We are moving towards a highly connected world with an impressive amount of data around us.

Systemic interdependencies of the socio-economic variables of the hyper-connected world we are living in (credit: World Economic Forum)

Have a look at this picture showing just a simple representation of part of the systemic interdependencies of the socio-economic variables of the world we are living in.

In this direction towards an hyper-connected world, thinking at the fast evolution of pervasive computing and networking “at the edge” (i.e., around the Users), there is a growing interest in finding ways for understanding better (steering, controlling) the dynamic emergence of connected groups of entities (e.g., nodes/devices, things, Users).

This is not only for the design of the self-control of dynamic networks hooking a sheer number of physical and virtual resources, but also for learning the highly interconnected Users’ experience behind services such as those provided by Google, Facebook or Twitter… (i.e., involving any sort of relationships among people and data through social networking tools). Two sides of the same coin. Still we find systemic interdependencies.

Modelling “emergence” (and taming “butterfly effects”) is a well-known problem, which has been already analysed in several other contexts of Network Science (e.g., in medicine, biology, neuroscience,  etc). Emergence can be seen as the aggregation of “mesoscopic structures” which are dynamically and spontaneously aggregating in highly connected (collaborative-competitive) environments.

Needless to say that a inner “mathematics of emergence” (if any) may have interesting business implications for any Telecom or ICT Player wishing to gain a winning role in future networks and services ecosystems.

Bose Einstein Condensation (Credits: mit.edu)

Interestingly, some studies from statistical physics, which are modelling each node (or device, or ensembles of data) in a network as an energy level and each virtual link as a particle, show a perfect analogy between the mathematics of a network and the mathematics of a Bose gas: just have a look at this paper.

It appears even possible to argue that “first-mover-advantage”, “fit-get-rich,” and “winner-takes-all” strategies observed in collaborative-competitive environments emerge from the underlying dynamic networks in which single nodes captures a macroscopic fraction of links. And the “temperature” (T) is the main controlling parameter.

It might be possible even applying the same model to the human minds, which are behind the nodes and are, in turn, influenced by their interactions. Each single thought can be seen as an energy level (e.g., even the psychologist R. Assagioli argued something like that!) and association of a couple of thoughts as a particle: well, this is just another level of abstraction, but the inner mathematics, might be indeed the same. This is the universality of Nature’s laws.

Then, at the end of the day, we are talking about nested multi-scale networks, creating collaborative-competitive environments probably based on an the same inner mathematics, e.g., the one governing the condensations of “giant waves” (or certain levels of coherency) at the right conditions (i.e., values of some controlling parameters).

Concluding, I’m not arguing that future Internet (or human mind) should be modelled like a realistic Bose gas (even if this idea is very fascinating), but this reasoning brings to my mind another lateral perspective: said systemic interdependencies of a highly connected world full of data may represent sometimes a risk; for example think about unwanted couplings or over-coupled relations… do we have the instruments to tame unwanted “condensations” ? Can we “shield” Users from too many connections and too many data ?

Many people have no doubt that s/w and h/w technologies will progress so much to offer (sooner or later) Carriers’ class network solutions à la SDN (Software Defined Network) and NfV (Network functions Virtualization). I tend to agree: it is not about whether or not, but when and how. And when will be earlier than we expect now. The question, which is not that debated yet, is how, i.e., the “strategic side” of the potential adoption of these technologies progresses. Can we monetize all of this ?

It is clear that any promising technology is likely to adopted not only if it is reducing costs (e.g., Capex and Opex savings), and if it is trusted, but also if it is really able to create new businesses. Today, SDN and NfV seems to be “panacea”, solving any problems that Network and Service Providers may have in optimizing their infrastructures. But where is the real value in terms of new business? Many say “programmability”: I’m not fully convinced. It’s more, “adaptability”, to me: bringing the network on the top. I think that the point is changing perspective in taking the strategic biz advantages of the tremendous technology advances which we are witnessing: it is likely that in less than five years it will be possible to develop L4 to L7 network functions (almost) fully in software (e.g., executing them in Virtual Machines) and dynamically allocating and moving VMs over distributed resources. Throughput will be improved (as simply it will be possible moving seamlessly virtual resources closer to them, thus reducing RTT) whilst Operators will be able managing QoS policies at higher levels (than we are doing today).

This is like saying that the “mind” of an “ossified network” could be removed from today’s closed (and costly) boxes and moved up to a software flexible level, where is orchestrated, thus increasing dramatically network “adaptability”. And the network will be then “on the top” ready for new biz models, or redefining the current ones.

