Lähdekoodi sivulle H2020 V1

[[op_en:Horizon 2020]]
{{arviointi |moderator=smxb}}

== Johdanto ==
Horizon2020-puiteohjelma käynnistyi vuoden 2014 alussa. Ohjelmakomiteoiden työohjelmat vuosille 2014-2015 on julkaistu, ja ensimmäiset H2020-haut ovat jo sulkeutuneet. Vuosien 2014-2015 tulevat haut ovat jo selvillä, ja niiden sisältöön ei enää pysty vaikuttamaan. '''EU-komissiossa valmistellaan kuitenkin jo vuosien 2016-2017 työohjelmia. Niiden sisältöön vaikuttamisen aika on nyt!''' Tulevien työohjelmien hakuihin pitää saada aiheita, jotka kiinnostavat suomalaisia hakijoita ja joiden hauissa suomalaisilla hakijoilla on mahdollisuus menestyä. Tällä sivustolla kerätään suomalaisten osallistujien aiheidoita Horizon2020-ohjelman ICT-työohjelmaan 2016-2017. 

Tämä sivusto on julkinen eli sitä pystyy kuka tahansa lukemaan. Kommentointia varten pitää rekisteröityä.


'''Yhteyshenkilöt:'''
Katja Ahola (Tekes), Sami Majaniemi (LVM), Marko Heikkinen (Tekes), Juha Latikka (Suomen Akatemia), Elina Holmberg (Tekes), Hannu Hämäläinen (STM) ja Jouko Hautamäki (Tekes)


'''Virallinen kommentointiaika:''' 6.5.2014 - 28.5.2014.

== Kontaktoitavat tahot ==

# Yritykset
# Tutkimuslaitokset
# Yliopistot
# Muut tahot

== Tutkimusaiheet / Research topics ==

'''Tähän kohtaan toivomme erityisesti aihe-ehdotuksia ja kommentteja. Kirjoitathan tekstin mielellään englanniksi.'''

Mitä aihepiirejä halutaan saada aukaistua tuleviin H2020 ICT-hakuihin? Voidaanko/kannattaako samalla tehdä avointa ennakointityötä sen suhteen mitä Suomessa pitää lähitulevaisuudessa tutkia/opettaa?
 

=== NOKIA’s (Networks) contribution to H2020 Work Programme 2016-2017 ===

From Nokia’s (Networks) perspectives, new research is needed on the following Mobile Broadband key development areas (Note: the summary or the 5G requirements have been listed after these development areas):


==== Enable 1000 times more capacity ====
{{piilotettu | More spectrum, higher spectral efficiency and small cells shall provide up to 1,000 times more
capacity in wireless access. Although the industry today has not defined what 5G will look like
and the discussions about this are in the earliest stages, flexible spectrum use, more base
stations and high spectral efficiency will be key cornerstones, and research on it needs to be continued 
in Work Programme 2016-2017.

Some details:
The increased capacity needs shall be supported by the new spectrum sharing methods and intelligent antennas. The new methods shall support administration and optimization of the shared spectrum across different access technologies and across different administrative domains.
For instance, several operators may share the same frequency band to their overlapping networks and the interferences/disturbances from one network to another have to be taken into account for the optimal allocation of the frequencies. This may have implications also on the traffic management of the shared networks.

New methods and tools are needed for testing the new network functions (development phase) and for verifying the performance of the deployed networks. All functions and parameters are interdependent, and new methods are needed to analyze and find out the overall dependencies and impacts between functions.}}

==== Reduce latency to milliseconds ====
{{piilotettu| In addition to pure network capacity, the user experience of many data applications depends
on the end-to-end network latency. Advanced audio-visual real-time applications such as
cloud gaming, tactile touch/response applications and machine-to-machine interactions (M2M, IoT, Industry 4.0) will push latency requirements down to single digit milliseconds in the future. 

Also, the network and application level protocols have to be faster and more efficient for enabling higher network performance and better energy efficiency. }}

==== Teach networks to be self-aware ====
{{piilotettu| Today, network operators spend about 15-20% of their total OPEX on operating, managing and optimizing their networks. The introduction of additional radio access technologies, multiple cell layers and diverse backhaul options will increase complexity and risks driving up network OPEX substantially. The application of big data analytics and Artificial Intelligence technologies are needed to create the Cognitive Network that can autonomously handle complex end-to-end network and service management.

The heterogeneity of the network accesses increases the need for the more intelligent traffic management and off-loading functions. New, efficient and self-learning traffic management methods have to be researched and understand more.

The intelligent traffic management is very important when a general communication network is used to connect different kind of industrial internet, machine-to-machine and health-care systems together with heavily varying traffic demands.}}

==== Personalize the network experience ====
{{piilotettu| Customer experience management (CEM) has become an industry priority over the last few years. In the future, the capabilities of CEM shall be enhanced substantially when combined with the Cognitive Network approach outlined above. In short, cognitive networks shall dynamically optimize the experience of selected users in response to a changing environment.}}

==== Telco Clouds ====
{{piilotettu| Cloud technologies being able to provide computing and storage resource on-demand have brought substantial gains in efficiency and flexibility to the IT industry. Similar gains could be achieved when applying cloud principles to telco networks with virtualization decoupling traditional, vertically-integrated network elements into hardware and software.
The migration of network elements in combination with software defined networking (SDN) will transform today’s networks into a fully software defined infrastructure that is both highly efficient and flexible.
A key research area in the telco clouds is also Security & Privacy. It is not sufficient if the network itself is safe, but at the same time it is used for cheating. The methods have to be developed for the cloud environment to prevent any kind of fraud by the users of networks.}}

==== Flattening total energy consumption ====
{{piilotettu| In mature markets, energy consumption already accounts for 10-15% of the total network operational costs and may hit 50% in developing markets. The focal point for improving energy efficiency is the radio access, which accounts for around 80% of all mobile network energy consumption. Advanced power amplifier technologies, baseband efficiency and heterogeneous network architecture evolution are the key ingredients for the efficient radio access network of the future.}}

==== Requirements for 5G ====

In the following, the requirements for 5G have been summarized:
{{piilotettu| The use cases, key design principles and vision of the 5G system lead to requirements that the future mobile broadband system will need to meet:
*	10,000 times more traffic will need to be carried through all mobile broadband technologies at some point between 2020 and 2030.
*	The need for more capacity goes hand-in-hand with access to more spectrum on higher carrier frequencies. The new 5G system needs to be designed in a way that enables deployment in new frequency bands.
*	We will see growth between ten and a hundred devices for each mobile communications user. This trend will continue and 5G needs to be designed to accommodate such growth in device numbers.
*	Radio latency lower than one millisecond, which is important for achieving high data rates while keeping equipment cost low.
*	The requirement for low radio latency is followed by other requirements - jitter (latency deviation) of 20μs or end-to-end latency targets that vary between different service types.
*	A battery life of 10 years needs to be achievable. Part of this lifetime extension will come from the evolution of battery technology but part will come from efficient handling of machine type traffic in the 5G system. Reduced power consumption and increased battery life will also be very important for more complex devices, 
*	Energy consumption for the operators needs to decrease.
*	The possibility to handle devices with very low cost has to be ensured. This means for example, that using very high frequency bands may only be an option for simple devices, as transceiver capabilities don’t need to be the same for all devices. 
*	Peak data rates of a 5G system will be higher than 10 Gbit/s but more importantly the cell-edge data rate (for 95% of users) should be 100 Mbit/s. This will allow the use of the mobile Internet as a reliable replacement for cable wherever needed.
*	We will still see improvements and demanding requirements for spectral efficiency in terms of average bit/s/Hz/cell for ultra-dense deployments. 
*	Mobility should be ensured for velocities as of today or higher – of course, small cells will be optimized for nomadic mobility but higher speeds will also be supported. 
*	Accurate positioning of the device shall also be possible with 5G, indoors as well as outdoors. Location-based services are becoming more important and will be followed by location-based reality augmentations. 
*	Security will be a very important requirement for 5G and the trend is already visible and addressed now. 
*	smooth mobility between cells, layers and radio access technologies needs to be assured
*	support for any-to-any communication (so not only uplink or downlink but also device to device and node to node self backhauling) needs to be assured.
*	5G networks need high capacity and low latency backhaul without a significant increase in cost compared to today’s backhaul.
*	5G networks will need to be programmable, software driven and managed in an integrated way.}}


