The huge number of discarder cell phones is highlighting the even larger number of cell phones we buy every day

Every single day we see the announcement of a new cell phone, tablet, digital camera and … you name it. This trend is just getting stronger as processing and communications are becoming part of any object. In many cases we will be able to use these objects as interface to get information about the object itself as well as information of any other kind. For sure manuals are fading away, substituted by on line help. And we can be sure that in a few years most manuals will also be replaced by user communities always at hand to provide information.

The ambient itself is becoming an interface, through the objects it contains. We will no longer need to look for a PC to connect to the web but we can use the table we happen to be seated at to communicate with the web and get information. This is going to transform many objects into terminals and increase the value of terminals in economic terms and our perceived value of them relegating the network to an always available commodity. A bit like today when we look for a car, a bus or a bike to move around and we are never concerned about the road. We take it for granted that the road is there!

The market of terminals is already bigger, in economic terms than the market of network equipment. Additionally, the life time of terminals is generally shorter than the one of network equipment. The result of these two factors is that in this last decade the evolution of the network has been influenced by the terminals. In this coming decade the evolution of the network will be steered by terminals.

The tipping point was the explosion of the cell phones, it has passed 4 billions at the end of 2010 and it is forecasted to approach 9 billions by 2020. Even more important in 2010 over 1.6 billion cell phones were sold and the forecast is for over 4 billions sold in 2020. The life time of a cell phone varies greatly in different countries from as low as 6 month to 4 years.

In addition to cell phones we are starting to see the uptake of really portable screens, like the tablets. In this decade we are going to see a multiplication of devices that interconnect with the network on one side and to a human on the other.

In total there may be over 20 billion devices that interface human beings with the network in 2020

There will be even more devices connected through the network (IoT, Internet of things) in addition to sensors, already discussed.

The strong innovation cycle of terminals will continue throughout this decade and by 2020 it is expected terminals will:

❏    embed large storage capacity

❏    have a processing capacity in line or exceeding the need

❏    be able to connect to a variety of access points (adapting coding, frequency and signaling as required)

❏    be able to create their own local network and share it with other terminals

❏    be able to single signals out of noise through cooperative processing with other terminals in the area thus overtaking the Shannon thresholds.

Terminals are, therefore, steering the innovation in this decade and by 2020 they will be also an integral part of any communication network.

• Terminals, their evolution and their capability have to be at the core of the COMSOC business and all involved in terminals design are part of the COMSOC audience.
• Today COMSOC is only marginally involved in CE, Consumer Electronics; by 2020 CE has to be an integral part of COMSOC.
• Terminals based networking is a new area that can be specifically addressed by a COMSOC Technical Committee.

Visualization coming up strong as biz advantage

More and more we are seeing interaction based on visual communications. Thanks to ever better (and cheaper) displays we are exploiting what is easiest for us to capture information: our sight. Give me a page of text and it will take me 3 minutes to go through it, give me an image and in under a second I get it. And very likely what I get as an image will have a more profound effect on me.

As data multiply we will need to coalesce them into images, something we can capture at a glance. New ways of converting data into images are under study and new ways to animate information are proving their effectiveness.

Smart and easy visualization is becoming a competitive advantage and we are going to see plenty of innovation in this area. As books move into eBooks and pages become interactive expect a brand new experience.

By 2020 we can expect to:

❏    Have widespread 3D visualization through a variety of technologies

❏    Have large, paper thin displays

❏    Have bendable displays

❏    Have color reflective display supporting video

❏    Have resolution beyond the human eye

❏    Have widespread haptic displays

❏    Have many applications to display complex data set

Display technology keeps delivering better images in a better form factor. Screens by 2020 will come in many forms, and some of them will be dirty cheap so that they will become part of many objects.

3D visualization will be common, although a significant portion of the content will remain 2D, but with a resolution exceeding the one of the human eye. This means that the bandwidth required to feed these screens will be higher than the one we used in 2010. A 10 fold increase in bandwidth demand both in wireline and wireless can be expected.

