linked to physical location as we stay and move from place to place. Our strong belief that the era of persistent location is upon us in which the users location data is going to be constantly used to describe, sort and match people based on their attraction and repulsion to real life activities.

The audience discovery engine helps smart companies to take advantage of location data to understand consumer behavior and deliver relevant information.

What is the key USP (Unique Selling Point) for Locomizer?

If a company has location data on its customers, we can build highly targetable user interest profiles by identifying behavioral patterns from their location history. This enables companies to discover and uncover the right audience for the right product and service……

……Simply by answering the question “who to target?”

If companies do not have location data, there is a solution via our geo-behavioral interest graph. An interest graph is an aggregated view of footfall traffic by interest and time interval thus giving contextual knowledge in any given hour, week or month. This helps in being able to make decisions about when and where to target your audience.

Locomizer is backed by more than 10 years research on the behavior of cells. Development of Locomizer’s core algorithm has been inspired by Biology giving an accurate insight to consumer behavior as compared to machine learning because it is based on principles which are 

inherently natural.

That is interesting. What is this “biological algorithm”?

The biological algorithm is based on the way cells behave in space and time. This principle can be applied to explain and predict human behavior – this is the core idea behind the research.

Does this mean that you have identified a correlation or a pattern?

It is not a correlation. The research involves some general rules of science that have been used to understand cell behavior which have been adapted to understand and predict human behavior.

How do you identify an audience for a company?

People make decisions to visit a place which are based on historical patterns as well as needs and desires. Alongside our biological algorithm, research done by other companies and scientists have identified that individuals follow a certain trajectory of movements. This is based on an historical pattern and is not just a random.

We are all creatures of purpose and habit.

If there is a known history of visiting locations Locomizer is able to make accurate predictions rather than guesses. But this is not the core technology. The core of the technology is to take all the personal footprints and place everyone on the same scale (bench mark or ruler).

As an example, the history of three people visiting a location where cinemas are prevalent, like Leicester Square London, can be used as a benchmark. Each person would enjoy visiting this location and do so on a weekly basis but each one would have a different behavioral pattern.

If one of the individuals visits Leicester Square and it is known that he frequents the cinema regularly, his behavior would not be difficult to predict. The opportunities are plenty and the effort made is minimal on the behalf of the individual.

If the second individual has to travel to visit the cinema from a location that does not have one, then this individual would be of interest. He would be visiting the cinema with purpose and would spend time and money to fulfill this need.

In the case of the third individual who frequents Leicester Square because he works in that area there is plenty of opportunity and interest in additional services, such as cinemas and restaurants which can be pushed to him via mobile advertising.

How different is this from Foursquare who get users to checkin to a location?

First of all Foursquare does not offer detailed profiling – what they do is count where people visit and how many times. Foursquare does not take into account the reason behind being in a particular location but only that the person was there – Locomizer takes this into account.

Counting could be equated to rational behavior and we are not rational beings. Foursquare relies on the checkin but for Locomizer it is not a question of checkin but the context of being around a location.

Mobile telecoms collect less accurate location data than Foursquare (within 50 to 150 meter accuracy). Locomizer can work with this and create accurate profiles. 10 meters can make a difference in the profile of a person if using Foursquare but with Locomizer we look at the context which relies on many points. If there is a shift of 10 meters or so due to changes in infrastructure of a location the context remains similar – we can extract the same value from changing circumstances and hence less accurate data.

What actually is provided is footfall traffic by time in a given location which could help retailers, for example, to offer time and location based product promotions.

What about privacy?

The profiles generated don’t tell where individuals spend their time – it is more about how profiles of various sorts are attracted or repelled to/from certain locations by historical activity.

So if an individual visits a place where a cinema is located, that individual would not be that much different than a person who visits the cinema or restaurant in the same location in the same time frame. Both individuals will have separate profiles but “personal information on location, times of visit and where visited” is not gathered or recorded.

As an additional example, a profile may indicate that there is more of an attraction for visiting the cinema than the previous month. It does not indicate how many times the cinema was visited or which cinema.

It gives an indication of a profiles propensity to be around cinemas as compared to the average person who would not visit the cinema in the same time frame of a couple of days, weeks or months.

What about data gathered by mobile companies – how would you deal with that?

We are not data collectors but data processors. We rely on third parties in establishing permission of end users. If the provider wants to store data, consent must be given by the customer.

Locomizer’s is aware of the privacy issue with our aim to protect the consumer. To tackle this we can create accurate profiles without the users ID and telephone numbers. The profiles created are accurate but totally abstract.