===Demos Helsinki suggestions to H2020 WP 2016-2017===

The core contribution of technology to society is usually the behavior change that new technologies enable. Demos Helsinki proposes

==== Retrofitting ICT in cities and buildings for smart living ====
Retrofitting ICT in cities and buildings to make us behave smarter in smarter environments

==== Using ICT to enable preventive and inclusive healthcare ====
Using ICT to enable preventive and inclusive healthcare and allow autonomy in healthy behavior for everyone

==== Tools and interfaces for socially responsible and participatory behavior ====
Building tools and interfaces to enable socially responsible and participatory behavior

==== Using ICT to support sustainable lifestyles ====
Using ICT to support sustainable lifestyles

==== ICT intensive futures forecasting ====
ICT intensive futures forecasting to improve resilience in investments, education goals and science projects


=== Qlu Oy: Proposal for a co-operation program to the Horizon 2020 ===

==== Teaching environments optimized for HOH students====

{{piilotettu|
''Contact'': Juha Nikula, Managing director, Qlu Oy, +358 40 5881138, juha.nikula(at)qlu.fi

'''Program Description'''

The goal for this program is to cost-efficient methods for building teaching environments optimized for the needs of hard-of-hearing (HOH) students, but also serving efficiently the needs set by the new network based teaching methods.  
The main goal is to make it possible for everybody, also the HOH students, to participate efficiently in the bi-directional discussions in the teaching environment.  This is especially important in learning foreign languages  and also in the discussion based teamwork.
These environments also have value in business and social life, which also are more and more operating in the network environment.

'''Working group'''

We propose that this program will be executed as a co-operation between our company, Qlu Oy and one or several Finnish communal operators. If seen feasible, the community could be expanded to include one or several communication technology companies and/or academic research groups. }}


=== Aalto yliopisto suggestions to H2020 WP 2016-2017===

==== Linked Data ====

{{piilotettu| 

''Contact'': Eero Hyvönen, Aalto yliopisto

* Linked Data
* Linked Data quality and re-use
* Knowledge Discovery in Linked Data
* Visualization and exploration of Linked  Data
* Linked Big Data }}

==== Semantic knowledge extraction from unstructured data ====

{{piilotettu| 

''Contact'': Eero Hyvönen, Aalto yliopisto }}


=== Safety related topics ===

==== ICT based risk assessment and identification ====

{{piilotettu|
There is a lot of SME industry where occupational and industrial safety and safety culture is at lower level than in large enterprises that can invest more to safety related things. One solution could be ICT based systems for risk assessment and risk identification. It is important to bring the safety improving solutions to the practical level in SMEs. }}

==== RAMS product and production design ====

{{piilotettu|
Reliability, availability, maintainability and safety (RAMS) related issues shall be considered as an essential part of system engineering and as a whole from the beginning of the product design. RAMS related issues are very important in all machines and production systems but especially in paper industry and large manufacturing and production lines. Already now the production systems and machines include distributed control systems and a lot of diagnostics. Such ICT solutions are necessary in the product design that enable an effective RAMS design for products and production systems. }}

==== IoT, safety and risk management in industrial systems ====

{{piilotettu|
Furthermore, internet of things (IoT) is strongly coming  to industrial systems and machines, and this is a feature to be included to the product characteristics. IoT brings a lot of possibilities, but also risks and threats (information security, personal safety, etc.). These threats and measures to tackle these threats should be studied so that severe accidents, relating to both safety and security, can be prevented. }}


=== Future network and device evolution ===

==== Improved access networks and enabling technologies====

{{piilotettu|
''Contact'': Markku Juntti, University of Oulu

Improved access networks and enabling technologies for better energy and spectral efficiency as well as design and operation flexibility. Including software defined radios and networks.
* Ultradense networks
* Distributed antenna systems
* Cloud processing }}

==== End-to-end optimization of wireless networks ====

{{piilotettu|
''Contact'': Markku Juntti, University of Oulu

End-to-end optimization of wireless networks and connections for internet of connected objects and industrial internet to enable efficient use and support for big data applications over wireless connections.
* Big data over wireless
* Application driven connection optimization }}

==== Device and antenna technologies based on new materials ====

{{piilotettu|
''Contact'': Markku Juntti, University of Oulu

Device and antenna technologies based on new materials: multimode and reconfigurable antenna technologies.
* New materials
* New antenna solutions }}


=== Ministry of Transport and Communications suggestions to H2020 work programs ===

==== Digitalization, socio-economic and evidence based decision-making ====

{{piilotettu|
Teema liittyy laaja-alaisesti koko yhteiskunnan rakenteeseen, toimintaan ja kehitykseen. Teemassa ei rajoituta pelkkään digitaaliseen tekniikkaan ja raaka-dataan liittyvään problematiikkaan, vaan huomiota tulee kiinnittää myös sosiaalisten vaikutusten ja tiedonjalostusketjun toimintamalleihin liittyviin aiheisiin. Digitaalisen yhteiskunnan kehittäminen vaatii tiivistä julkisen ja yksityisen sektorin välistä yhteistyötä. Tältä osin haasteena on määrittää julkisen ja yksityisen sektorin roolit, tehtävät ja vastuut kehittämisen eri vaiheissa. Tarkoituksena on luoda pohja digitaalisuuden edellyttämälle paradigman muutokselle. Digitalisoituneessa yhteiskunnassa data sekä siitä analytiikan avulla luotu tieto ja siihen perustuva päätöksenteko ovat keskeisiä lisäarvoa luovia tekijöitä. Tieto luo perustan innovaatioille, uudelle liiketoiminnalle sekä hallinnon rakenteiden uudistamiselle, joilla vastataan murroksessa olevan toimintaympäristön haasteisiin ja otetaan haltuun sen tarjoamat mahdollisuudet. Digitaalisen talouden kasvu edellyttää, että digitaalisten palveluiden turvallisuus kyetään takaamaan. Digitalisaation kehittämiseen liittyy automaation vaikutusten arviointi, sosioekonomiset vaikutukset, ennakointi ja järjestelmien testaus.

”Kaiken internet, internet of everything” ja Internet of Things ja sen hyödyntäminen liikenteessä on vahvasti mukana tulevaisuudessa. }}

==== Maritime transport and industries ====

{{piilotettu|
'''Meriliikenne ja –teollisuus:'''

* Teollisen internetin soveltamisesta meriteollisuuteen
* big datan hyödyntäminen 
* Builging Information Modelling (BIM) ja elinkaaripalvelut
* miehittämättömättömät ratkaisut
* yritykset haluaisivat kehittää EU –projekteissa erityisesti tuotantoon (esim. robotiikka) ja tuotannonohjaukseen liittyviä innovaatioita
* Tiedon laajempaa hyödyntämistä ja älyliikenteen edistämistä mm. meriliikenteen turvallisuuden parantamiseksi 


'''Maritime, Waterborne:'''
* automated time based emission measuring and reporting from process industry manu-facturing processes and impacts to greenhouse gas emissions to help to direct the pro-duction to be more eco-efficient  
* 4D/5D real time video virtualisation in maritime spatial planning and to prevent disasters in sensitive areas and also to help estimate and forecast how the spills would behave in case of different climate conditions, connections to other databases
* 4D/5D video virtualisation in developing the logistics corridors in container and other freight transportation cases, how the heavy trucks impact on the roads and how the sea-port-inland port transport corridors should be developed and organised to lower the CO2 and other emissions.
* Cost management reporting systems utilising big data and cloud databases
* Fill rate optimization in global tarnsportation of goods
* Packing and fillrate optimization in every step of the goods transportation from factory to end customer
* Harbour time optimization at ship loading and unloading
* Robot utilization at warehouse order picking
* Energy saving robotics
* Independently moving and working robots at ware houses 
* New pay per use business model for robot systems
* Big data analyzis of robot systems
* Robotics in house building }}