4k resolution will likely have the same penetration that HD had in 2010. This requires bandwidth over 50 Mbps. Portable screen will be HD or exceed 300 dots per inch.

Progress in reflective technology will make screens usable in full daylight and the availability of bendable screens, at a very low cost, will bring display capability to many common objects, from table tops to walls, to some products’ labels.

Most screens will enable interactions (either directly or by coupling with a gesture or voice recognition device). Additionally, paper thin display will change the feeling of ambient and can provide, along with effective communications, the feel of presence.

Screens are probably the most important factor in driving the expansion of broadband in this decade in the mass market. Along with them, software will provide new ways of displaying complex data set, as an example in visualizing health related issues, safety concerns, the working of enterprises and cities and so on.

Education and training will make substantial use of visualization in all fields.

• The evolution of display technologies will impact the demand of bandwidth and will steer towards new communications architectures. This will have to be considered by COMSOC. Many display producers will embed communications capability in their products hence becoming a potential audience for COMSOC (it has already started).
• Education products produced by COMSOC should ride this wave. Visual and Interactivity are the characteristics of any future education program.

Metadata will be the huge asset for generating value

Data are valuable but their value is depreciated by their abundance (generally speaking). The abundance of data is basically hiding many hues: Google is possibly the best tool to hide data. If your data is listed on the 100th page in a Google search it becomes basically invisible since no-one will ever browse the 100th page.

What is needed is a dramatic syntheses of data, their transformation into a much smaller subset that is reacher in semantics and that can be perceived.

There are many companies (intelligence agencies of many forms) that create metadata as their value proposition. Tools will become available to to extract meaning out of huge data sets, and to create metadata.
Metadata will consists of layers upon layers and will create an increasing value, masking the value of the raw data.

By 2020 we can expect to:

❏    Have creation of meta data at creation, on demand, as a service, as a pre-processing

❏    Have significant data correlation

❏    Have data abstraction

❏    Have semantics derivation

The Data Tsunami makes possible on the one hand to create new information by processing and correlating data and on the other hand it forces to sieve through data, effectively creating new data. These are usually addressed to as Meta Data. Most of future fruition of data by end users is actually fruition of meta data.

The creation of these meta data may be further refined to derive the semantics and by packaging it in ways that are most effective, or meaningful, to the user, in a way hence creating a meta semantics level.

There are many approaches to this data “refinement” and adaptation, including data correlation, abstraction, semantics derivation.

Telecommunications, being aware of certain aspects (like location, usage history, terminal being used, ambient characteristics, social community of the user…) can deliver, or provide the necessary parameters to third parties to enable many kinds of data refinement.

It can also act as an independent and trusted party to provide access to raw or semi processed data for global analyses by third parties.

In doing this a Network Operator enters into the data service domain. This can be seen as a major shift from the present role of Operators and involves a lot of regulatory issues. At the same time it requires a lot of technology, e.g. for data neutralization.

By 2020 it may be expected that a number of “Data Organizations” will have been set up and this will be a most important area both for enabling biz and as a biz in itself.

• The Data Management is an important part of the future business  and  some Telecom Operators will be involved in that. Beyond regulatory aspects there are many technological aspects that COMSOC can support through its technical groups.
• The availability of contextual, emotional, expectational data can drive new ways to control communications flow and the networks may need to negotiate with those capturing the semantics of communication. It is a completely new space that COMSOC needs to consider.

Construction of metadata can provide an intelligent layer to data communication, leading to new “intelligent network architectures” above the signaling layer.

Some days ago I’ve found an interesting website called “M2M APPS” that is a platform for the global machine-to-machine communication value chain.

The goal is to aggregate companies in the M2M market and share info to create new ideas and business opportunities.