Location Based Services stagnates due to “Privacy Concerns”

Privacy issues are inherently important in the adoption of certain technologies, particularly to Location Based Services. In 2003 a Context Based Research Group report highlighted the collapse of public versus private geographies referencing mobile phone use. Further research carried out by Driscoll (2002) highlighted that consumers would use location Based Services as long as the potential benefits outweighed the drawbacks and were receptive to the notion of location based advertising, if it was not intrusive and reduced their fees.

The latest Pew Report has brought to the fore that the adoption of Location Based Services by the consumer is stagnating as compared to other services such as video calling. Video calling on mobile phones is finally going mainstream, with usage increasing from 7% of cell phone owners in 2010 to 21% in 2013. But growth in location-sharing is fizzling badly, increasing from 5% in 2011 to just 8% in 2013.

This can be further contrasted by comparing income levels. Video calling grows in popularity the more money consumers make — it is used by 16% of people making less than $30,000 and by 29% of people making more than $75,000. Nearly all advanced mobile services follow this pattern; usage increases the more sophisticated the consumers get. But this is not what is happening with location-sharing services. Those services are used by 9% of people making less than $30,000, and just 7% of people making more than $75,000.

Certain other interesting variables have been highlighted in the report. Location sharing is apparently used less by the more educated consumers. The most likely issue is privacy with the more educated Americans are more likely to fret about the implications of sharing their location data.

This backed up by the following data. 10% of phone owners without high school diplomas are into location-sharing — but just 7% of college educated phone owners use related services.

Privacy has been an ongoing issue in the adoption of Location Based Services. With out a proper resolution where the consumer trusts this technology, Location Based Service will stagnate. Location Based Services is potentially the most relevant data that marketers would be able to use.

The next article will introduce Locomizer as potential resolution to the privacy problem.

Driscoll, C (2002, July). What do Consumers Really Think?.  GPS World, 13, p34-37

23rd IoT Meetup

I attended the 23rd IOT Meetup in London last night. They say that the number 23 has some mysterious significance and truth be told I think that could be the truth. The event was an exciting as ever but more so with the quality of speakers that presented last night. The following is brief breakdown of the event..

The meetup was hosted at the Head Office of Xively.  Xively is a Platform as a Service that provides everything you need to simplify and accelerate the creation of compelling connected products and solutions.

The first speaker was David Ryan who was discussing Argot software development kit he has developed over a period of ten years with a live demonstration. Basically Argot is a series of tools and libraries designed to help software developers build languages for the Internet of Things.  It uses the concept of a compact extensible metadata dictionary that can be embedded on the smallest of devices. The Argot dictionary allows devices to store and document the structure of the data they use in communication.

The next speaker was the most intriguing of all.  Rachel Rayns is self driven entrepreneur, Artist & Maker and Raspberry Pi Fan.  Rachel discussed her transition to being the first Artist in Residence at the Raspberry Pi Foundation.  Her full presentation can be found by clicking the here.  Rachel’s main goal is talking about the Pi’s potential as a tool for artists.

The last speaker was Dominique Guinard who talked about the developments of Android as the Universal Gateway for IoT.   Dominique’s talk covered the developments of QR codes, Near Field Communication and Arduino in IoT. Dominique’s full profile can be found here.

Finally a big thank you to Alexandra Deschamps-Sonsino, Founder of the Good Night Lamp and a major influence in making sure that IoT London Meetup runs smoothly.

#geomob London

I managed to get to a very exciting meetup this week entitled #geomob based at the Google Campus in London. This is the first meetup that I attended in a few months and I was not disappointed. The meetup is one of the leading events for the open source geo community.

All the companies that presented looked at the most innovative ways of mapping data. First off was a Noah Veltman who mapped the history of San Fransicso streets. He discussed the various pros and cons of the project which can be followed by clicking this link http://sfstreets.noahveltman.com/

Savio Dimatteo a software engineer for Lokku-Nestoria discussed some of Nestoria’s geocoding challenges. Nestoria is one of the leading property search engines in the UK.  The UK is particularly easy to map but the talk covered issues related to mapping places where data is very limited such as India.

