My Digital FootPrint

The Strange Order of Things: Life, feeling and the making of cultures by Antonio Damasio Follow him on Twitter ---- For decades, biologists spurned emotion and feeling as uninteresting. Antonio Damasio, a professor of neuroscience, psychology and philosophy, sets out to investigate “why and how we emote, feel, use feelings to construct ourselves and how brains interact with the body to support such functions”. He demonstrates that we are our chemistry and that feelings are central to the life-regulating processes which keep us alive and thriving. What the body feels is every bit as significant as what the mind thinks and we can turn to emotions to explain human consciousness and cultures. It is worth spending a few minutes reading the praise section as the depth and quality will confirm this book is worth taking seriously. You may not like the content or agree with it - but it is worth reading. The link here to data and sensors is that maybe we can deter

Chuck Hollis from EMC wrote a great blog “ Learning to get comfortable with sensorship ” – As the blog starts “ No, that's not a typo, but a twist of a clever phrase that our own Thom Lytle used to describe our increasing participation in sensor networks of all kinds.” I one hundred percent agree that we are the sensor and have written about it many times and you cannot escape it.  You are either generating the data yourself on your devices, you data about you is being captured by someone else.   Bottom line: there is no way I can participate in modern life with being an unwitting participant in untold dozens of information gathering exercises.   Me?  I am with Chuck, JP, Jeff Jarvis and many others on this and I've decided to embrace my sensorship.   The potential for bad is far outweighed by the potential for good -- both for me as an individual, and for me as a member of society.   I choose to be sensored. There will be occasional unpleasant experiences ahead I'm

BusinessWeek  reports  on retailers’ use of RetailNext camera surveillance software to glean intelligence from shoppers’ behaviour.  The software takes a video feed to analyze customer behaviour.  RetailNext  is a real-time in-store monitoring, enabling retailers and manufacturers to collect, analyze and visualize in-store data.”  RetailNext’s software can integrate data from hardware such as RFID chips and motion sensors to track customers’ movements.  The company explains that it “tracks more than 20 million shoppers per month by collecting data from more than 15,000 sensors in retail stores.”  Its service apparently helps stores figure out where to place certain merchandise to boost sales.  3VR is used by T-Mobile to track how people move around their stores, how long they stand in front of displays, and which phones they pick up and for how long.  3VR is testing facial-recognition software that can identify shoppers’ gender and approximate age.   Radical point: facial recognit

What can you do with data if you can collect it… Acceleration, Vibration, Motion/Velocity/Displacement, Position/ location, Proximity, Pressure, Force/Strain/Load/Torque, Vertical Height above sea level, Sea/land motion activity, Movement Water levels, Leaks, Chemical/Gas, Odour, Pollution, Biological process, Flow, Temperature/ heat production, Humidity/Moisture, Air pressure Sound/ Ultrasound, Electric, Light, Solar intensity, Magnetic, Background noise, Signals Images, Video, What is near by (saying hello), Routes and routines, key strokes, downloads, usage, content, http://www.societyofrobots.com/sensors.shtml

This is Carlo Ratti speaking on Architecture that senses and responds Whilst we love to think that we create our own digital footprint, the more sensors there are there more something else know about us......

This TED talk  by Julian Treasure  - 5 ways to listen better. He has several versions on the same topic and theme http://www.juliantreasure.com/Julian_Treasure/Home.html  The reason for putting it here on a blog about digital footprints is that listening is part of our digital footprint...and sensors are trying to do what Julian describes for us to help in that filter.

Mobile Web 2.0 – value lies in getting data out from a device   We have discussed Mobile Web 2.0 and Web 2.0 extensively above. We have seen that Web 2.0 could be viewed as harnessing collective intelligence and, by extension, Mobile Web 2.0 could be viewed as harnessing collective intelligence from mobile devices. This is depicted in Figure 15. Figure 15 Moving focus to getting more data off a device than on to it The ability to get data out of or off a mobile device lends itself to the unique advantage a mobile device has. We explore this idea in greater detail in subsequent chapters. Considering this concept, that there is more value in getting data off a mobile, let’s consider that sensors (acceleration, temperature, noise level) can easily be placed in or attached to mobiles. Further, a user can send information from their device, by voice, IM (Instant Message) or text, to a centralised service point. Both sensors and people can provide vital data during a disaster-relief operatio

Mobile Web 2.0 – value lies in getting data out from a device   We have discussed Mobile Web 2.0 and Web 2.0 extensively above. We have seen that Web 2.0 could be viewed as harnessing collective intelligence and, by extension, Mobile Web 2.0 could be viewed as harnessing collective intelligence from mobile devices. This is depicted in Figure 15. Figure 15 Moving focus to getting more data off a device than on to it The ability to get data out of or off a mobile device lends itself to the unique advantage a mobile device has. We explore this idea in greater detail in subsequent chapters. Considering this concept, that there is more value in getting data off a mobile, let’s consider that sensors (acceleration, temperature, noise level) can easily be placed in or attached to mobiles. Further, a user can send information from their device, by voice, IM (Instant Message) or text, to a centralised service point. Both sensors and people can provide vital data during a disaster-relief operatio