I knew other websites where companies inside the same market can register to promote themselves, for example the Green Economy Network website promoted by Assolombarda. In this case every company can declare their activities and business in the value chain but at the end it’s just a repository 1.0.

In the M2MAPPS portal, instead, I’ve found 2.0 concepts because there is also the opportunity to share ideas, to communicate and so to develop the borning business of M2M and related Applications and Services. Using social media channels and functionalities, blogs, and forums everyone can engage in professional discussions, Q&A sessions and polls, get in touch with leading M2M experts and keep ears to the ground of the global M2M industry.

Sharing info and ideas  is the best way to enhance and develop business.

Yesterday I’ve made a talk at ITU Telecom World 2011 on challenges and opportunities of future networks: in particular, I’ve focused the discussion on the “edge”. Technology advances and the related cost reductions are paving the way to a wider and wider embedding of communications, storage and processing powers inside any network nodes, machines, smart things and any Consumers’ electronics devices.

Then we can argue that future networks, more specifically at the edge, will interconnect a huge number of real and virtual entities providing the Users with any services by using local processing and storage resources.

The thesis I’ve put forward during the talk has been that this future growing complexity of edge networks will require looking at them with the mathematical instruments of nonlinear dynamics. Traditional approaches will be no longer applicable.

Nonlinear dynamics is the theory of nonlinear systems and processes, those where the “result” is not proportional to the “cause”. It includes theory of deterministic chaos (which doesn’t mean random disorder). Chaotic systems behave like there were stochastic but in fact they are deterministic: they show predictability in a short-time-scale but non-predictability in a long-time scale due to extremely high sensitivity to initial conditions and to system’s parameters.

So, I’ve argued that the myriad of real-virtual entities interacting with each other at the edge will behave like a chaotic system, a sort of dynamical game where rules will change dynamically. The challenge is modelling the related dynamics (beyond Nash equilibria) and mastering said complexity to extract value.

Overall the vision has been well received and it has created an interesting debate; one of the most interesting questions I’ve got has been: how can we really apply nonlinear dynamics (which has per se a rather complicated mathematics) to such a complex environment as future edge networks, where there will be millions of nodes? I’ve replied that, obviously, I don’t have an answer today, but I a relatively strong feeling, which follows.

In physics state of a system in a given moment of time is characterized by values of state variables (i.e. data). The minimum number of independent state variables that are necessary to characterize the system’s state is called the number of degrees of freedom: if a system has n degrees of freedom then any state of the system may be characterized by a point in an n-dimensional space (with appropriately defined coordinates) called the system’s phase space. Attractor is a subset of the system’s phase space that attracts trajectories (i.e. the system tends towards the states that belong to some attractor).

Why don't model states attractors rather than single neurons ?

The behaviour of a local network (or recursively a node), like a nonlinear systems that change with time, is dominated by a relatively small number of “attractors”, which correspond to activity patterns (i.e. eventually sets of data). So let’s abstract said behaviour of a local network with the attractors. Then, let’s suppose that these local networks communicate with each others by means of multiple connections, that is, by activity patterns (i.e. sets of data). Let’s abstract also this: the degree of influence that the state of one local network would have on the state of other ones would be given by a “multi-dimensional matrix” coupling attractor states.

Turn nonlinear complexity into interactions of attractors and you’ll get a simpler picture. What if we push this approach even up to getting User’s behaviours in terms of attractors ? I think that for a Telecom Operator it’s better knowing the anonymous Users’ attractors rather than the termination attached to them.

We are staying in contact with the guys that I met at the conference to make a toy model and some simulations.

I’ll keep you posted.