Michael Tandy is a software developer for the online grocery delivery company Ocado. Michael discussed the false ideas that programmers have about names, time and geography with numerous examples of all. Michael’s site is http://www.mjt.me.uk/

Raymond Kenney, the co-founder of Inquiron and Mapsdata show cased Mapsdata services. The service is a user friendly program that helps to visualise sales, demographic and social data in very easy steps. Interpretation is more effective than the use of spreadsheets and is illustrated in an innovative way that helps to visualise complex results. Cross-correlation of various data sets such as World Bank Data, Twitter and other open data. For further information please click the following link: http://www.mapsdata.co.uk/

The most exciting presentation was by the co-founders of ViziCities. Peter Smart and Rob Hawkes’ main inspiration was SIM City, which I am sure that you have played at one time or another. Vizicities is effectively a real time version of the game with many potential applications which include gaming, infrastructure projection as well as social study.

In conclusion, London is hosting the most exciting and cutting edge innovative companies in the world. Mapping big data is not just about analysis but also about presentation.

Firefox Smartphone – catalyst for change?

Firefox Operating System (OS) started life in July 2011 as Boot to Gecko, a Mozilla project aimed at creating a slimmed down operating system for mobile devices. The structure and philosophy behind the project was very simple and is now coming into full fruition. It is built on a straightforward premise that the web is a platform and therefore is the heart of the user experience.

What this means is there is a directness between the hardware abstraction layer and the web with no extraneous layers between the Kernel, Gecko (the Firefox engine) and Gaia (the user interface). So Gaia is written in Javascript and the OS uses customised version of Gecko to enhance and facilitate high end performance.

What else makes Firefox OS stand apart from Android and iOS is the simple and trans formative idea that the prevailing code of native apps, locked down platforms, proprietary software stores and capricious developer rules is limiting and unnecessary and can be transformed by web based apps that interact with stripped down and optimised OS that does little else but act like a phone and converse with the web.

This all may seem a bit idealistic but Firefox will be fulfilling unmet needs and opportunities by running high grade web apps on a low end feature phones thus delivering a better smart phone experience to a higher proportion of the population worldwide.

Mozilla is planning to make Firefox OS accessible to 2 billion people who have never experienced an affordable, fully hackable mobile OS before. This opens up a multitude of different opportunities to developers in a culture where the governing rules for the ecosystem will be looser which will lead to freedom and innovation.

So what is the ideal market for this? Well, 58 per cent of devices sold in Latin America that cost less than $100. This market is out of reach for iOS or Android but is a perfect fit for Firefox which has a lighter footprint and can act as a vehicle for web apps and low end devices.

In conclusion the killer USP is the software which is optimised for low end devices, where Firefox OS and its apps are one layer closer to the hardware; so less memory and CPU is needed to give the same performance as on high end devices. This could conclusively change the dynamics of the smartphone market as it stands today.

Windows 8 and Nokia – A closer relationship

Despite the growing dominance of Android, Nokia the Finnish mobile phone maker has stated that it will maintain its primary focus on Windows based smartphones.  Stephen Elop, chief executive  of the Nokia Corporation was speaking to reporters in Bangkok on the 6th February, when he made the statement.

Nokia has maintained a strong presence in Thailand for 20 years and it is in the top-14 focus country for Nokia globally.  Stephen Elop expressed confidence that Nokia will regain a dominant position in the smartphone market and regain leadership of Thailand’s overall mobile market.

Stephen Elop is aimed at working closely with the mobile operators and retailers in transforming Thailand’s mobile industry into third generation technology from the traditional 2G.

The company is planning to broaden is product portfolio by adding both higher-capacity models and lower price points to its Windows-based Lumia smartphone line, said Mr Elop.

Nokia is managing its transition period for its five components: network, mobile phones, intellectual property and patents, location-based service and smartphones.  More apps will be offered on top of the 125,000 and it already has gained traction with navigation and mapping apps which have achieved high success among motorists.

An interview with Asif Khan from TheLBMA

The following interview was carried out with Mr Asif Khan on 13th November 2012 at Wallacespace in London.

Asif, a proud Canadian, is a veteran tech start-up, business-development and marketing entrepreneur with nearly 15 years experience. He is currently focused on working as a consultant, speaker and venture capitalist to the location-based marketing services community. In support of this, Asif recently formed the Location Based Marketing Association – whose goal is to educate, share best practices, establish guidelines for growth and to promote the services of member companies to brands and other content-related providers.

When I saw your talk on Untether.talks in the of Summer of 2012, I immediately thought that you were talking about the Internet of Things when you were referring to the connectivity of devices. Was that the case?

No.  The Internet of Things is not very practical today because the media envisaged is not geo-taggable and because this is the case you cannot easily create relationships between the content that sits on those mediums and where the consumer is located to them.