Please note that adaptability is a broader concept than adaptation: it’s the capacity to continue to function in an unknown or uncertain environment (as from Michael Conrad’s definition), by altering its structure and dynamics. The constraints that keep a network capable of adapting in prevailing circumstances should not interfere with its potential freedom to function in an unknown or uncertain environment. A simple example: human body shows adaptation, while the nervous system exhibits adaptability (e.g. by learning): and not for that we’re saying that the nervous system is an “over the top” on the human body !

So in this evolution we have to change the perspective about the “network”: the network will not be any more an ossified “body” connecting end-points, but it will be an highly flexible communication fabric full of software processing and storage power. As said, it is like bringing it “on the top” (of multiple physical resources, even belonging to other domains). And the value of this “new network” will increase tremendously, as the bet is on its adaptability. A way to monetize ICT technology advances, whilst leveraging one of our major assets: reaching millions of Users.

One may ask: then, where’s the “intelligence” ? Inside or outside the network ? Well, reading Michael Conrad’s book “Adaptability”, the answer is easy. He introduced the concept of “hierarchical compensation”, which is the idea that adaptation predominates at one level and adaptability predominates at another level, in a delicate balance. Needless to be more explicit on the biz implications of this which appears to be natural law.

For sure, the dynamics will be in the hands of Users (who we’re reaching) , to whom we have to look at, not as network end-points but as “minds” playing with the adaptability of this network fabric.

In this new game, there are huge opportunities for Players like us.

‘It from bit’ is an aphorism coined by the well-known Physicist J. Wheeler: he used these words to argue that anything in the physical world, i.e., any “it”, ultimately derives its existence from “bit”, i.e., “information”.

Today, we may also argue the opposite, which is inverting the aphorism and saying ‘bit’ from ‘it’. As a matter of fact, today’s technologies allow us associating “bits of information” to anything in the physical world.

One may say, this is not the type of “information” meant by J. Wheeler. Yes, this is still information, digital information. For J. Wheeler, who was a Physicist, ‘bit’ is something related to our sensory perceptions, while ‘it’ is something like a quantum field whose existence we deduce from a pattern of perceived “bits”.

For us ‘bit’ could be a piece of mined data (i.e., think about the Big Data) and ‘it’ anything around us (i.e., think about the Internet with Things).

Anyway, the concept of “information” is so profound, that defining it is not that simple. We’ve got several definitions in the past. The same for “networks”, so strictly related to the transfer of information…

A well-known  definition of information is from Shannon, which he also called entropy following a Von Neumann’s advice, by analogy with Boltzmann’s entropy in statistical mechanics. On the other hand, this definition of information is nothing more than a sequence of symbols: in practice binary numbers with their probabilities. The semantic (or even emotional) meaning of this information is up to the Sender and the Receiver, and often them may interpret the information messages differently, depending on their cognitive (internal – external) states.

A challenge that we have today is going beyond this traditional information theory, which considers the exchanges of messages between endpoints.

This has been captured very well by Frederick Brooks in “The Great Challenges for Half Century Old Computer Science”: “Shannon performed an inestimable service by giving us a definition of Information and a metric for Information as communicated from place to place. We have no theory however that gives us a metric for the Information embodied in structure. . .”

How information, and its processing, in broad sense, can be fully embodied in the structure of the reality ?

This is what happens in Nature: from electrochemical information in networks of neurons, to biological information stored, and processed in living cells, to information enabling self-organization in ecosystems… Just speculations ?

Not only. When imagining the coming of an environment around us, made of an ultra-dense intertwining of processing, storage and embedded communications, we need finding a new definition of information, related even to the cognitive and emotional processes of human mind.

This will bring, also at the same time, to a new definition of “networks” beyond the concept of connecting end-points, towards the idea of a communication fabric embedded into environment: welcome to the real future Internet.

I wish starting this post by resuming the vision of Telecommunications as a gigantic supercomputer nicely described in Roberto’s post. Internet and Telecommunications today are based on massive distributed networks interconnecting processing and storage nodes. This is recalling the image of hundreds of thousands of chips interconnected each other in a supercomputer: even more amazingly, in the near future, both inter- and intra-chip communications will be carried through optical signals. This is also what we’re dreaming for future transport networks as “photons are faster and consume less than electrons”.