A number of companies are offering personal data store solutions

In my previous posts, I have introduced the concept of “Personal Data Store” where an individual can collect, store and manage all his/her personal data: “Personal Data Store” is intended to be a cornerstone for the evolution of the current enterprise-centric ecosystem on personal data (in which end-users are poorly involved in handling of their personal data) towards a user-centric one (in which end-users can enforce a greater control on their personal data). Tools implementing “Personal Data Store” functions adopted names such as vault or locker (see http://lockerproject.org/, http://themineproject.org/), in order to stress the fact that they enforce individuals’ ownership on their personal data. Such an analogy assumes that “my personal data” are “my data” and that I must keep them “secret”.

But this is not fully true. First of all, it is important to point out that my personal data are not only data that I explicitly generated, but they are data “about me”, e.g., data which are recording observations or analyses about me and my behavior. The discussion about the ownership of these personal data is an hot topic and several blogs (some of them are “Data Ownership in the Cloud”, Dataportability, and Privacy and Public Policy work group at Kantara) are active in debating on it. In some cases the discussion about who owns my personal data and who should own them becomes sterile, with limited impacts on the current approaches to personal data handling. An interesting position was proposed by Joe Andrieu, co-chair of the Kantara’s working group on information sharing (http://blog.joeandrieu.com/2010/01/21/beyond-data-ownership-to-information-sharing/ ): Joe suggests to move the focus of the discussion from the concept of data ownership to that of data sharing.

In fact, one of the first issues to be considered is that some personal data could have “multiple owners”. For instance, both the user and the provider of a service can claim ownership rights on the user’s personal data generated during the delivery of some service (e.g., the payment transaction). Moreover, a service provider can “observe” the actions I performed on its servers and store them on its servers (e.g., the record of my searches, the log of the pages I visited, or the network cells my mobile registered on). Such a situation causes several conflicts among the involved actors. In the current enterprise-centric ecosystems, it is necessary to protect end-users: therefore providers must declare the conditions  (and in some cases they are obliged to put constraints) on the treatment of the data used, generated or observed during service deliveries.  In order to improve/equilibrate the current situation it was suggested to enable individuals to collect and manage all the data about them, including those generated or observed by the providers during the delivery of services requested by the end-users. To enable this scenario, service providers must share with individuals data about them, by providing mechanisms to access/retrieve/synchronize such data (e.g., by extending the APIs already adopted by some of them in order to attract more users, or by adopting XDI-based interfaces). In this way, individuals can collect, manage, and exploit data about them.

Another issue to be considered concerns the “meaning of data ownership” in the digital context where data sharing is, in general, implemented as data copying. The term of ownership is quite clear in the physical world: selling, transferring, or stealing a thing (made of atoms) preclude the original owner from continuing to use it. This does not apply to the cyberspace, where (copies of) some piece of information (made of bits) can be sold, transferred, and stolen without that the original owner “necessarily” loses rights on it.

Sharing through copy allows individuals to disclose pieces of information to multiple service providers in order to request or to get better (e.g., personalized or higher-quality) services. Actually, for an individual, sharing personal data (even if they are classified as “sensible”) is more important that keep them secret: if we do not disclose personal data, we cannot fully exploit them (e.g., if I do not disclose to a doctor my symptoms and habits, she/he can hardly elaborate a right diagnosis).

In order to keep “ownership” on disclosed personal data,  we must be able to control their sharing: we must discriminate which pieces of personal information can be disclosed to a given 3^rd party and to declare under which constraints such a 3^rd party is entitle to use them. In this scenario, a critical point is the trustiness of the 3^rd party (e.g., we have to trust that our doctor keeps professional secrecy, when we disclose to him/her information on my health): in fact, it is almost impossible to guarantee only by means of technical solutions that our disclosed personal data are used under our conditions as soon as they are passed to a 3^rd party (e.g., see the experiences of defining DRM frameworks). In order to reduce these risks we could decide to share information only with trusted parties, or only with parties which operate platforms “certificated” by some external authority.

