by Dirk Helbing (ETH Zurich and TU Delft) and collaborators
Motivation: As the development of the Internet of Things (IoT)
is taking up speed, connected devices produce staggering amounts of data.
Estimates by Cisco say that by 2020, there will be 7 times more connected
things than people – devices which will produce hundreds of zettabytes of data
every year. Moreover, according to IBM, data volumes will soon double every 12
hours, not just every 12 months. Then, it is neither possible to store all the
data nor to transmit it all for centralized processing and optimization.
Therefore, we expect a new, complementary big data paradigm to emerge. This
will involve decentralized approaches, data aggregation, and "optimal
forgetting" of data, also for reasons of costs, scalability, and security.
As the connectivity in today's
techno-socio-economic systems increases steeply, systemic complexity does so,
too. Taken together, this will exclude a centralized optimization of
societal-scale systems, particularly due to the NP-hardness of some
optimization problems and the limitations of predictability and calibration
(such as over-fitting problems and parameter sensitivity). However, highly
performing solutions can often be realized in real-time by means of flexible
and adaptive, decentralized heuristics. These specify self-organizing systems.
All that matters to reach a desired systemic outcome efficiently is a suitable
choice of the interactions between the system components. The scientific
disciplines of mechanism design and complexity science serve to identify these
interactions.
Decentralized solutions also leave space for
diversity in the locally applied goal functions. This allows for innovation and
solutions that fit the respective context and local culture best. Diversity is
also often favourable for innovation rates, for societal resilience to
disruptive events, and for collective intelligence. For these and further
reasons, decentralized and pluralistic systems can be superior to systems
lacking diversity. (Note that, in biological systems, the value of diversity is
already recognized since a long time.)
Managing the future data deluge wisely is currently
one of the biggest challenges facing policy, industry and civil society. The
project proposed here will study how big data from connected devices can be
used to build a so-called Planetary Nervous System. We envision a transparent,
open-access information system, which can support crowd-sourced real-time
measurements of the world. A system like this could revolutionise many sectors,
from urban planning and traffic control to the early detection of epidemics.
Compatible with IBM's IoT paradigm of "device democracy", we propose
a citizen-run participatory platform. The platform aims to have extensive
features to protect user privacy and create big data as a public good. The goal
is to build a participatory data ecosystem complementing classical big data approaches
and to offer opportunities for everyone: science, business, politics, and the
citizens.
Historical development and state
of the art: In 1943, IBM chief Watson expected that there is a world market for
maybe 5 computers. However, everything changed when computers were connected
with each other. ARPANET networked a couple of military computers to make the
USA less vulnerable to nuclear attacks, using the principle of
decentralization. Soon after this, the Internet became accessible for everyone,
and with the invention of the http protocol, Tim-Berners Lee made it possible
to easily connect webpages with each other. This created the World Wide Web
(WWW) and made it useful for non-experts, which was the precondition for the
emergence of a multi-billion-dollar digital market. With social media, the
Internet reached out for people, who became nodes of a global information
processing system. Now, things are connected to the Internet too, creating the
Internet of Everything. It is predicted that, by 2025, 150 billion sensors will
be wirelessly connected to the Internet (of Things), which makes it possible to
give objects senses and, together with actuators and machine learning software,
even a certain level of artificial intelligence (AI).
This allows one to create a
digital nervous system on a planetary scale, i.e. a global
Internet-of-Things-based information system with an intelligent, learning
software layer on top. The question is how to design and operate such a system
to support a thriving economy and society. Answering this question requires not
only knowledge from computer science and electrical engineering, but also
knowledge from the social sciences and complexity science, to ensure a systemic
view. Here, we propose the Nervousnet project to develop such an integrated
solution.
Project overview: Unlike most Internet of Things
initiatives spearheaded by big IT companies or public institutions, Nervousnet
shall be run as a 'Citizen Web', built and managed by its users. Inspired by
Wikipedia and OpenStreetMap, people, companies and devices will be able to
interact with Nervousnet in 3 ways: by contributing data; by analysing the
crowd-sourced datasets; and by sharing code and ideas. Anyone should be able to
create data-driven services and products using a generic programming interface.
The aim is to yield societal benefits, business opportunities and jobs.
Today, there are several Internet of Things platforms and
data science projects that share Nervousnet’s vision, but none has its scope.
These projects focus on participatory data collection
(http://funf.org, http://www.kaaproject.org; http://thethingsnetwork.org/, or
http://www.opensense.ethz.ch/trac/);
decentralized communication services (http://maidsafe.net/,
https://www.ethereum.org, http://dcentproject.eu);
or big data analytics:
(www.socialsensor.eu, http://datalook.io,
https://www.amigocloud.com, http://stratosphere.eu, http://gdeltproject.org).
Nervousnet aims to meet all three objectives
Moreover, Nervousnet wants to
enable real-time measurement and feedback to support self-organising systems.
