All things considered, this Julian Bleecker’s Why Things Matter is probably the closest we have to a Manifesto for the internet of things. Bruce Sterling’s Shaping Things is also in that category, but it’s a longer text and it lacks the theoretical punch. Besides, any theoretical piece on networked objects has to first deal with the modern separation of the world into the nature/culture dichotomy, and Bleecker does just that with his use of Bruno Latour’s We Have Never Been Modern.
As I discussed here, and here, Google seems to seriously plan for and work towards a prime position in cloud computing (web 3.0?). A couple of interesting links relate to that. First comes the now infamous interview Eric Schmidt, Google’s CEO, gave at the WSJ. In that interview he made a number of comments indicating where Google are looking at the moment, but for some reason all it was remembered for is his quip that because of privacy issues with social networks in the future kids may end up having to change their names when they reach adulthood. Ok, this is odd, and it came out of nowhere, but surely there are more interesting bits in what he had to say. Much more interesting for example is his hint that Google are seriously working on developing semantic algorithms:
This statement has to be read in the context of Google’s move to the cloud. In that paradigm the semantic depth of your search query will be provided by your entire cloud footprint. This is quite literally an Artificial Intelligence in full operational mode. As William Gibson writes in a recent article in the New York Times,
“We never imagined that artificial intelligence would be like this. We imagined discrete entities. Genies.”
We imagined HAL, and Wintermute, but instead of managing an ultimately controllable anthropomorphic machine we have to deal with a distributed mind that is built of…us. An ambient socio-digital system.
In part 1 I mentioned Google’s focus on low latency sensors and massively redundant cloud data centers. Google is not the only company in the race though, and probably not the most advanced down that road. Ericsson – the world’s largest mobile equipment vendor – is seriously planning to operate 50 billion net-connected devices by 2020. Only a small fraction of these will be what we consider as ‘devices’ – mobile phones, laptops, Kindles. The enormous majority will be everyday objects such as fridges (strategic object due to its central role in food consumption), cars (see the new Audi), clothes, basically everything potentially worth connecting. This implies an explosion in data traffic.
As Stacey Higginbotham writes over at Gigaom:
So even as data revenue and traffic rises, carriers face two key challenges: One, the handset market is saturated; and two, users on smartphones are boosting their consumption of data at a far faster rate than carriers are boosting their data revenue. The answer to these challenges is selling data plans for your car. Your kitchen. And even your electric meter.
In other words, it is in the interest of mobile providers to extend the network to as many devices as possible so that they can start profiting from the long tail. As the competition in mobile connectivity is fierce and at cut-throat margins, the first company to start mass-connecting (and charging) daily objects is going to make a killing. Hence Google’s focus on sensors and data centers.
This presentation by wireless analyst Chetan Sharma outlines the motivation for mobile providers to bring the internet of things as quickly as possible.
Bruce Schneier has posted over at his blog the following draft of a social networking data taxonomy:
Why is this important? Because in order to develop ways to control the data we distribute in the cloud we need to first classify precisely the different types of data and their relational position within our digital footprint and the surrounding ecology. Disclosed data is of different value to Behavioral or Derived data, and most people will likely value their individual content such as pictures and posts much more than the aggregated patterns sucked out of their footprint by a social network site’s algorithms. Much to think about here.
Data, data everywhere, The Economist, on how to manage superabundant data clouds
The data deluge, The Economist, on the implications of data clouds for business, governments, and society
Cloud