In summary, we could claim that I enforce the ownership on my persona data (i.e., data about me) not because I can keep them secret, but because I can control their collection, sharing and usage. In order to do that, I should be able to establish relationships with the providers of the services I use (either in the physical world or in the digital one), by defining which are the data I disclose to a given provider (my data in the figure), the data that this provider disclose to me (provider’s data), and the data which are generated in the context of service deliveries (our data). The relationship must also include the policies constraining the usage of the shared piece of information. Such relationships are a possible tool by means of which individuals and providers can solve possible conflicts (or tussles) on the personal data ownership, in particular those concerning “multiple ownership”.

“Personal Data Stores” should provide the features to define and control the personal data sharing. A first set of capabilities is related to the definition and the implementation of relationships between individuals and 3^rd parties: they could be achieved by means of mechanisms to define views on shared data (e.g., OASIS XDI), enforce policies on data accesses (e.g., W3C User-Managed Access), and cope with identity management. The second set concerns on how to control and constraint the parties in the use of the data shared in the context of a relation: this could rely on some of the existing frameworks on usage control policies.

Unfortunately, several issues are not fully covered by the available technologies. A first open point is related to the frameworks for enforcing policies on access to and use of a given data: in general, they assume that a single actor, the “owner”, defines the policies the policies to access a given piece of information. As in the context of personal data multiple actors can claim ownership rights on a single piece of data, extensions should be elaborated in order to enable multiple actors to define policies on a single piece of information.  The second issue concerns the mentioned weakness of the frameworks for enforcing usage control policies.

Therefore, control on sharing personal data should be addressed in a multidisciplinary way, by complementing technical solutions with a legal and regulatory framework for preventing abuses.

Tweeting is creating flash social networks connecting people and ideas

I am at the plenary meeting of the Communications Future Program, listening at the opening speech by Matt Bross, former CTO of BT and nowadays Global CTO of Huawei.

He has just made an interesting comment. Thanks to communications power in these last decade has shifted to the people. Look at what has happened in the Arabic Countries, a small constituency  voice its disagreement and this gets amplified through the network(s) and rebound involving more people and more till the point that established power is turned upside down.

As this happens we see many tools on the network(s) linking people but at the same time Telecom Operators keep connecting termination point. They do not know the people, they know the termination attached to people. This is why Telecom Operators are missing the opportunity created by the shift of power to the people and why the Facebooks out there are taking the upper hand. They connect people, independently of the terminations that may be used.

This ties in with the concept of identity (for us the identity is the telephone number or the SIM), with the way we use (or not use) data.

I feel in synch with his comments.

In a recent post we’ve elaborated about the challenges of understanding analogies between neurons networks and future networks in order to exploit some of the principles of nervous system functioning. An ideal scenario is viral networks at the edge, where, in the future, a huge number of small self-adaptive nodes (enactive nodes) will be able to create dynamic networks (of processing and storage resources) expanding and contracting over time. Each node will be able to perceive its environment and  to self-adapt dynamically cooperating and competing (in sharing functions and resources) with all the neighbor nodes as in “games”.

How can we embed a sort of “nervous system” in such simple nodes?

We wish to go beyond the traditional concept of neural networks to increase the level of flexibility and learning features.

Imagine such node’s nervous system as a dynamical system with a certain number (theoretically the higher the better) of dimensions so that mathematically it can be treated as a field, a continuous space where the nervous activity takes place. This is the basic idea of Dynamic Neural Fields (DNFs). This is an example of equation modeling it.

Example of equation of a DNF

DNFs have amazing applications in the fields of A.I. and Robotics. As an example, have a look at this recent paper:

 http://www.uni-ulm.de/fileadmin/website_uni_ulm/iui.inst.130/Mitarbeiter/oubbati/Publications/OubbatiICANN11.pdf

 Researchers have modeled the flocking behavior of a number of entities (e.g. agents) through DNFs. Simulations have shown the emergence of a synchronized motion of the group even without a leader; entities’ behaviors have been first transformed to separate stimuli entries of DNFs, and then combined in a global stimulus by assigning a situation-based priority to each behavior (remember the loops of F. Varela). In this case, the simulations have demonstrated the feasibility of the DNFs approach to simulate an emergent flocking behavior. Now they are planning a real-world implementation for a swarm of robots.

