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This is a chart from a 1995 National Academies study showing some of the billion dollar industries spawned by computing research. Of course, “billion dollar industry” is not the only test of impact.  Still, this is tremendously impressive:  the Internet, the graphical user interface, workstation-based computing, modern integrated circuit design …
Self-explanatory.
(The blob on the right is meant to connote the transformation of science.)
This is a chart from a 1995 National Academies study showing some of the billion dollar industries spawned by computing research. Of course, “billion dollar industry” is not the only test of impact.  Still, this is tremendously impressive:  the Internet, the graphical user interface, workstation-based computing, modern integrated circuit design …
Of course, “What have you done for me lately?” is what everyone wants to know.
In 2003, the National Academies revisited the impact of computing research.
Many of the innovations that were obvious in 2003 had been invisible to us in 1995.
Self-explanatory.
These are just a few examples.  (The pictures are in the order of the bullets, top to bottom and left to right.)
 
 
 
 
The goal of these presentations is to expose the computing research community to the sort of long-range thinking that is already underway in our community.  The intent is to be inspirational and empowering.
 
 
 
 
 
 
 
 
The networks that we have created and that have evolved over the decades are complex.
For example, the Internet is complex. Here is a picture of the ARPANET in 1970.   Here it is in 1980.  Twenty years later, it is too complex to draw, let alone understand, model, or predict its behavior.  The Internet is computer science’s gift to society, but ironically we cannot even describe it.
We interpret networks at multiple layers of abstraction.  Below, we are concerned with new technologies: in the beginning we communicated via phone lines, modems, and cables underground and now we have a proliferation of communication media and a proliferation of devices, sensors, and actuators.
Above, we are concerned with new kinds of social uses of our computers and networks: from the days when people shared a terminal, to today where we have a multitude of applications, from the good to the bad. Examples of the good are on-line banking, social networks, and open courseware that already enables tens of millions of people, including tens of thousands of high school students, to learn from famous professors.  Examples of the bad are spam, worms and viruses, and distributed denial of service.
I’ve shown pictures of the past and the present.  What about the future?   I believe that if we understand the complexity of our networks better then we can evolve them in ways that can unleash unimaginable creativity and innovation—from new technologies, to new applications, to new users—and hopefully at the same time improve the overall security of our networked systems.
77
The networks that we have created and that have evolved over the decades are complex.
For example, the Internet is complex. Here is a picture of the ARPANET in 1970.   Here it is in 1980.  Twenty years later, it is too complex to draw, let alone understand, model, or predict its behavior.  The Internet is computer science’s gift to society, but ironically we cannot even describe it.
We interpret networks at multiple layers of abstraction.  Below, we are concerned with new technologies: in the beginning we communicated via phone lines, modems, and cables underground and now we have a proliferation of communication media and a proliferation of devices, sensors, and actuators.
Above, we are concerned with new kinds of social uses of our computers and networks: from the days when people shared a terminal, to today where we have a multitude of applications, from the good to the bad. Examples of the good are on-line banking, social networks, and open courseware that already enables tens of millions of people, including tens of thousands of high school students, to learn from famous professors.  Examples of the bad are spam, worms and viruses, and distributed denial of service.
I’ve shown pictures of the past and the present.  What about the future?   I believe that if we understand the complexity of our networks better then we can evolve them in ways that can unleash unimaginable creativity and innovation—from new technologies, to new applications, to new users—and hopefully at the same time improve the overall security of our networked systems.
78
The networks that we have created and that have evolved over the decades are complex.
For example, the Internet is complex. Here is a picture of the ARPANET in 1970.   Here it is in 1980.  Twenty years later, it is too complex to draw, let alone understand, model, or predict its behavior.  The Internet is computer science’s gift to society, but ironically we cannot even describe it.
We interpret networks at multiple layers of abstraction.  Below, we are concerned with new technologies: in the beginning we communicated via phone lines, modems, and cables underground and now we have a proliferation of communication media and a proliferation of devices, sensors, and actuators.
Above, we are concerned with new kinds of social uses of our computers and networks: from the days when people shared a terminal, to today where we have a multitude of applications, from the good to the bad. Examples of the good are on-line banking, social networks, and open courseware that already enables tens of millions of people, including tens of thousands of high school students, to learn from famous professors.  Examples of the bad are spam, worms and viruses, and distributed denial of service.