Let’s make an example, looking at a data center, which can be seen as a sort of a supercomputer. The data center network fabric is a network capable of interconnecting thousands of server, storage and other network ports in a flat, ultra-low latency, high bandwidth infrastructure that provides any-to-any connectivity. A flat fabric-based network architecture eliminates the need for multiple layers, switch-to-switch interactions: it simplifies network management and operations while improving performance. Other nodes can be seamlessly added given the fabric’s high degree of scalability. Also, we may say that data center approach  is application driven, rather than network driven. Indeed this is what we’ll see in the future also for Telecommunications networks.

In the future, end Users (applications) will be more and more able to “drive the network dynamics”, introducing flooding the network by the edges: by Users it is meant not only people by also machines, smart objects, things and any device which is attached to the network at the edge. In fact, technology advances (e.g. standard h/w performance, embedded communications, device miniaturization, etc.) and the related costs reductions are progressively moving an incredible amount of processing, storage, communications-networking capabilities at the edge of traditional networks, i.e., towards the hands of the end Users. Actually, it’s a few years that we are witnessing this trends: examples are CDN delivering contents from caches at the edge, closer and closer to end Users, other edge computing services (e.g., web acceleration) provided by Players like Akamai, etc.

New paradigms as SDN (Software Defined Networks) and NFV (Network Functions Virtualization) are creating the conditions to reinvent networks architectures as they are offering the possibility to look at the whole network in an abstract way, shaping resources and connecting them in a dynamical way.

In this post, for example, I’ve already pointed out that chance of virtualizing network and service functions which are provided today by expensive L4-L7 functions middle-boxes, and moving them in the Data Centers or even better at the edge (where this huge amount of resources is accumulating), as closer as possible to the Users. This will be a big change. Middle-boxes are today closed pieces of equipments. Not only said stateful middle-boxes are breaking the end-to-end principle, they also contributing to the network ossification, but they are representing a significant fraction of network capital and operational expenses (due to management complexities).

My bet is that in the near future, the edges will look like a data center network fabrics capable of interconnecting thousands of standard hardware servers, storage and other network nodes. Edges will become like Distributed Network Computing Platforms (creating the so-called Edge ICT Fabrics), composed by pools of general purpose h/w resources (capable of computing, storage and network I/O). Edge ICT Fabric will includes Users’ devices, CPE, aggregation nodes, Edge PoPs (which can be even seen as micro-data centers at the edge). Edge ICT Fabrics will be characterized by high flexibility, performance and self- adaptation at run-time (e.g. dynamic flocking of resources according to needs). Importantly, it will be also possible harnessing and combining all unused resources (e.g. computing and storage power at end Users’ home and in the edge micro data centers). Through the Edge ICT Fabrics, it will be possible programming, allocating and moving a variety of virtual architectures (spanning across diverse edge networks or even across today Data Centers) on-demand, based on Users’ applications, also meeting governance and biz requirements (no more ossified networks structures).

This is a change of paradigm: a storm of pieces of software (even open source) executed on general purpose hardware will allow to abstract all network functions and services, in a way to impact profoundly Telecommunications and ICT business. This impact should be considered from the point of view of Incumbents’ networks, but as well as from the one of OTT, enterprise networks and consumer electronics.

Today communications networks include a range of deployed middle-boxes such as WAN optimizers, NAT, performance-enhancing-proxies, intrusion detection and prevention systems, any sort of firewalls, and other application-specific gateways… Each middle-box typically (closed and quite expensive) supports a narrow specialized function (layer 4 or higher) and it is mostly built on a specific hardware platform.

Middle-boxes are deployed along most paths from sources to destinations: that’s why networks lost the initial end-to-end principle of Internet (with packets being just forwarded). In this paper they have presented a measurement study conducted from 142 networks in 24 countries, including cellular, WiFi and wired networks, public and private networks, residential, commercial and academic networks: it sounds incredible, but it resulted that about 33% of paths tested keep state and perform some level of L4+ functionality. Not only said stateful middle-boxes are breaking the end-to-end principle, but they are representing a significant fraction of network capital and operational expenses (due to management complexities).  Above figure is extracted by this paper: it shows that the number of middleboxes is on par with the number of routers in a network. Why do not we get rid of them?