 In one sentence, DNFs represent an example of a nervous system-like “missing link” between sensors and actuators. I see potential applications of this avenue of research not only in A.I., robotics but also in future networks at the edge. Certain levels of cognition can be embodied (with DNFs-like approaches) into enactive nodes (as introduced in a former post) to create, dynamically, viral self-adapting networks of networks.

A world of data being crunched in many ways and many places

As data are becoming the real raw material of the Information Society they will be used by a variety of players and there will be forces to share them and other to restrict access. Data will be everywhere and aggregated in different forms. Most of the time processing will happen at each aggregation point. Other times, processing will require usage of data contained in different aggregation and this requires management of ownership boundaries along with privacy, authentication and much more.

The sheer number of data demands, in many cases, huge processing capacity, in other cases it will be a matter of coordinating and aggregating several local processing. We are heading to a very complex framework in terms of processing and processing will be more and more intertwined with networking.

By 2020 we can expect to:

❏    Have increased 100 fold the performance, using multi-core, multi parallel systems.

❏    Be based on new widespread distributed, clustered processing architecture (processing cloud).

❏    Be performed in a variety of objects, as tiny as sensors and tags and as big as supercomputers clusters.

❏    Have seen the flanking of alternative processing paradigm, namely molecular and quantum computing. Whilst it can be reasonably predicted that molecular computing will be used in specific niches (like genomics) it is difficult to make any prediction about quantum computing. If it pans out, issues like new cryptographic systems will have to be addressed.

The cost of processing will continue to decrease in this decade, at the same rate it did in the last four decades. The processing capacity for mass market will reach a plateau since it exceeds demand, probably in this first part of the decade. Some mass market processing needs, however, will continue to put pressure on processing performances, such as the chips for the rendering of video signals. As video will move in this decade to the 4k standard higher performances will be required for signal processing in television sets, in video cameras and related devices.

Increasing performance will be seen, coupled with lower energy demand, in handheld equipment and sensors. This latter will change some processing architecture (processing is cheaper than transmission in terms of energy bill). Particularly, sensor networks are likely to exploit local sensors processing capability for decreasing the number of data transmitted.

Also, signal processing in terminals may become much more demanding, particularly towards the end of the decade once the terminal can be asked to employ more sophisticated signal analyses to increase spectrum efficiency. In turns, this will lead to a change in the communications protocols and architectures (see 4.4).

The massive distributed processing where the “cloud” becomes a giant computer brings to the fore issues of latency and this in turn may push towards optical networks architectures not requiring an electronic signal manipulation (passive optical add drop).

• Processing and communications impact architectures and COMSOC should be involved in this.
• The processing at the network edges displaces the intelligence and affects the current network architecture. It can result, as some are claiming, in a transparent network or in a diffused network control. This latter may be the case once we consider the network as spanning beyond the present boundaries to include the networks at the edges. The problem in this expansion, of course, is the ownership domain  that does not span across these networks.
• Sensors networks cannot be considered separating the aspects of communications from the ones of processing. A unified view is required.
• The cloud is going to be distributed over the network, over the edge networks, over the terminals (in many cases indistinguishable from edge networks) and over objects. Its processing is coupled with its inner and external communications capabilities (especially when latency is an issue) and shall be an important area in COMSOC.

The next decade will see, eventually, the failing of the Moore’s law applied to silicon. This will create a major earthquake in many industry sectors. It is likely that the overall processing power will continue to increase but such an increase will be based on carbon rather than silicon.

Body area network will take data harvested from body sensors to network gateways

Data are being generated everywhere, and in part will be processed locally. However, there is a need to transport them for remote monitoring and global analyses.