==== Aviation ====

{{piilotettu|

'''Ilmailu'''
* miehittämätön ilmailu Unmanned Aerial Vehicles
* digitalisaatio on keskeinen osa miehittämättömän ilmailun kehitystä
* Laukkujen kuljetus lentokoneeseen voitaisiin automatisoida.
* Sähköinen automaattitraktori voisi vetää koneen portilta kiitoradalle
* SESAR –ohjelma sisältää paljon digitalisaatiota, tiedon hyödyntämistä ja automatisaatiota. Esim lentokentän liikennevirtojen ohjailu, häiriötilanteet (tuhkapilvet, lumisateet ym).
* Vaisala on kehittänyt lentokoneiden jäänestoon säästä riippuvan suoja-ajan laskentajär-jestelmän (Hold Over Time) }}

=== Miniature smart devices for detection of Atrial Fibrillation ===

{{piilotettu |
''Contact'': Tuomas Valtonen, Tero Koivisto,  Technology Research Center, Brahea Center, University of Turku

Atrial fibrillation (AF) is a very common cardiac anomaly, present in approximately 2% of all people, i.e. in approximately 140 million people globally. The condition becomes even more commonplace from the age of 65 – approximately five percent of all 70 year-old persons and more than 10% of all persons 85 years or older suffer from AF.

Approx. 15 million people suffer stroke worldwide each year; of these, 5 million die and another 5 million are permanently disabled. In the EU, more than 463,000 people annually die from a stroke. As the lifetime cost of a stroke is estimated at approximately 65,500 euros, strokes burden the EU economy by over 3,600 disability-adjusted life years and more than 38 billion euros each year.

Approx. 15–45% of all strokes are caused by AF. Hence, in Europe alone AF accounts for up to 208,000 deaths per year and costs up to 17.1 billion euros per year. In addition, strokes due to AF are more severe than when due to other causes, due to which the actual cost of AF may be even greater.

By means of new anticoagulant medication products approx. 70% of strokes could be prevented. AF typically begins as asymptomatic, in which case the patient remains unaware of the condition ("silent AF"). If we could detect AF at an early stage before it causes blood clots and strokes, the EU alone could avoid up to 145,000 deaths every year and costs up to 12 billion euros per year.

Detection of silent AF is a major challenge, as its symptoms may be sporadic and thus absent during medical check-ups: for example, in one study the median time for detection of AF was 84 days. Via long-term monitoring, e.g. with a duration of several weeks, it would also be possible to detect silent AF. By means of wide-scale screening of risk groups, e.g. persons older than 65 years, we would not only spare lives, but also enhance quality of life and achieve significant economical savings.

In order to detect AF, there is a growing need for a miniaturised smart devices which can be conveniently worn during long time periods, possibly lasting up to a year. The development of novel detection techniques will serve as an important building block in a smart system for tracking the progression from silent AF to permanent AF. Today’s knowledge of this type of progression is scarce at best, the main reason being the lack of suitable recording technology. A solution to this problem could have major impact on future healthcare as AF is the most common sustained arrhythmia in clinical practice, all too often leading to a stroke. }}

=== Aalto ARTS suggenstions to H2020 work programs ===

==== Strong Alternative Scenarios for Research Funding ====

{{piilotettu |
''Contact:'' Kari-Hans Kommonen, Media Lab, Arki

In the EU, research funding is typically based on strongly programmed research calls, which are based on commonly accepted doctrines and assumptions concerning the development of society, technology and economy. This leads to systemic rejection of visions that diverge from these fundamental doctrines as a viable basis for research, and leads to a lack of diversity that seriously hampers the possibilities of Europe to generate viable alternatives to prevailing understandings and to develop truly innovative initiatives.

In future EU research programmes there would be thus a great need to strengthen radically the opportunities for new openings that diverge from the preprogrammed visions, and also make sure that the demands for consortia and project forms do not discriminate against insightful seeds of change. }}

==== Open Intellectual Property ====

{{piilotettu |
''Contact:'' Kari-Hans Kommonen, Media Lab, Arki

In order for the R&D in Europe to benefit all citizens, communities and enterprises as opposed to be buried in the vaults of R&D labs and proprietary monopoly products, EU R&D funding should be only directed to support work that produces openly published and freely available and modifiable (open source) results. }}

=== Aalto University, suggested topics ===

Contact: Vuokko Lepistö-Kirsilä, tutkimusasiamies, Aalto-yliopisto Tutkimuksen tukipalvelut p. 050 381 6396.	


==== Web of Building Data - dynamics, quality and security ====

{{piilotettu|
Contact: Heikki Saikkonen, Seppo Törmä
Organisation:  Aalto University
Email:  seppo.torma(at)aalto.fi

Buildings are becoming increasingly data intensive. The amount, variety, and complexity of data related to buildings is growing, ranging from regulations, urban plan, geographic information, component and material catalogs to different design models (architectural, structural, mechanical, ...) of a building and the data created in the operational stage of the building lifecycle (maintenance and usage models, as well as sensor data gathered from a building). Unfortunately, in the current practice these datasets remain difficult to access and disconnected from each other. Even for intended users, too much tedious and error-prone human effort is needed to integrate data from different sources, causing serious problems in the cost and quality of buildings. For other stakeholders and wider audience the possibilities to access building data are almost non-existent, which seriously limits the value of the data and the ways it could be utilized in innovative applications in the Smart City area.

Recent advances in the Web of Data technologies – developed for truly decentralized representation of structural data on the Web – have enabled the interlinking of diverse building data with connections to activities, social networks, and business operations in buildings. It enables distributed publishing, accessing, querying, and linking of building data. This decentralized approach conforms to existing organizations and practices in construction industry, and can be adopted without changes in existing processes in construction projects. Cross-model linking can support inter-enterprise workflows, information aggregation for analyzes and summaries, and advanced change management protocols. It enables the linking of building information models to and from external data sources, and open access to relevant parts of building data over the lifecycle of a building. It has a great potential to foster the evolution of a building-related data and applications within the Smart City ecosystems. 

Although the basic tools to convert building information models into Web of Data representations are already in place, the characteristics of construction projects and building-related datasets create a number of research topics not faced in the previous Linked Data applications. Firstly, the dynamic nature of the data creates needs to manage changes and version histories of the dataset and linksets. Secondly, there are higher coverage and quality requirements for linking: since links are used in real construction workflows or Smart City applications, it is essential that all links have been identified and that there are no incorrect links to confuse the activities. Thirdly, the need to control the access to published datasets creates security-related research topics.}}


==== Personalized diagnostics and care though big data analytics ====

{{piilotettu|
Contact: Samuel Kaski
Organisation:  Aalto University
Email: samuel.kaski(at)aalto.fi

Personal health records, patient records, clinical measurements and genomic information are severely underutilized due to lack of common databases, unsolved privacy issues, and lack of suitable analytics tools. The analysis methods need to be scalable to the massive sizes, accurate, and capable of estimating and expressing uncertainty of the results, properties necessary for health-related decision support systems. Promising elements of the solutions exist in current massive recommendation engines, big data analytics methods and advanced interfaces. They need to be brought together, developed further, and tailored to the task of personalized diagnostics, monitoring, and treatment and care recommendation. The systems will be applicable as professional decision support systems and information seeking methods, to personalized health, exercise and diet monitoring and recommendation services. }}


==== Computational Synthetic Biology for Sustainable Bioeconomy ====

{{piilotettu|
Contact: Juho Rousu
Organisation:  Aalto University
Email: juho.rousu(at)aalto.fi