We have mentioned several times that technology records and cost reductions are progressively moving an incredible amount of processing, storage and networking capabilities at the edge. Imagine, as an Operator, to take full advantage of this trend by virtualizing those functions which are provided today by middle-boxes: in other words “dematerialized” and move them at the edge, as much as possible nearer the Users (or maybe some function could be even moved in the Data Centres). The infrastructure will be completely reshaped: Core Network will become stateless (as Internet in former days) and the Edge Networks (and the Data Centres) will become the only stateful parts of the networks! By the way, this is my personal take about the real innovation of Software Defined Networks and Network Function Virtualization: moving the stateful functions of the infrastructure part at the edge and part in the Operators’ Data Centres.

As pointed out by Roberto, in the next decade the edges are likely to be autonomous systems; I’d even say that edges will become the main stateful parts of the network, thus creating a massive distributed data base around the Users. No doubts that the amazing increase of smart nodes and devices at the edges will provide globally enough processing power, data storage capacity and communications bandwidth to achieve this vision earlier than one might expect!

And this, I guess, would allow Operators to re-shuffle the cards of the biz game, even with the OTTs, as money flows in at the first mile: the ability of managing and orchestrating the “states” at the edges, strategically around the Users and across their Data Centres, will create new biz ecosystems with new rules of cooperation-competition between a galaxy of unexpected Players.

Today there are plenty of open source software solutions that can be used to implement a fully open Cloud Computing environment; just to mention some of them: libcloud, OpenStack, NiftyName, Juju, appscale, SlapOS, buildout, supervisord, PyOCNI etc. Imagine using such solutions to create an ICT environment exploiting end-Users’ idle resources (instead of the servers in traditional data centers) for providing computing and storage services. This is Fog Computing at the Edge !

Fog Computing is about extending the Cloud Computing paradigm up to the edge of the network, by using a sheer number of unused ICT resources. It is not just a new tech buzzword, but it is about the migration trend of processing power, storage capability and embedded communications towards the edge of the network, which means in the hands of the Users. Fog computing could be also about storage for disaster recovery.

This is going to become a reality today. Symform is an example of start-up offering disaster resilience as a “decentralized, distributed, virtual, and crowd-sourced” fog. Let’s see how it works. Some Symform’s Users act also as hosts by allocating some amount of their on-site unused storage for use by Symform: pricing is 15 cents per gigabyte per month but if they provide as much storage resource as twice the data they are uploading, then their fog storage is free. When a User uploads a file to Symform’s fog, the system replicates it for redundancy, shreds it into tiny pieces, encrypts each piece, and then distributes it to other Symform Users. The system splits each 64 megabyte block of data into 96 fragments; only 64 of those fragments are necessary to recreate the entire block.

One may wonder about the performance of Fog Computing. Well, this brings me back to folding@home, a crowdsourcing initiative about computing intensive simulations of protein folding and other types of molecular dynamics. Folding@home uses the idle processing resources of thousands of personal computers owned by volunteers. As of November 28, 2012, folding@home has 208,622 active CPU cores, 10,206 active GPUs, and 4,583 active PS3s, for a total of about 5 petaFlops! (a petaFlop is a quadrillion calculations – 10¹⁵ – per second). Titan, first supercomputer in the world, has reached today a speed of 17.59 petaFlops.

Just imagine the variety of ICT services that could be executed and provided by orchestrating the idle computing and storage local resources of millions of smart nodes at the edge…one may argue that not all types of services and applications can run entirely on the edge, however, there are several examples like disaster resilience, content aggregation and transformation, data collection and analytics, static data bases, and many others (even at lower OSI layers) which can benefit from the fog.

Fog Computing vision goes beyond Cloud Computing by arguing the use a sheer number of resources distributed at the edge of the network. Apparently another buzzword. On the other hand, today we are already witnessing a progressive migration of processing power, storage capability and embedded communications towards the edge of the network; this trend, coupled with devices miniaturization and costs reductions, will create the conditions whereby Users will literally “decide and drive”  future ICT networks and services. This will have big impacts. This floating “fog” of ICT resources at the edge will give rise to new biz models based on new forms of competition and cooperation between existing Providers, and new  ones entering the arena, including utilities, car manufacturers, consumers’ electronics, public administrations, communities, etc.

In these dynamical games we’ll see innovative proposals, rewarded directly by the market itself, which will be essential encouragement for further investments. So, ideally, in the edge ICT fabric it will be possible creating, programming, instantiating or migrating dynamically different types of virtual functionalities and services as well as alternatives of the same. No more ossified architectures. In other words sort of ephemeral networks of resources will plastically self-adapt to humans’ dynamics. And it shouldn’t be a surprise discovering that this follows the laws of emergence in “complex systems”!

Emergence of flocks of birds: each individual responds to local conditions with a similar rules set.