A (almost) pervasive network will take care of this transport. The “harvesting” may require different approaches, sometimes energy issues will dominate the scenario leading to some local clustering and thereafter the sending of aggregated data over the networks through a gateway. Some other times data will need to be sensed, rather than being autonomously sent.

The good news is that there are, and will be many options for data transport.

By 2020 we can expect to:

❏    Be achieved through fiber at multi Tbps

❏    Be achieved through wireless at Gbps

❏    Be achieved by proximity

❏    Be achieved through state change (updates of Data Clusters)

❏    Be achieved through the cloud

❏    Be achieved through skin

❏    Be achieved through electrical, plumbing, lightning and other means

❏    Be achieved through optical pills (optical storage sent over wheels)

Data transport will increase 100 times over this decade. The international and domestic backbones will see an expansion of the optical fiber and the adoption of 100 – 1000 Gbps links based on DWDM. Optics will continue to be a major area for research and industry.

At the access level two main evolutions will continue: fiber deployment and wireless coverage. Both are expected to see significant efforts in research and in industry. 1 Gbps both on fibre and on cell will be common in 2020.

In public wireless area the main effort will be towards dense coverage to support multi Mbps to many customers. Major changes can be expected in the wireless domain fueled by the multiplication of micro-cells. WiFi areas created by the terminals, sharing of network access bandwidth, vertical roaming, use of unlicensed spectrum, use of dynamically allocated spectrum (SDR).  As incandescent lightning will be replaced by LED lightning these latter will be used s downlink communications in the 100 Mbps range.

A significant part of this evolution is fostered by the terminal evolution (see 4.4). These are also going to play a role in new communications architectures with flow of data from one terminal to another (one vehicle to another, one object to another) with the possible role of bridge to create dynamic local area networks.

Given the massive storage capacity of terminals they will also be able to capture data by proximity and relay them to other terminals, once in range. This paradigm may find application in developing countries, in rural areas, as well as in urban spaces, as an example by capturing bulk data in front of a shop and using them once home, or in vehicular communications where the passing of a vehicle through a gate (a toll station, a recharging station…) can result in an update of its data base.

The global data architecture will overlay the physical communications pipes. Large data centers as well as smaller and focused ones (up to a terminal data base size) may be linked one another and kept in synch. Hence, any new data may result in a global change of state at the Data Layer. This architecture makes data local and steer the network evolution towards a data centric architecture.

A part of it may be taken by the cloud. This goes beyond a layered and diffused data hosting. It can become a data processing and it can also become the bridge between object as atoms and objects as web presence, upon which services may be constructed.

A role in data communications may also be taken by human to object communications through the usage of the skin. A host of medical appliances (and sensors) may use the skin as a conductor. Specific protocols may be required.

Electric metering and sensors of various kinds may take advantage of other physical conductors, like electric wires, plumbing, pavement, textiles, wall paper and paintings. Different protocols may also be required and these special networks will need to be bridged to the main networks.

Finally, data storage support may become so dense, able to store multi TB in tiny optical grain, that a portion of the data communications may take place by physically moving these storage support, as it is the case today with the delivery of music, movies through CDs and DVDs.

This is an area that has not been considered as part of the “telecommunications” domain, although it entails massive communication. It is not likely to be killed by the broadband pipes and it should be considered as a part of the telecommunications because of the interactions with the general data layer.

Future data transport is likely to consist of several different architectures and physical means and there is the challenge to have this operated seamlessly and effectively.

• Data Centric Networks have to become a major area for COMSOC
• Alternative Data communications technologies will become “locally” important and a presence of COMSOC in these areas can greatly benefit an expanded membership
• Data encapsulation in services creates different paradigms for their transport (Web 3.0 and beyond). It is an area overlapping with the Computer Society that can be either seen as transparent to communications “dumb pipes” or central to communications since the communications aspects are an integral part of the data encapsulation characteristics. COMSOC should work for and support this latter.