For Europe, dependency on fossil fuels  in industrial production of fuels, chemicals and materials, is both an ecological risk and economical handicap. Synthetic biology is a new field which combines biology and information technology to enable man-made design of biological systems and make their functions predictable. To realize the full potential of Synthetic biology, advanced computational modeling and algorithms are required, in order to support biological circuit design, experiment design optimization and data analysis. Methodologies originally developed for digital computers will provide a useful template, however, the  uncertainlties of biological parts and systems call for extension of the methods. Industries that replace fossil fuel sources for biological feeds such as waste and industrial side-streams present a growth opportunity for Europe and help to alleviate the dependency on fossil fuels for better climate and more self-sustainable Europe. }}

 
==== Future cognitive transport protocols ====

{{piilotettu|
Contact: Jukka Manner
Organisation:  Aalto University
Email: jukka.manner(at)aalto.fi

As the world is moving towards wireless communication, we are still using transport protocols and algotithms designed for static fixed networks. With mobile communication, the network characteristics and features can change frequently and without prior warning. We have to start designing cognitive transport protocols that can adapt to dynamic changes in network performance and availability; today algorithms are too static to make full use of the available resources. An important issue is also network neutrality and sharing of limited resources between users and between applications. }}


==== Proactive security ====

{{piilotettu|
Contact: Jukka Manner, Mikko Särelä
Organisation:  Aalto University
Email: jukka.manner(at)aalto.fi, mikko.sarela(at)aalto.fi

Network and ICT system security is typically based on reacting to malicious use. We build various nested defence mechanisms and walls for protecting our resources from an attacker, waiting for the attacker to come. The opponent typically scans targets, looks for vulnerabilities and then hits when a weakness has been found.

This model is a classi cat-and-mouse game, and the defending party is always at risk. We need to switch the model to a proactive operation, where we actively use the attackers tools to find our own weaknesses, and fix them before the opponent is able to make use of them. In general, this resembles active scanning of potential targets but can be much more sophisticated. }}


==== Security of software defined networking ====

{{piilotettu|
Contact: Jukka Manner, Mikko Särelä
Organisation:  Aalto University
Email: jukka.manner(at)aalto.fi, mikko.sarela(at)aalto.fi

The way networks are built and managed is rapidly changing. Whereas in the past, networks were distributed systems built on protocols. If the network needed new features, one had to design, implement, and deploy a new protocol in the network. Sometimes this meant changing all the existing hardware and sometimes it was enough to bring in a new box designed to carry the new feature. This way for managing and developing networks has lead to long lead times for new products and complexity that leads to high operating expenses, configuration errors, and costly security problems.

Software defined networking (SDN) is a new centralized paradigm for deploying and designing networks. All decisions about packet forwarding are made by a network controller that has full knowledge of the network topology. It provides the network an operating system that makes the network programmable. With this, the network can be centrally managed, and new features can be deployed by just writing new network application on top of the controller.

The three main drivers for the adoption of software defined networking are cloud computing, big data, and mobile computing. According to SDN central report, Software defined networking is expected to grow ten fold in the next few years to approximately $35 billion by 2018. The transition to Software Defined Networking brings with it two major research questions: First, how does the change from distributed networking protocols to centralized network operating system change the security of the system? Does the change bring new vulnerabilities that need to be understood? Second, is it possible to create new security features at the network level that would have been impossible (or next to impossible) with the traditional distributed approach. }}
 

==== Securing software-defined networks ====

{{piilotettu|
Contact: Tuomas Aura
Organisation:  Aalto University
Email: tuomas.aura(at)aalto.fi

Computer network architectures are undergoing a major change: new network are built using software-defined networking (SDN) technologies and patterns, such as OpenFlow and network virtualization. The new networks differ from previous ones in that the routing and network topology are defined at a software-based controller rather than at the routers and physical links. This architecture enables fast deployment and experimentation with new, application-specific routing and security policies. The change in network technology is already taking place in data centers and intra-domain networks, and it could later revolutionize also inter-domain networking.

It is still not well understood what kind of security threats and vulnerabilities the new networking technologies will bring, or what new opportunities are created for improving network and communication security. When the SDN technologies are used for data centers, 4G networks, and future network environments, these become potential targets for large-scale cyber attacks. Moreover, the combination of novel technology and business incentives may result in entirely new tradeoffs that are currently unexpected and lead to changes in the traditional threat landscape. The increased levels of virtualization and location independence of services also create challenges for identity and access management (IAM). Research in these areas is urgently needed because major industrial SDN deployments are expected to go ahead in the next few years. }}


==== Security for billions of ubiquitous and embedded devices ====
 
{{piilotettu|
Contact: Tuomas Aura
Organisation:  Aalto University
Email: tuomas.aura(at)aalto.fi

Great security challenges arise when billions of new embedded and ubiquitous computing devices are connected to the Internet and cloud-based services. The software, hardware and communication on these devices needs to be protected against hacking and other malicious attacks. Scalable security architectures and protocols are needed for secure device discovery, for associating the devices securely with online servies, for communicating data and instructions securely, and for updating software and managing the device configuration and ownership over their lifecycle. Trusted computing technologies and security testing techniques are needed to increase the robustness of the software and hardware. The potential applications range from consumer appliances and mobile devices to ubiquitous sensors and digital signage, and to plant and farming machinery. Security will be a critical requirement in the growing competitive market for network and cloud-connected ubiquitous devices. }}


==== Personalized learning environments for learning computational thinking online ====

{{piilotettu|
Contact: Lauri Malmi
Organisation:  Aalto University
Email: Lauri.Malmi(at)aalto.fi

Computational thinking and basic programming are everyday skills of the 21st century, and are increasingly being included in K-12 curricula. There is a rapidly growing number of children, schoolteachers, and other adult learners both within and outside of formal education who need effective ICT education. Given the wide variety of backgrounds, proposed solutions must be scalable, accessible, and customizable.

Massive open online courses (MOOCs) may provide a partial solution, but at present, they are largely based on a one-size-fits-all model of lecture-driven content delivery rather than effective, research-based, discipline-specific pedagogies that can be tailored to each individual learner or group. These useful pedagogies include: programming assignments with automatic feedback, serious games, visualization and simulation activities particular to ICT, etc. These activities require support in the form of educational technology; various separate software tools exist but are presently difficult to offer as parts of an online course package.

The situation calls for the integration of ICT-specific pedagogies and supporting tools into MOOC platforms and personalizing the use of online learning materials and tools for computational thinking and basic programming such that are directed to different age groups from primary education to adult education.

Personalization involves the automatic adaptation of the learning environment to suit a learner's personal learning path. An adaptive learning environment suggests and selects activities for the learner on the basis of their growing competence; feedback may also be adapted accordingly. This can be accomplished by modeling each learner's conceptual knowledge of computational concepts in combination with large-scale analytics of learners’ behavior and progress.