Mobile Web 2.0 – value lies in getting data out from a device   We have discussed Mobile Web 2.0 and Web 2.0 extensively above. We have seen that Web 2.0 could be viewed as harnessing collective intelligence and, by extension, Mobile Web 2.0 could be viewed as harnessing collective intelligence from mobile devices. This is depicted in Figure 15. Figure 15 Moving focus to getting more data off a device than on to it The ability to get data out of or off a mobile device lends itself to the unique advantage a mobile device has. We explore this idea in greater detail in subsequent chapters. Considering this concept, that there is more value in getting data off a mobile, let’s consider that sensors (acceleration, temperature, noise level) can easily be placed in or attached to mobiles. Further, a user can send information from their device, by voice, IM (Instant Message) or text, to a centralised service point. Both sensors and people can provide vital data during a disaster-relief operatio

Mobile Web 2.0 – value lies in getting data out from a device   We have discussed Mobile Web 2.0 and Web 2.0 extensively above. We have seen that Web 2.0 could be viewed as harnessing collective intelligence and, by extension, Mobile Web 2.0 could be viewed as harnessing collective intelligence from mobile devices. This is depicted in Figure 15. Figure 15 Moving focus to getting more data off a device than on to it The ability to get data out of or off a mobile device lends itself to the unique advantage a mobile device has. We explore this idea in greater detail in subsequent chapters. Considering this concept, that there is more value in getting data off a mobile, let’s consider that sensors (acceleration, temperature, noise level) can easily be placed in or attached to mobiles. Further, a user can send information from their device, by voice, IM (Instant Message) or text, to a centralised service point. Both sensors and people can provide vital data during a disaster-relief operatio

Like Neil Armstrong, whilst walking on the moon, and Nelson Mandela walking free from Robben Island , we all leave footprints. Footprints are more than identity. Footprints are about where we have been, for how long, how often and the inter-relationships, they are memories and moments. Therefore, digital footprints are not about your identity, your passport, bank account or social security number. Digital footprints come from your mobile, web and TV interactions and comprise the digital data and also the Metadata [i]   (data about data) of who we are, the true value and why the ownership of this data class is the battleground to be won and lost.   However, the original web-based digital footprint and its digital data belonged to the individual at some point. But the individual is currently not empowered to hold or manage this digital footprint. Mobile adds a unique dimension to the digital footprint since mobile provides new content, Metadata and the social context for the digital fo

New Social Rules for Living in a Digital Age If we think about current digital services, such as social media, as a game e.g If Twitter is about getting the best quip of the day or providing some useful info, Linkedin is about proving "my network is bigger than yours", and Facebook is about sharing that "I have a more interesting life than you";  then there must be some new rules for these new games, but what are they? Before the new rules it is, I believe, worth confirming and stating that all of the old rules are still valid, relevant and have not been washed away by this new digital age.  A few examples of old rules that are timeless would include:- 1.       Don't gossip, make things up, slander, steal, pinch or lie 2.       Have evidence and be professional, factual, accurate, honest, and transparent 3.       Engage and treat others how you want to be treated yourself 4.       Opinions are personal, be gracious, open, respectful and accepting of di

Guest blog from Ajit Jaokar .   Original post is here Overview The many privacy related issues raised by the Web will be amplified in the world of mobility and even more so, in a world dominated by sensor networks. Current thinking seems to converge on one important conclusion: through the combined interaction of law, technology and Internet literacy, people should be in a position to control how their own personal information is made available and used for commercial (or other) purposes. In this post, we explore the feasibility of users managing their own data. i.e. if we indeed want users to manage their own data, what are the issues involved in making this happen? We also look at an alternative i.e. allowing devices to mirror social privacy norms. Hence, I see the discussion as ‘Changing user behaviour to incorporate new device functionality’ OR ‘Changing device behaviour to mirror privacy expectations in human interactions Privacy and management of data – A background

Soap box ramble - Currently we use motion sensors to switch on lights or set of an alarm (intruder).  What will happen when these sensors knows it is you, and I mean you, you become uniquely identifiable. Suppose your home sensors detect who is going into the room and set the lighting, TV to what you like.  It sends you a text to say which off the lights, or reminds you that the oven is on.  Will we see this as centralised big brother or that we can negotiate with our power companies to get cheaper bills when we move supplier by showing we are responsible and accountable? Should the marketing be about reducing the standby energy production amount and therefore reducing pollution and extraction, or should we just be doing it anyway.  Is this technology looking for a problem? Data, smart grids, identity, user behaviour.  Will you change if you knew your actions could count against you? Will the cost of implementation be higher than any return?  As I said, mutterings of a mad man th

Reality mining is according to MIT is “the collection of machine-sensed environmental data pertaining to human social behavior. This new paradigm of data mining makes possible the modeling of conversation context, proximity sensing, and temporospatial location throughout large communities of individuals. Mobile phones (and similarly innocuous devices) are used for data collection, opening social network analysis to new methods of empirical stochastic modeling.” We know that every time you search (text, image, video), you give Google some value, you give even more value when you click on a result.  From each search and click Google ‘learns’ and increases it knowledge and value.  Google doesn’t need you name or address to do this, but with Picasa facial recognition, oddly enough they do know what you and your family look like. However android opens up the opportunity to gain all the mobile operator data as well. Mobile operators know who you are and where you live and from your call