Emergence is topic already acquiring a growing interest in social networks: there are interests in modeling and predicting the dynamics of groups of people, the viral diffusions of certain ideas or concepts, use of resources, or even the potential adoption of product and services.  Think about the the convergence of Internet and the social attitude of humans: beyond sites such as Facebook, LinkedIn, MySpace, Wikipedia, YouTube there is a broader process to form connections with others, build groups and to engage communications.  A political message, or a piece of news or a meme are examples of information that can spread from person to person, in an epidemic way. This can catch the attention of millions of people creating ephemeral human dynamics, made visible by on-line expressions.

 Modeling ICT social epidemics provides the opportunity to identify influential behaviors, or ways to predict, trigger or incentive mass adoptions of products or ICT services. Several mathematical approaches have been proposed for modeling these dynamics: a diffused idea is modeling the state of each person as a member of a lattice and updating it by using simple rules depending on the states of neighboring members. Each state could be represented by a set of variables such as cultural skill, preference, beliefs, etc. and each of them associated with a certain flipping energy, which is the cost of changing the state given its connection with other members, which are in other variables-states. At the end of the day, it’s about how environment conditions, or messages, will influence people and vice-versa how individuals influence each other and the environment.  In a next post I’ll provide some simulations examples.

 In summary, as in any complex system, also in the Edge ICT Fabric, it will a matter of taming complexity and extracting simplicity out of “local-to-global” dynamics.

DNA is the well known macro-molecule with a double helix, encoding all genetic information in a language with 64 three-letter words built from an alphabet with a set of four different letters. The used symbols are A, C, G, T and mean adenine, cytosine, guanine and thymine (thymine T is replaced by uracil U in RNA). Since the discovery of the molecular structure of DNA in 1953, by Watson and Crick, a lot of progresses have been made in studying the ensembles of molecular structures of the genetic code.

Scientists are investigating why this special language has been chosen by Nature.

As we have read in the last post there are effort for mimicking this language in informatics:    an avenue towards DNA-based computing and bioinformatics. On this matter, let me go back again to symmetry.

DNA has two helices, which run anti-parallel to each other: this is an inherent symmetry, which is highly important in the replication process of DNA. Furthermore, they say that the genetic code has an exact A-G permutation symmetry and an almost exact T-C permutation symmetry with respect to the third nucleotide. Given the enormous importance of spontaneous symmetry breaking in several physical phenomena, these symmetries in the genetic code are even more amazing!

In 1993 this paper proposed explaining the degeneracy of the genetic code as the result of a symmetry breaking process. This can be can be compared with explaining of the positioning of the chemical elements in the periodic table as a consequence of an underlying dynamical symmetry (which, in turn, are reflected in the electronic shell structure of atoms). Have a look at this recent paper to read more details about this fascinating perspective. Universal characteristics of symmetry breaking are even here, in the language of Nature.

Since the discovery of DNA huge progresses have been made in unveiling the genetic code, and the rate of discoveries in this field is accelerating day by day thanks also to the growing amounts of processed genetic data: a multi-disciplinary approach capable of integrating Mathematics, Physics, Biology, Informatics and Engineering could bring to a breakthrough, changing profoundly ICT horizons.

How can we model the dynamics of a human community, or a business ecosystem as a complex adaptive system ? I believe the basic understanding and tools needed for deal with these tasks have been already developed in other disciplines (such as Physics and Mathematics). Most probably what is required is a just a critical cross-fertilization and re-interpretation of achievements in order to make them applicable to another context (apparently far away). Let me make an example.

Symmetries in the Alhambra Palace, Granada

Think about symmetry, which is playing a key role in our understanding of Nature. Mathematically speaking, symmetry is characterized by the invariance of some mathematical object under some transformation. For example, a parabola y=x² is symmetrical with respect to the y-axis, since it is invariant under the transformation that takes the variable x and transforms it into –x.

In physics, mathematical symmetries imply conservation laws: for instance, translation invariance implies momentum conservation, while rotational invariance implies angular momentum conservation.

Symmetry breaking is an amazing phenomenon in Nature.

There are two types of symmetry breaking: explicitly and spontaneously. To understand the difference in simple terms, let us imagine that we are watching a group of people in a square downtown in Turin. People will be walking in random directions, with local aggregation patters. Now suppose that someone on the first floor of a building in the square starts to do something spectacular, then people will all look in the same direction, eventually moving in the direction of that building. This is an example of explicit symmetry-breaking: an action external to the behavior of people in the square makes all of them to behave in the same way. Coherency emerges as a result of the symmetry breaking. It’s the same principle of the laser!