Research on this topic addresses the needs of national teacher education organizations as well as individual EU citizens. The research contributes to the growth of the intellectual capital of the EU and consequent economic growth. }}


==== Beyond search - new intelligent interfaces to information ====

{{piilotettu|
Contact: Samuel Kaski
Organisation:  Aalto University
Email: samuel.kaski(at)aalto.fi

The dominant paradigm of accessing information is very inefficient for complex and uncertain information needs, and alternatives do not keep pace with the big data. Advanced interfaces are needed which combine intelligence in user modeling, personalization and adaptation to contexts, with new human-computer interaction paradigms and advanced visualizations. Furthermore, the interfaces need to operate seamlessly with advanced information retrieval solutions and big data capable services. A dominant proportion of current work is knowledge work which can be made significantly more efficient with the new tools. Also end-user interfaces to services needs new interfaces as the number and variety of services keeps increasing. Immediate examples are customer-relationships management interfaces, general-purpose interfaces to company databases, as well as personal and public databases such as emails, and interfaces to recommendation engines spreading to most on-line retail and services. }}


==== Smart cities: analysis of hetereogeneous and continuous streams of data ====

{{piilotettu|
Contact: Aristides Gionis
Organisation:  Aalto University
Email: aristides.gionis(at)aalto.fi

Smart cities: analysis of hetereogeneous and continuous streams of data (traffic, trajectories or people, different types of sensors, social media streams, etc.). Develop methods that extract patterns from the data, summarize the data, identify events and outliers. Use the discovered knowledge to make recommendations to citizens, optimize available resources, minimize energy consumption, improve livability of cities. }}

 
==== Social media: Analysis of social media streams ====

{{piilotettu|
Contact: Aristides Gionis
Organisation:  Aalto University
Email: aristides.gionis(at)aalto.fi

Social media: Analysis of social media streams. Summarize the main events and identify the trends. Take into account the different attributes of the data in order to identify local trends and events in sub-dimensions of the data (geographic, demographic, etc.). Identify experts. Utilize the obtained summaries to recommend interesting content to users and improve information-retrieval capabilities. }}


==== Health and well-being: Develop data-driven approaches to improve health and well-being ====

{{piilotettu|
Contact: Aristides Gionis
Organisation:  Aalto University
Email: aristides.gionis(at)aalto.fi

Health and well-being: Develop data-driven approaches to improve health and well-being. Exploit the large amount of data obtained from social sensing applications, such as friendship networks and social-media content, as well as self-measurements, commonly referred as  “quantified self.” Develop methods that analyze the available data to better understand the factors that affect health and well-being, and exploite the findings in order to build a more health-aware society. }}


==== Algorithmic challenges in big-data analysis ====

{{piilotettu|
Contact: Aristides Gionis
Organisation:  Aalto University
Email: aristides.gionis(at)aalto.fi

Algorithmic challenges in big-data analysis. Develop efficient methods that analyze very large amounts of data while tackling successfully the challenges of modern applications. Methods need to operate in stream fashion over the data, use small amounts of space, have linear or even sublinear time complexity, operate when the data are distributed over many locations, deal with very large dimensionality, adapt to concept drifts, respect privacy requirements, and more. }} 

 
==== Power over Ethernet ====

{{piilotettu|
Contact: Pekka Nikander, Tuomas Aura
  
Power over Ethernet (PoE) is an established standard that is now emerging as a grass-root alternative to mains electricity in building-scale power distribution.  It is suitable for ubiquitous smart devices that consume up to about 60-80 Watts, in homes, offices, and industrial settings.  Early existing applications include, for example, energy-saving lights, retail terminals, wireless access points, and networked video cameras.   
 
The main advantages of PoE are safety and connectivity.  PoE cabling and devices may be safely installed by non-expert users, and the power comes always together with a high-bandwidth network connection.  In order for the European industry to stay competitive in this area of embedded system development, research is needed on open hardware and software platforms, including PoE device development, on software-defined power supplies, enabling flexible conversion between voltages and between AC and DC power, on flexible utilisation of locally generated and mains electricity, and, in general, on new applications based on the so called extra low voltage (ELV) direct current (DC) technology. }}


==== Wireless Systems Big Data ====

{{piilotettu|
Contact: Riku Jäntti
Organisation: Aalto University, Department of Communications and Networking
Email: riku.jantti(at)aalto.fi

Wireless systems, especially mobile systems generate huge amounts of measurement data related to e.g. transceiver location (device and radio node), device and base stations IDs, applied service types and radio measurements. Currently this data can be collected from the network using 3GPP minimization of drive test specification (trace functions), device applications or using propriety systems in RAN or mobile core (e.g. traffic measurements in Gn point in core). Some operators are already releasing data for researchers, see e.g. Orange D4D Challenge http://www.d4d.orange.com/en/home, where data from their Senegal network is released. The big data generated by wireless systems can be utilized not only to optimize the efficiency of the network itself but also to bring benefits to society at large. Applications can rise in many fields including among others health and assisted living, agriculture, transport/urban planning, and energy sector/demand side optimization. The research problems are related to anonymisation of data, processing extreme large amount of data, finding balance between local processing and transport of data etc. The data that ICT systems generate can be utilized for the benefit of the society. There are business opportunities for network equipment vendors, operators, data processing companies and various potential users of the data. }}

 
==== Censorship-resistant communications ====

{{piilotettu|
Contact: Riku Jäntti
Organisation: Aalto University, Department of Communications and Networking
Email: riku.jantti(at)aalto.fi

We have seen in recent past that government agencies are easily tempted to tamper with Internet access, connectivity, and services in many ways: 1) to monitor network users; 2) mine network data on servers to track individuals; 3) disrupt connectivity in case of unrest or uprising; and 4) censor content. Especially Censorship is pervasive in the network, intentional as well as coincidental. In order to maintain freedom in the Internet, numerous approaches were pursued in the past. One example is The Onion Router (TOR) which maintains privacy in web browsing. TOR does not completely prevent traffic analysis and does not help against connection disruptions. Recently bottom-up or do-it-yourself networking has been introduces to tackle this problem using opportunistic networking and disruption tolerant networking technologies that allow floating content. The objective of censorship-resistant communications is to enable communication and content storage/sharing without the reliance on fixed infrastructure. Politically it has two goals: Digital Inclusion, extending the reach of Internet in an affordable way towards human right for everyone and Freedom of Speech, resisting censorship and providing anonymity. }}


==== Internet Trust ====

{{piilotettu|
Contact: Riku Jäntti
Organisation: Aalto University, Department of Communications and Networking
Email: riku.jantti(at)aalto.fi

Current networks such a 3G, Internet etc are pretty secure. However, lots of fraud is conducted over these secure networks. The Internet lacks a trust model and clear identification of entities. Therefore attribution of behavior is cumbersome and expensive. As more things become connected, possibilities of new types of fraud emerge while the old ones will have a wider area of application to more users, more networks and more value in transactions over the network.

Applicable theory: trust modeling and trust management, trust chaining, secure identities.  Technology adoption theory. A suitable privacy solution must be applied.

Technological viewpoint: Components of the solution include: firewalls, intrusion detection, reputation management systems applied into all communications over the Internet. Cloud services make it possible to create a network wide view and under certain conditions achieve affordable attribution of misconduct as well as block the offenders in a fine-grained manner without disturbing legitimate traffic. A solution must also be deployable i.e. have a very good alignment of costs and benefits, the latter being larger than the costs. The proposed solution must also fit into some reasonable business models of the entities. }}


==== Distributed and Mobile Cloud Systems for Service Innovation ====

{{piilotettu|
Contact: Antti Ylä-Jääski

Mobile devices are by their very nature very resource constrained in available battery power, CPU, memory, network, as well as storage capacity compared to the server hardware available in the cloud backend systems. This means that mobile devices need to be tightly integrated to the cloud backend systems in order to do computational tasks that are too heavy for them. However, this basic setup is not yet sufficient for highly interactive applications. The wide area network (WAN) communication latencies between the mobile device and the possibly quite physically remote cloud backend can often be too large for interactive mobile applications, e.g., for interactive augmented reality applications such as Google Glass, as well as computationally intensive mobile intelligent information access applications, or for example smart, real-time vehicular traffic systems.

The focus of the research theme is to bridge the gap between mobile devices and the cloud based server backend systems into a single seamless distributed and mobile computing platform. Open research areas for this distributed and mobile cloud systems platform includes (a) new mobility management solutions, (b) new programming paradigms, (c) new data management solutions, and (d) analysis of the new service opportunities and their respected converging business models.

Such an architectural change will have a significant impact on the mobile service delivery system opening new business opportunities through new computationally intensive mobile services and applications. This can be seen as a competitive opportunity for the European industries leveraging existing competencies for the new developing global markets. The fundamentals call for basic research which should be complemented through applied research with industrial partners. }}

 
==== Quantum nanoelectronics ====

{{piilotettu|
Contact: Jukka Pekola

Quantum Technology is the field of utilizing systems, which are controlled at the level of single quantum states, for practical technological applications. Because quantum mechanics allows for dynamics prohibited in classical physics, quantum technologies may break the classical boundaries and provide not only great quantitative improvements in existing applications but also qualitatively new concepts revolutionizing our society. 
 