For example, self-controlled traffic lights responding to local pedestrian and
vehicle flows can reduce urban congestion and outperform today's systems based
on centralised control. The ultimate challenge will be to digitally enable
collective intelligence. Digital assistants can help bring knowledge, ideas and
resources together. A pluralistic approach to information processing is
important to view complex problems from varied perspectives and also to
anticipate and assess rare and extreme events that are costly for society –
such as natural disasters, blackouts or financial meltdowns.
Nervousnet uses distributed data
storage and distributed control, so that it is more robust to attacks and
centralized manipulation attempts, easy to scale up, and tolerant to faults.
Nervousnet's approach is compatible with the principles of informational
self-determination and, according to our judgment, also with the new EU Data
Protection Directive.
Attracting users is a further
challenge, which will be addressed by 'gamification' and a micro-payment system
to reward and incentivise digital co-creation. As critics may worry about the
responsible use of bottom-up systems, Nervousnet also aims to integrate
reputation systems, qualification mechanisms, and self-governance by community
moderators ("social technologies").
In the long run, we expect that
measurements tailored to specific purposes, together with crowd-sourced data
generation, curation and analysis may outperform the big data analytics
approach currently in vogue. Just as the open standards of the World Wide Web
created unprecedented opportunities and a multi-billion-dollar economy, the
right framework for the 'Internet of Things' and digital society could foster
an age of prosperity.
Project components: To provide the full perspective
of the project, which may be realized long-term with complementary resources,
we give an overview of the main envisaged platform components and
functionalities in the following.
Figure: Schematic illustration of
some core components of the Nervousnet concept.
In the long run, the Nervousnet
platform (to run on several mobile platforms such as iOS and Android, but
also on embedded devices such as Arduino) will comprise a considerable number
of functional elements:
Real-time measurement: The Nervousnet platform opens up
various sensors for real-time measurement of the world around us (status:
done). This will enable the measurement of "externalities" such as
social or environmental impacts of interactions between systems and system
components (e.g. of noise or other emissions). We plan to make it easy to add
further, external sensors, for example, for smart home applications, using
Arduino and Raspberry Pi platforms. [Has already been internally tested, needs
to become easy to configure and use]
Anonymization: Data streams are not linked to
personal or smartphone identifiers, but to a randomized identifier that can be
specified to change over time. [Done]
Security: Data streams, communication and
data sources (smart devices/their owners) shall be secured by state-of-the art
encryption. Data shall be decentrally stored to reduce possible impacts of
attacks. [Implementation in progress, will be further extended]
Privacy: The GPS sensor is not made
accessible or turned off by default. The smartphone's microphone is not made
accessible as a microphone. Its signal is turned into a virtual noise sensor by
means of a digital filter. A similar strategy is applied to other sensitive
sensors. [Implemented and on-going, see also "Data aggregation"]
Informational self-determination: Users can easily turn data
collection through the Nervousnet platform on or off and regulate the frequency
of data collection. Furthermore, they can determine separately for each virtual
sensor, whether data will be logged only locally for one's own use (e.g. in
smart home applications) or shared with others. [Implemented]
Decentralized data storage and
processing: Nervousnet is based on distributed storage to make the platform
resilient to attacks and centralized manipulation attempts, easy to scale up,
and tolerant to faults. User groups can decide to join the Nervousnet community
and share their data with it or decide to run their own data server.
[Functional, under further development]
Forgetting: Most of the measurement data are
needed for real-time feedback (as they are needed to enable self-organizing
systems). Users will be able to specify a time period after which data
will be non-decryptable or automatically deleted (at latest). On top of this,
Nervousnet aims to determine strategies for optimal forgetting (the usefulness
of most data decays quickly with time). [To be implemented]
Data aggregation ("global
analytics engine"): Before their deletion, data may be aggregated to provide information
such as minimum and maximum values, averages, standard deviations, or other
relevant statistical quantities to provide information at various levels of
granularity without revealing personal data. This is being done by a
"global analytics engine". The particular novelty of our approach is
that it can aggregate data in a fully decentralized fashion, even when input
data changes over time [Feasibility has been successfully tested; first version
available soon]
Local analytics engine: Data logged locally can be
accessed for providing data-driven services to Nervousnet users. The data are
managed and accessed transparently via a high-level application programming
interface that hides low-level technical details. [First version exists, will
be further extended]
Pluralistic data processing: Users can also process a certain
volume of shared Nervousnet data for free for own applications, such as
services or games. They can run their own data aggregation and visualization
algorithms, such that their view of the world is not determined by others, but
self-determined. [Must be made user-friendly.]
Data visualization: The Nervousnet platform will
offer various possibilities to visualize data. For this, we intend to link the
platform with an open source visualization platform that runs on various
devices, including smartphones, such as the D3 JavaScript platform.
[Feasibility has been tested; to be developed further]
Personal data store: Sensitive personal data is only
stored locally on the own smartphone. A personal data store shall make it
simple to administer the data according to various categories and determine
whom to give access to what kind of data for what kind of purpose and period of
time (e.g. for use by a bank or other company). We might integrate OpenPDS
developed at the MIT or other open-source software such as digitalID.
Transactions may be supported by a AI-based digital agent that learns the user
preferences locally on the smartphone. [To be done.]