We can make it happen by cobbling solutions together but it would be very forced. So the solution is to move it to an environment which is much more seamless and simply being enabled ie where personal data is being made available, shared and utilised.

As an example if I were to walk into Starbucks today and check-in on Foursquare, that to me is the consumer saying that I am here in this space and sharing my location to those that follow me on that platform (the reality is that data of real-time location is available from the Foursquare API). Across the street where I am sitting is a billboard for BMW. Today it is very difficult for me to establish a relationship between an ad on the billboard and the consumer sitting in a coffee shop.

But surely that relationship is established by merely looking at it?

Yes. And in a present real life scenario, what should be happening is that owner of the ad should be picking up on the API data of Foursquare of the people entering the coffee shop and then should respond by engaging them in a mobile way by acknowledging their location, get them to look at the billboard and get then get them to interact in some way.

If you do that what has actually happened is that I have leveraged Location Based Services on a mobile level by making the billboard one to one measurable, which it is not today. What I am trying to do is to create a correlation between mobile location awareness and content sitting on another media that is in close proximity to where I am. Technology is available to make that happen but I have to do that manually.

Although the billboard company has a database of the boards located in the city with the various ads on each, they do not have an API for that data. If they did what I can do is to match that API with the Foursquare API in real time.

When do envisage that to happen?

Realistically this would happen between 18 to 44 months. From our perspective the billboard company and digital signage companies all understand that location is something that has to be part of where they go. The challenge becomes when they all see the value and then the question then transforms to, ok do we do that and keep private information or do we actually share it with others including our competition? Until this is a shared resource this still is very difficult.

The initial step for a billboard company would be to put it on a system but the following step is really crucial. Will the billboard company make that data available to exclusive clients as oppossed to everybody? Or do they decide to make it available to everyone and share that data on a common system with other billboard owners who have decided to so the same thing?

Obviously this is what TheLBMA would like to see because we think that the more information that is out there the more addressible it is.

In that sense it is not really about “The Internet of Things”, even though it is a noble concept. But the practicalities simply do not exist because the data partnership associations are relatively difficult to achieve. So it really is an internet or media cycle so to speak of “geo-addressable objects” as oppossed to a “live internet of things.”

So what would you rather name it then if not the Internet of Things? The term “geo-addressable devices” is too technical.

Not really. We talk about it in the context of a relationship between people, places and media and, we really have not looked at coining terms around a replacement for the term IOT. I only take issue that the term Internet of Things is really not a concept of things and is a stretch in some respects.

We can look at cars, which are internet enabled, but for me it is not only about being internet enabled. Going back back to the billboard example. That is just a piece of paper which is not internet enabled and never will be. Nor is it part of an internet of things or part of a geo aware addressable media.

If the billboard has an API behind it, it has geo information associated with it. So I can try linking that data with another device such as a phone that does have a sensor so a relationship can be created between these two objects. One is sensor free and the other is sensor rich. This is not limited to an internet sensor enabled world.

So the billboard and the mobile device don’t have to be connected at all. So if I am in Starbucks, I check-in on Foursquare and the data from that check-in via Foursquare’s API is available, so there is access to one feed of data.

The billboard has another set of data that is another data feed and when both data feeds are combined, this creates a relationship between the consumer whose share is in real time via sensor.

Practically speaking, say that the billboard was enabled by augemented reality or something that I can scan that logo via the mobile device; the billboard itself does not have a sensor but drives a reaction. Now that reaction based on profiling and other information sitting on my device.  And the billboard in it self is not a thing that is internet connected and will ever be. How does that fit into the concept of IOT? It does not.

The mobile device that I am holding in my hand and the billboard have a geo-location which is one way on how they relate to eachother. This what we look at – relationships between objects, between media types.

You mentioned in your talk at Untether.tv “ that there will be no privacy”. Can you expand on that?

When we talk about sharing your location I firmly believe that every person will share location in exchange for information which is uniquely, individually, valuable and relevant to them. For it is about the exchange of data. Studies have shown that the number people who are sharing thier data has increased over a two period.

That is not say that there should not be guidelines and boundaries. At the end of the day all these factors, whether it is mobile. location, social is all about data – it is about the data that we collect, it is about the data that we interact with and putting datasets together. Data is power and at the end of the day the question transforms into how do we use that data in ways that are not offensive, taking very careful consideration of what the consumer actually wants and what is valuable to that individual consumer.