As a FET proactive topic, quantum technologies is far from too narrow. 
In fact, focusing it more to the most promising platform, namely, quantum nanoelectronics, would be very important. Why? The answer is simply that without a focus, the funds will be scattered among too many small communities, and hence will not create substantial new research programs heading for real technological applications. Without focus the funds will mostly support  ongoing research in many fields only loosely connected to the core of quantum technologies. 
 
Many breakthroughs in quantum technologies related to quantum nanoelectronics has been already achieved. For example, the present view on the realization of a large-scale quantum computer and simulator is based on quantum nanoelectronics. To date, superconducting quantum computers are the most advanced and they have already reached some theoretical thresholds for fault-tolerant quantum computing. However, a truly large-scale operation with the current state of the art requires a vast amount of quantum bits (qubits) which can become impractical due to the relatively large size of a single superconducting qubit. Thus another very promising pathway is to employ single electron spins in silicon such as those trapped in phosphorus donor atoms or quantum dots. At the moment, the most notable competitors of Europe are the United Sates in superconducting quantum computers and Australia in spins in silicon. However, there are also very strong research groups in Europe and making them to work together in a FET Proactive project can provide us the leverage we need to be the world leader in quantum technologies. }}


==== Internet Trust ====

{{piilotettu|
Contact: Raimo Kantola
 
Current networks such a 3G, Internet etc are pretty secure. However,  
lots of fraud is conducted over these secure networks. The Internet  
lacks a trust model and clear identification of entities. Therefore  
attribution of behavior is cumbersome and expensive. As more things  
become connected, possibilities of new types of fraud emerge while the  
old ones will have a wider area of application to more users, more  
networks and more value in transactions over the network. 
 
Applicable theory: trust modeling and trust management, trust chaining,  
secure identities.  Technology adoption theory. A suitable privacy  
solution must be applied. 
 
Technological viewpoint: Components of the solution include: firewalls,  
intrusion detection, reputation management systems applied into all  
communications over the Internet. Cloud services make it possible to  
create a network wide view and under certain conditions achieve  
affordable attribution of misconduct as well as block the offenders in a  
fine-grained manner without disturbing legitimate traffic. A solution  
must also be deployable i.e. have a very good alignment of costs and  
benefits, the latter being larger than the costs. The proposed solution  
must also fit into some reasonable business models of the entities. }}


==== Motivating physical exercise with digital games and augmentation ====

{{piilotettu|
Contact: Perttu Hämäläinen
Organization: Aalto University
Email: perttu.hamalainen(at)aalto.fi

Problem: Modern lifestyle is increasingly sedentary, leading to obesity and health problems. Digital exergames – video games controlled using body movements - have been proposed as a solution, but recent research shows that only a small fraction of exergames provide exercise intensive enough for inducing health benefits.

Solution: Research is needed to identify which factors motivate and demotivate intensive movement in exergames and digitally augmented exercise environments. Present exergames need to be analyze to form hypotheses, and the hypotheses need to be tested by building and evaluating novel interactive prototypes and technologies. The research will yield tools and technology for promoting a physically active lifestyle, thus saving in health care costs. The research will also enable the creation of new sports and physical activities, which will benefit the fitness and media sectors. A recent example of such sports is augmented climbing, where interactive computer graphics are projected on the climbing wall, which increases the diversity and spectator value of climbing. }}


==== Real-time Biomechanics Simulation ====

{{piilotettu|
Contact: Perttu Hämäläinen
Organization: Aalto University
Email: perttu.hamalainen(at)aalto.fi

Problem: Human biomechanics simulation is already used in movement analysis, rehabilitation, and training, but realistic simulations are considerably slower than real-time, which limits the range of applications and reduces usability. Meanwhile, robotics and computer animation research has recently advanced to the point where physically based humanoid characters can improvise movements in real-time.

Solution: We propose to combine and further develop recent methods from robotics and computer animation in order to enable real-time biomechanics simulation. This enables novel application and research areas, such as interactive virtual sports coaches, benefiting fields such as sport science, digital games, and physical therapy. }}


==== Parallel programming models for ubiquitous services ====

{{piilotettu|
Contact:  Heikki Saikkonen, Vesa Hirvisalo
Organisation:  Aalto University
Email:  versa.hirvisalo(at)aalto.fi

We propose research on performance portable programming models that are able to support heterogeneous computing platforms with manycore accelerators. We promote especially models supporting media processing for sensor-rich environments such as smart houses, public spaces, and cars. Our goal is to do research that finds a balance between ease of programming and portability of performance.

A vast number of current and future services are thrusting on the availability of ever-increasing computing power. Many of them base the their operation and scalability on performance properties of underlying platforms. Further, in the development of the services, increasing productivity of designers and software developers is expected.

However, traditional unicore processors have ceased to scale in performance and also current multicore processors are getting close to their limits. This development will render many current programming models unusable for a wide range of applications. Manycore processors seem to have immense scaling potential left, but their current programming models (exemplified by, e.g., OpenCL and CUDA) are not suitable for wide programmer population and they do not provide sufficient performance portability.

Our research hypothesis is that combining the recent advances in compiler technology (such as thread-based optimizations) and runtime technology (such as dynamisms and virtualization in GPGPU platforms) with the modern web-related programming frameworks (as demonstrated by WebGL and Web sensor technology) will yield programming models and tools suitable for the future needs. }}

=== VTT contribution to H2020 Work Programme 2016-2017 ===



'''A) Business and application driven topics'''



==== Industial internet and productivity ====

{{piilotettu|
Solutions for internationally competitive Finnish core industries like  forest industry, metal industry, building, oil refining, machine industry, business services and electric equipment:
* Industrial measurements
* Global asset management
* Industrial internet }}

==== Technologies and services for hyper connected society ====

{{piilotettu|
Critical infrastructures, including transportation, energy, or buildings will be increasingly connected via information systems.. Hyper connectivity builds from sensoring via service architectures to understanding big data and eventually to utilizing diverse knowledge of human activity in digital society.
* Smart infrastructure (cities, buildings, smart lighting, smart grids, …)
* New digital services for society }}

==== Personalised Health Solutions ====

{{piilotettu|
* Personalised digital health services
* Big (Health) data analytics and decision support
* Wearable sensors and systems for wellness applications
* Technologies for point-of-care diagnostics and self-tests }}



'''B) Enabling technologies '''



==== Micro, nano and quantum technologies ====

{{piilotettu|
Micro, nano, and quantum technologies: Silicon microsystems, novel materials, advanced manufacturing and integration, novel sensors and systems, bio-interfacing, disruptive innovations by utilization of quantum mechanical effects, new era of computation power, data security. }}

==== Functional printing ====

{{piilotettu|
Functional printing (Thin Organic and Large Area Electronics  - TOLAE): Printed and hybrid sensors and systems for healthcare, buildings and environment;  autonomous sensor systems utilizing energy scavenging, energy storage, local signal processing and wireless data connection technologies; printed biosensors; Flexible and wearable solutions. }}

==== High performance sensing ====

{{piilotettu|
High performance sensing for industry, science, and society; Advanced measurement principles, devices and systems based on based on photonics /electromagnetics and biosensing; Development of measurement instruments and sensors; Miniature and mobile/portable solutions. }}

==== Future communications: 5G ====

{{piilotettu|
Future communications: 5G (radio access, network management, multimodal), optical connectivity components, sensor networks, Internet of things connectivity; Cloud technologies. }}

==== Cyber security and privacy ====

{{piilotettu|
Cyber security and privacy: Solutions providing prediction, situation awareness and resiliency against threaths. Solutions for data access and privacy. Security from silicon to cloud. Solutions for industry, business and society. }}

==== Data science and analytics ====

{{piilotettu|
Data science and analytics: Methodologies and applications of data mining, data analysis, and decision making support for services in industries, health, and business. Cloud  technologies and architectures. }}

== Priorisointiperiaatteet ==

Koordinaatioryhmä määrittää yhteistyössä osallistujien kanssa, kuinka aihe-ehdotukset ryhmitellään laajemmiksi kokonaisuuksiksi. Koordinaatioryhmän muodostaa H2020-ohjelman virallisen ICT-komitean asiantuntijaryhmä, jossa on edustajat Tekesistä, Suomen Akatemiasta, LVM:stä ja STM:stä. Joukkoistetut lobbaustavoite-ehdotukset Horizon 2020-ohjelman vuosille 2016-2017 alustavaan apilamalliin jäsenneltynä löytyvät alta listattuina. Huom! Mikäli jaottelu ja ehdotettu ylätason otsikkojaottelu on osallistujien mielestä epätyydyttävä, voi vaihtoehtoisia ehdotuksia kirjata alle.