Spontaneous symmetry-breaking is more subtle. Imagine that a single person, for example in the center of the square, walking randomly among the people, suddenly stops and starts simply to look up the sky in a very curious manner. When someone else notices this, stops and looks up. This induces others. Symmetry-breaking it emerges out of the interactions of the people in the square. It’s easy to detect similar dynamics in social communities, like Facebook for example. A trend-setter – someone who popularizes a new fashion – is creating a symmetry breaking.

In Physics, spontaneous symmetry breaking is an even more general principle at the basis of a vast number of physical phenomena, ranging for example from ferromagnetism to superconductivity. Also, in the context of the physics of elementary particles, spontaneous symmetry-breaking provides a mechanism by which the  masses of particles are generated.

So, it is amazing recognizing these universal characteristics of symmetry breaking: from classical to quantum physics, from biology to social dynamics analysis, to future Internet architectures (e.g. in IoT and IwT): whenever there is a sheers number of interacting  entities, from particles to objects to people. At the end of the day, a new fashion, or a new business could be seen as symmetry breaking events in markets fluctuating around steady states. Importance of understanding these principles is great.

Let’s go further with this analogy. Think about the Goldstone theorem. The theorem is arguing that massless particles (Goldstone bosons), generated at the spontaneous breakdown of symmetry, which are crossing the quantum system: capturing the nature of these emergent bosons, how many of them are generated in the symmetry breaking, and their dynamic propagation would allow us to understand the behavior of the system during the symmetry breakdown. One may ask “is there an equivalent of the Goldstone boson concept in an ecosystem , in a social community, or in a market” ? I suspect so: symmetry breakings are based on universal principles, so the mathematical laws behind their instances are very much the same!

In the last few years we’ve witnessed a progressing migration of “intelligence” towards the edges of the networks, i.e. towards the Users. Advance in processing and communication technologies (e.g.: higher and higher performance, miniaturization and cost reductions) is already bringing a proliferation of devices, embedding communications and computing, which , in the near future, will be more and more deployed in the environment we live.  In less than a decade, the edge of current infrastructures will become a distributed processing, storage and communication environment made of virtual resources (operated by a multitude of Players, not just Network Operators or OTTs). This transformation will create an ICT fabric capable of interconnecting people, machines and things, where services will be created and accessed through “everything”.

We are going beyond the Internet of Things or Machine to Machine as basically we are transforming “everything” in the ambient we live in a network node, by embedding not processing, storage and communications capabilities, but also a nervous behavior. These networks of networks of edge nodes will be connected to the traditional infrastructures so to expand traditional Telco-ICT networks towards the edge. A web of heterogeneous connections will capillary cover the ambient in which we live. 

We know from biology that spatially continuous networks with heterogeneous connections are ubiquitous in biological living systems, which naturally exhibit self-adaptation and self-control features, empowered by a capillary nervous system. Actually, in living organisms, body and nervous system are adapted to natural environments on many time scales, from evolution to development and learning: any individual organism brings is particular history of behavior and stimulation to any situation in which cognition is acted out. Considering the nervous system, each nervous cell is a very simple autonomous entity but, through the interactions of hundreds of billions of them, body is controlled and intelligence emerge.

The same will be for the future ICT networks: in this metaphor the body will be a dynamical set of physical networks and the nervous system will be a distributed overlay of cognitive software; both will adapt to the service environments, from evolution to development and learning: any dynamic network will bring is particular history of behavior and stimulation to any situation in which its artificial cognition is acted out. This network nervous system will be created by an overlay network of “nervous components” embedded into “everything”, from objects, edge nodes up to core nodes: you may see it as a very capillary management system of a highly pervasive network.

Imagine the impact of transforming each object into an entity that can communicate, that can allow you accessing every services and that is aware of the environment: it will create a huge number of new biz opportunities to be exploited. A Manufacturer or a Consumer electronics Provider would have the chance of transforming their products into means to provide services, and even to remain in contact with the User of the product.

If you think that I’m dreaming, have a look at this link, there is a nice example. The idea they have is creating mathematical models of the way the nervous system and the brain work, so to build products that behave more like animals.

 We are not that far from having a technology capable of developing an artificial nervous system for future ICT networks and services.

We too are active in this field, thanks to the EIT Activity “Smart Networks at the Edge”!