As an example O2 ran a campaign around their geo-fence deals in 2011 and gave the option to their 26 million customer base to opt-in.  From the summer to christmas in 2011, 13 of 26 million opted in and they are at 20 million (as of writing this article).

This is based on a couple of things. Firstly it is the consumer’s personal carrier which gives a certain level of trust, which is based on an established billing relationship so there is already some financial transaction. Secondly, when they released the program the consumer was given controls in defining their profiles ie the consumer gives certain factors that they want information sent to them via geo-data ie “only want to talk about shoes” and “send only four messages a week”.

There is privacy with the consumer controlling the filter of information that they want to share.

This is summed up in a simple equation : value + relevance = here is my information.

But value and relevance have to be at an individual personal level ie individual consumer profile.

What is the next step for LBS? Is LBS and LBM now synonymous?

For me LBS is a technology platforms that basically help us determine where someone has checkin. Marketing is how we apply a message based on the data collected via the service. The next step is an improvement in the way data is collected in the platform. Today a lot of the services are based on an app.

In order for the app to be successful it has to be downloaded by many people. People have apps on their phone that have been downloaded but are not used. Only a couple are actually used. So what will change is that content will be delivered to the consumer purely in a contextual framework.

As an example, there is an app for the British Museum which I would use only when I visit it. When do I get it becomes the question. As I approach the Museum, location awareness perspective will understand that I am coming into proximity to it, so it is intelligent in that context and will send a push application to my device. This will contain the message about the benefits that the visitor would enjoy if they used the app. The next question would be permission to push it to the consumer.

Once pushed the app enhances the visitors experience. Once the visitor has completed the visit the app would thank them for visiting and give them a choice if they would like to remove the app from their device. Content over app being delivered completely in context to where I am and what I am doing.  It has to be contextual.

As the head of TheLBMA, what is your ultimate goal?

Our goal is to primarily get marketers, advertising agencies and retailers to look at the power of location and what it can do across all media that they are investing in. The reason for that when you look at advertising spend globally today, 76% is going into traditional forms of advertising and 6% are going into mobile.

The problem that is emerging is that you have a thousand platform companies approaching brands to initiate a campaign who are vying for this 6% allocated budget.

What we try to teach is the principle that says that this is not the only way.  What we really should be doing is to build platforms that leverage the power of location and use them to better actually affect better performance measurability on traditional media assets ie enhance them.

Why? Because that is where the real money is. Back to the Starbucks, billboard example.  If BMW is initiating a billboard campaign and has asked various LBS platforms to pitch to them, Foursquare for example would need to convince the company that they can make their billboard campaign sell $1million more measurable one to one effect and can do something to connect with what is happening on a ground level.  This may convince BMW to transfer some of the larger traditional budget over to mobile.   This is coming from an understanding that this is from connected media not from mobile by itself.

So the key thing is getting organisations to understand that any media can be location based.

The Internet of Things and The Good Night Lamp

The Good Night Lamp is for people who are away from home and would like to keep in touch with their loved ones. But this is not unlike anything that you have seen before.  It is an innovative product which uses the idea of “The Internet of Things”.

For those who are not familiar with “The Internet of Things” or IOT for short, this is defined as a computing concept that connects everyday physical objects to the internet and are able to identify themselves to other devices.  The technology is synonmous with RFID as the method of communication which includes other sensor and wireless technologies.

The Good Night Lamp transforms the IOT from a concept to a very viable, tangible and practical product.  It comprises of a “family of lights”, made up of a larger light (primary) and two smaller ones (secondary).  All the lights are made in the shape of a home.  The idea in a nutshell is that it, it is possible to communicate one’s homecoming remotely to other holders in the “family”.  The smaller lamps (secondary lights) are synchronised to the larger lamp (primary light) so once the primary lamp is lit, the seconday ones also light up.

This is ideal in circumstances when a family member is away on the other side of the globe.  When the person has arrived or is back from a meeting, they could remotely turn on their lamp which in turn will also synchronise with the lamps on this side of the world.

The idea has gained prominent attention and the project is just taking off on KickStarter after a little seed funding from friends of the team that have supported the project.  The team comprises of four very clever and innovative people which include Alexandra Deschamps-Sonsino (Founder), John Nussey (Head of Products), Adrian McEwen (CTO) and Konstantinos Chalaris (Lead Designer).

Alexandra has written the project’s experience on KickStarter for other flegling ideas that need that initial financial and marketing push.  The Good Night Light project can be followed on twitter @GNLteam and with their official website Good Night Light Website and for more information of their progress on Good Night Lamp on Kickstarter.