 '''Huom! Ao. listojen numerointijärjestys ei tarkoita priorisointijärjestystä, vaan tarkoituksena on vain ryhmitellä ehdotukset aihepiireittäin!'''


'''Arctic'''

# Fully automated airports
# Harbour time optimization at ship loading and unloading
# Package handling automatization
# Traficflow calibration and automation
# Unmanned Aerial Vehicle networks


'''Bioeconomy'''

# Computational Synthetic Biology for Sustainable Bioeconomy: Algorithms, modelling and simulations
# Functional printing (Thin Organic and Large Area Electronics - TOLAE) 
# High performance sensing for industry, science, and society 
# Reaction libraries  
# Real-time Biomechanics Simulation  


'''Cleantech'''

# 4D/5D real time video virtualisation in maritime spatial planning and to prevent disasters in sensitive areas and also to help estimate and forecast how the spills would behave in case of different climate conditions, connections to other databases
# 4D/5D video virtualisation in developing the logistics corridors in container and other freight transportation cases, how the heavy trucks impact on the roads and how the sea-port-inland port transport corridors should be developed and organised to lower the CO2 and other emissions
# Applying Big Data an IoT in Maritime
# Automated time based emission measuring and reporting from industry manu-facturing processes and impacts to greenhouse gas emissions 
# Builging Information Modelling (BIM) and life cycle services
# Energy saving robotics
# Independently moving and working robots at warehouses
# Maritime safety via realtime analysis
# Micro-, nano-, and quantum technologies R&D
# Quantum nanoelectronics 
# Robotics
# Unmanned solutions


'''Digital Economy and Services'''



# Anonymisation of Wireless Systems Big Data, processing extreme large amount of  Wireless Systems Big Data  data, finding balance betweenlocal processing of Wireless Systems Big Data and transport of Wireless Systems Big Data  etc. 
# Answering to 5G challenges
# Answering to algorithmic challenges in big-data analysis 
# Augmented reality user interfaces, modelling engines and rendering farms
# Beyond search - new intelligent interfaces to information 
# Big data analyzis of robot systems
# Censorship-resistant communications 
# Cost management reporting systems utilising big data and cloud databases
# Cutting costs and emissions by flattening total energy consumption of networks 
# Cyber security and privacy: Solutions providing prediction, situation awareness and resiliency against threaths. Solutions for data access and privacy. Security from silicon to cloud. Solutions for industry, business and society.
# Data science and analytics: Methodologies and applications of data mining, data analysis, and decision making support for services in industries, health, and business. Cloud technologies and architectures  
# Digital content production network funding models
# Distributed and Mobile Cloud Systems for Service Innovation  
# Fill rate optimization in global transportation of goods
# Fraud and cheat testing in the face of Internet evolution
# Future cognitive transport protocols that can adapt to dynamic changes in network performance and availability
# Future communications: 5G (radio access, network management, multimodal), optical connectivity components, sensor networks, Internet of things connectivity; Cloud technologies   
# ICT intensive futures forecasting to improve resilience in investments, education goals and science projects 
# Industrial Internet and productivity Solutions for internationally competitive Finnish core industries 
# Internet of things (IoT) troubleshooting
# Internent of Trust - components of the solution include: firewalls, intrusion detection, reputation management systems applied into all communications over the Internet 
# Knowledge Discovery in Linked Data
# Linked big and open data
# Linked Data quality and re-use
# More spectrum, higher spectral efficiency and small cells shall provide up to 1,000 times more capacity in wireless access.
# My Data encryption
# New network based teaching methods
# Open Intellectual Property - In order for the R&D in Europe to benefit all citizens, communities and enterprises as opposed to be buried in the vaults of R&D labs and proprietary monopoly products, EU R&D funding should be only directed to support work that produces openly published and freely available and modifiable (open source) results
# Opening data in machine readable form in the Universities and by the public authorities
# Packing and fillrate optimization in every step of the goods from transportation from factory to end customer 
# Pay per use business model for robot systems
# Parallel programming models for ubiquitous services 
# Personalized learning environments for learning computational thinking online 
# Power over Ethernet 
# Proactive IT-security strategies and generic SDNs (Software Designed Networks)
# Production management and procedure innovations 
# Reduce network latency to milliseconds 
# Reliability, availability, maintainability and safety (RAMS) design for products and production systems 
# Retrofitting ICT in cities and buildings to make us behave smarter in smarter environments
# Robotics in house building
# Robot utilization at warehouse order picking
# Safety improving solutions to the practical level in SMEs 
# Security and reliability of software defined networking 
# Security for billions of ubiquitous and embedded devices 
# Semantic knowledge extraction from unstructured data
# Smart cities: analysis of hetereogeneous and continuous streams of data 
# Strong Alternative Scenarios  - In the EU, research funding is typically based on strongly programmed research calls, which are based on commonly accepted doctrines and assumptions concerning the development of society, technology and economy. This leads to systemic rejection of visions that diverge from these fundamental doctrines as a viable basis for research, and leads to a lack of diversity that seriously hampers the possibilities of Europe to generate viable alternatives to prevailing understandings and to develop truly innovative initiatives. In future EU research programmes there would be thus a great need to strengthen radically the opportunities for new openings that diverge from the preprogrammed visions, and also make sure that the demands for consortia and project forms do not discriminate against insightful seeds of change.
# Teach networks self-awareness and optimatization skills with AI and Big Data 
# Technologies and services for hyper connected society Critical infrastructures 
# Ubigue and layered cities rendering farms 
# User and consumer oriented service design processes
# Visualization and exploration of Linked Data
# Web of Building Data - dynamics, quality and security - the dynamic nature of the data creates needs to manage changes and version histories of the dataset and linksets. Secondly, there are higher coverage and quality requirements for linking: since links are used in real construction workflows or Smart City applications, it is essential that all links have been identified and that there are no incorrect links to confuse the activities. Thirdly, the need to control the access to published datasets creates security-related research topics. 


'''eHealth, mHealth and wellbeing'''

# Applications, data mining and miniaturised smart devices for convenient long period health monitoring
# Building tools and interfaces to enable socially responsible and participatory behavior
# Develop data-driven approaches to improve health and well-being 
# Personalized diagnostics and care though big data analytics
# Personalised Health Solutions - Personalised digital health services - Big (Health) data analytics and decision support - Wearable sensors and systems for wellness applications - Technologies for point-of-care diagnostics and self-tests  
# Motivating physical exercise with digital games and augmentation 
# Using ICT to enable preventive and inclusive healthcare and allow autonomy in healthy behavior for everyone 
# Using ICT to support sustainable lifestyles 



=== Collected topics for Digital Economy and Services ===

 '''NB: The numbering of the listed items below does not correspond to the priority ordering!'''