Personalized services: The personalization of services
will be enabled by means of a matching principle. The offers by a provider of
data, services or products can be customized using data from the personal data
store, without revealing the identity of the customer or user (if they don't
want to). [To be done.]
Social technologies: The Nervousnet platform will be
equipped with social mechanisms (such as communication or social networking) in
order to create social opportunities. This will consider knowledge from game
theory, mechanism design, and complexity science such that the implemented
mechanisms support coordination, cooperation, and responsible use. [To be
done.]
Digital assistants: AI, machine learning and
other approaches (such as recommendations and best practices from experts and
users) will be employed to offer assistance to users. This will be done in a
distributed way (e.g. on the user's smartphone) and only if explicitly agreed
by the respective user. [To be done.]
Creating a Participatory
Information and Innovation Ecosystem Open source and open innovation: Most computer code of the
Nervousnet platform (not necessarily though the code of potentially commercial
apps running on top of it) is made open source to allow experts to check its
security and functionality. This creates trust and also participatory
opportunities to contribute to the further development of the platform and its
functionality. [This is our policy; a dual licensing model is being considered]
Crowd-sourcing and participatory
opportunities: Users can contribute data, analyse the crowd-sourced data, contribute,
use and modify code (e.g. new measurement methods, "virtual
sensors"), or release apps using Nervousnet data. Therefore, anyone can
create data-driven services and products using a generic programming interface.
[Must be made user-friendly]
Reputation and incentive systems: In order to reward users for
contributions (for data, code, and apps, or also for social, healthy or
environmentally friendly behaviour...) and to assess the quality of data and
services, multi-dimensional reputation and incentive systems will be designed
and developed for the Nervousnet platform to provide a differentiated, context-
and community-specific feedback. The incentive system will also be used to
promote responsible use of the Nervousnet platform. [To be done.]
Collective intelligence and
digital democracy: It is also planned to develop an online deliberation platform, which
allows one to collect and integrate the knowledge and ideas of many people in
order to support better solutions to problems that need to be solved. [To be
done.]
Citizen and Business Engagement: One goal of the project is to
reach citizen engagement with digital technologies, as this is important for the
digital transformation of our society to succeed. For this, participatory
opportunities and open innovation are as important as incentive systems and gamification
(and a collaboration with companies and public media).
Treasure Hunt: This app allows to localize
"Treasures" equipped with Bluetooth beacons, turning the smartphone
into a kind of radar system measuring proximity. [App exists, must be
integrated into the Nervousnet platform]
Competition Game: This app allows one to perform
group competitions using the output of selected virtual sensors (measurement of
acceleration, noise or distance). For example, one could perform a virtual arm
wrestling or a biking competition. [App exists, must be integrated into the
Nervousnet platform]
Swarmpulse: This app allows one to perform
geo-located measurements of light or noise intensities and create corresponding
maps. It is also possible to leave geo-located text messages, which could be
links to photos or movies, thereby allowing people to map the environment
around them. [To be done.]
Disaster response and societal
resilience: This app should provide functionality in case the mobile phone network
is broken down, offering information exchange via ad hoc network protocols such
as the one used by "firechat". The app should also offer simple local
coordination of supply and demand between peers – something like a sharing
economy functionality for disasters. [To be done.]
Early warning system: Advance detection of impending
risks with predictive analytics and methods from complexity and network
science. [To be done.]
Usefulness for policy
decision-making: Nervousnet and apps running on top of it (particularly Swarmpulse) will
be able to engage with citizens and to collect data on the environment
(allowing one to generate, for example, noise maps, maps of plant and animal
species, etc.).
Invitation: Everyone who is motivated and
qualified to contribute to the Nervousnet platform or Apps is invited to
contribute. We are reaching out to international academic institutions, but
also to coding, gaming and fablab communities to establish international
Nervousnet hubs (see nervousnet.info). You can also apply to join the
PhD class on „Engineering Social Technologies for a Responsible Digital
Future“ at TU Delft with your own funding, e.g. a stipend, see http://www.tbm.tudelft.nl/nl/onderzoek/engineering-social-technologies-for-a-responsible-digital-future/
for details. In case of interest, please send an email to nervousnet@ethz.ch
References
D. Helbing and E. Pournaras, Build Digital
Democracy, Nature 527, 33-34 (2015): http://www.nature.com/news/society-build-digital-democracy-1.18690
E. Pournaras, J. Nikolic, P. Velasquez, M. Trovati,
N. Bessis, and D. Helbing, Self-regulatory information sharing in participatory
social sensing, preprint (2015).
E. Pournaras, M. Warnier, and F.M.T. Brazier, A
generic and adaptive aggregation service for large-scale decentralized
networks, Complex Adaptive Systems Modeling 1: 19, 1-29 (2013).
E. Pournaras, I. Moise and D. Helbing,
Privacy-preserving ubiquitous social mining via modular and compositional
virtual sensors, in: Proceedings of the 29th IEEE International Conference on
Advanced Information Networking and Applications-AINA-2015 (Gwangju, South
Korea, March 2015), pp. 332-338.
D. Helbing, Interaction Support Processor (2015) https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2015118455