I’ve shown pictures of the past and the present.  What about the future?   I believe that if we understand the complexity of our networks better then we can evolve them in ways that can unleash unimaginable creativity and innovation—from new technologies, to new applications, to new users—and hopefully at the same time improve the overall security of our networked systems.
79
Motivated by these observations, herein lies a fundamental question:
Is there a science for understanding the complexity of our networks such that we can engineer them to have predictable behavior?
I interpret the term “networks” broadly.  We need to understand networks at multiple layers of abstraction, as illustrated by the picture: from the physical layer on the bottom through multiple architectural and protocol layers in the middle all the way up to the top layer of networks of people, organizations, and societies.
The field of computing has science drivers, technology drivers, and societal drivers.  Network science and engineering is no different.  For all three drivers, there are fundamental challenges that face us if we are to make progress in answering the question stated earlier.  Let’s use these three interacting sets of drivers to frame a research agenda for network science and engineering.   I’ll start with the science drivers.
The scientific challenge is to understand the complexity of networked systems.  We do not have theories of our networks such that we understand their emergent properties.  We do not have formal models of our networks such that we can assert any guarantees of reliability or survivability, especially in the presence of disruptive events or malicious attacks.  We do not have models of our networks such that we can accurately predict their performance; for example Poisson models and heavy-tail distribution models are unrealistic or overly simplistic.
The technology drivers come from new communication substrates such as wireless and optical.  These new substrates change the physical characteristics of the network, suggesting new network architectures, where the goal is not just simply optimizing the transmission of a packet from one host to another.  For example, because of increased bandwidth and large distances between nodes we already view the network not just as a communication medium but as a storage medium.  Technology drivers also come from new devices. The ubiquity of mobile computing and communication devices, for example, is forcing us to rethink our networks in truly fundamental ways, to include at the very least the human dimension of what networks are.
Moreover, new theories and new architectures enable new societal uses of the network—creating new opportunities such as virtual worlds and new challenges such as ensuring the security and privacy of users and their data.
For your interest, I’ve included in small font, a sampling of fundamental challenges for each of these three drivers.
The research communities active in addressing these intellectual challenges are correspondingly listed on the right.  Indeed one of the opportunities in developing a research agenda for network science and engineering, is ambitiously to ignite a diverse range of communities to work together and look at networked systems from a holistic viewpoint—it’s not enough to invent a new theory, a new architecture, or a new application without understanding the implications it has for all aspects of the networked system.
I would like to emphasize the expanse of these intellectual challenges.  I am talking not just about networks as the invisible infrastructure that we all take for granted today, but about networks of people and organizations that creatively use the invisible infrastructure in unforeseen ways.
To calibrate the rapid growth in societal impact of the Internet, let me give you some concrete examples: Mosaic, the first browser, was created in 1993 and Google was founded in 1998, less than ten years ago.  Google is known for search but it makes its money on ads;  who would have guessed?  (As as aside: remember that Mosaic and Google, like other browsers and search engines, were born out of NSF/CISE-funded research projects in universities.) Within just the past four years, we have seen the creation and influence of social networks embraced by the younger generation: SecondLife, FaceBook, and YouTube.  We are also seeing old institutions remake themselves, including the print media like newspapers and the music recording industry.  More sobering, in April of this year we witnessed the first cyber attack on a nation—Estonia—by a well-organized group in Russia.  And just three weeks ago, the US company Seagate reported that hard drives sold in Taiwan contained Trojan Horses planted by subcontractors in China so that any information stored on those tainted hard drives was automatically uploaded to websites in Beijing.  We did not anticipate any of these phenomena—good and bad—nor did we anticipate how they have completely changed the way in which people and organizations interact with each other.
It is the expanse of all these intellectual challenges that necessitates including not just researchers in computer and information science and engineering, but researchers in economics and the social sciences.
Using IT for delivering various service to the rural developing world
Rural people have many important information needs that are not being served
Important contextual limitations...  which often means first-world technology solutions wont work
Field staff visit farms, and collect pictures, video, audio and data;
Updated on a blog;
This data can be used by: agri specialist, certification, marketing
 
ACI (Amer Compertiveness Initiative) double the Fed Budget for basic res in physical sci over 10 yrs  9.75B -> 19.5B