# [[H2020_V1#Enable_1000_times_more_capacity | Enable 1000 times more capacity]]
# [[H2020_V1#Reduce_latency_to_milliseconds | Reduce latency to milliseconds]]
# [[H2020_V1#Teach_networks_to_be_self-aware | Teach networks to be self-aware]]
# [[H2020_V1#Personalize_the_network_experience | Personalize the network experience]]
# [[H2020_V1#Telco_Clouds | Telco Clouds]]
# [[H2020_V1#Flattening_total_energy_consumption | Flattening total energy consumption]]
# [[H2020_V1#Requirements_for_5G | Requirements for 5G]]
# [[H2020_V1#Retrofitting_ICT_in_cities_and_buildings_for_smart_living | Retrofitting ICT in cities and buildings for smart living]]
# [[H2020_V1#ICT_intensive_futures_forecasting | ICT intensive futures forecasting]]
# [[H2020_V1#Linked_Data | Linked Data]]
# [[H2020_V1#Semantic_knowledge_extraction_from_unstructured_data | Semantic knowledge extraction from unstructured data]]
# [[H2020_V1#ICT_based_risk_assessment_and_identification | ICT based risk assessment and identification]]
# [[H2020_V1#RAMS_product_and_production_design | RAMS product and production design]]
# [[H2020_V1#IoT.2C_safety_and_risk_management_in_industrial_systems | IoT, safety and risk management in industrial systems]]
# [[H2020_V1#Improved_access_networks_and_enabling_technologies | Improved access networks and enabling technologies]]
# [[H2020_V1#End-to-end_optimization_of_wireless_networks | End-to-end optimization of wireless networks]]
# [[H2020_V1#Device_and_antenna_technologies_based_on_new_materials | Device and antenna technologies based on new materials]]
# [[H2020_V1#Digitalization.2C_socio-economic_and_evidence_based_decision-making | Digitalization, socio-economic and evidence based decision-making]]
# [[H2020_V1#Maritime_transport_and_industries | Maritime transport and industries]]
# [[H2020_V1#Aviation | Aviation]]
# [[H2020_V1#Web_of_Building_Data_-_dynamics.2C_quality_and_security | Web of Building Data - dynamics, quality and security]]
# [[H2020_V1#Future_cognitive_transport_protocols | Future cognitive transport protocols]]
# [[H2020_V1#Security_of_software_defined_networking | Security of software defined networking]]
# [[H2020_V1#Security_for_billions_of_ubiquitous_and_embedded_devices | Security for billions of ubiquitous and embedded devices]]
# [[H2020_V1#Personalized_learning_environments_for_learning_computational_thinking_online | Personalized learning environments for learning computational thinking online]]
# [[H2020_V1#Beyond_search_-_new_intelligent_interfaces_to_information | Beyond search - new intelligent interfaces to information]]
# [[H2020_V1#Smart_cities:_analysis_of_hetereogeneous_and_continuous_streams_of_data | Smart cities: analysis of hetereogeneous and continuous streams of data]]
# [[H2020_V1#Social_media:_Analysis_of_social_media_streams | Social media: Analysis of social media streams]]
# [[H2020_V1#Algorithmic_challenges_in_big-data_analysis | Algorithmic challenges in big data analysis]]
# [[H2020_V1#Power_over_Ethernet | Power over Ethernet]]
# [[H2020_V1#Wireless_Systems_Big_Data | Wireless Systems Big Data]]
# [[H2020_V1#Censorship-resistant_communications | Censorship-resistant communications]]
# [[H2020_V1#Internet_Trust | Internet Trust]]
# [[H2020_V1#Parallel_programming_models_for_ubiquitous_services | Parallel programming models for ubiquitous services]]
# [[H2020_V1#Industial_internet_and_productivity | Industial internet and productivity]]
# [[H2020_V1#Technologies_and_services_for_hyper_connected_society | Technologies and services for hyper connected society]]
# [[H2020_V1#Micro.2C_nano_and_quantum_technologies | Micro, nano and quantum technologies]]
# [[H2020_V1#Functional_printing | Functional printing]]
# [[H2020_V1#Future_communications:_5G | Future communications: 5G]]
# [[H2020_V1#Cyber_security_and_privacy | Cyber security and privacy]]
# [[H2020_V1#Data_science_and_analytics | Data science and analytics]]


==== Summary of cross-cutting themes ====


Digitaalisuus on läpileikkaava ja nouseva ilmiö niin toimialojen sisällä kuin toimialojen välilläkin. Kilpailukyvyn kannalta on oleellista tunnistaa erityisesti digitaalisaation hyödyntäminen toimialoja yhdistävänä tekijänä. Toimialojen väliseen rajapintaan muodostetut digitalisaatioon perustuvat palvelukonseptit edesauttavat talouskasvun tukemista ja vientitoiminnan globaalia edistämistä. 

Digitaalisten palveluinnovaatioiden (MaaS, SaaS, EaaS jne.) tuottaminen edellyttää panostusta sekä mahdollistavien teknologioiden tutkimukseen, palvelukehityksen itsensä tutkimista että lopuksi palvelun tuottamien vaikutusten analysointia. Taustamateriaalista kerättyjä toistuvia teemoja ensimmäisessä luokassa ovat mm. 5G-tekniikat, heterogeenisten verkkojen hallinta ja erilaiset älykaupunki, älyliikenne- yms. sensoriteknologioiden ja IoT:n sovellukset. Toiseen luokkaan kuuluvat sosio-ekonomiset mallit, henkilö-kone rajapinnat ja vuorovaikutus, personoitu palvelumuotoilu yms. Lopuksi kolmanteen, eli digitalisoitumisen vaikutuksien tutkimukselle avattavat haasteet ovat niin syvästi yhteiskunnan toimintaa muuttavia, että niitä ei pidä jättää edellisten kahden luokan tutkimuksen varjoon. Merkittäviä tutkimuskysymyksiä tässä luokassa ovat mm. luottamus internettiin, yksilönsuoja, työn muuntuminen ja energiatarve. Big Data -analytiikka, joka mahdollistaa älykkäiden ohjausjärjestelmien ja älykkään päätöksenteon ja suunnittelun eri organisaatiotasoilla on tärkeä läpileikkaava teema.


{{piilotettu|

'''Some re-occurring points taken from individual abstracts'''

* More spectrum, higher spectral efficiency and small cells shall provide up to 1000 times more capacity in wireless access. 
* end-to-end network latency. Advanced audio-visual real-time applications such as cloud gaming
*  The application of big data analytics and Artificial Intelligence technologies are needed to create the Cognitive Network that can autonomously handle complex end-to-end network and service management under heterogeenous network access: The intelligent traffic management is very important when a general communication network is used to connect different kind of industrial internet, machine-to-machine and health-care systems together with heavily varying traffic demands.
* optimize the experience of selected users in response to a changing environment.
* The key to improving energy efficiency is the radio access, which accounts for around 80% of all mobile network energy consumption. Advanced power amplifier technologies, baseband efficiency and heterogeneous network architecture evolution are the key ingredients for the efficient radio access network of the future.
* battery life
* Retrofitting ICT for buildings
* Knowledge extraction from Big Data, especially unstructured data
* Reliability, availability, maintainability and safety (RAMS) related issues shall be considered as an essential part of system engineering and as a whole from the beginning of the product design. Product line diagnostics and the challenges brought by the IoT (Internet of Things).
* Improved access networks and enabling technologies for better energy and spectral efficiency 
* End-to-end optimization of wireless networks and connections for internet of connected objects and industrial internet to enable efficient use and support for big data applications over wireless connections
* Evidence based decision making, big data analytics and decision making
* automated time based emission measuring and reporting from process industry manufacturing processes and impacts to greenhouse gas emissions to help to direct the production to be more eco-efficient 4D-5D real time video virtualisation in maritime spatial planning, disaster prevention.
*  (Recent advances in the Web of Data technologies)decentralized approach conforms to existing organizations and practices in construction industry, and can be adopted without changes in existing processes in construction projects. Cross-model linking can support inter-enterprise workflows, information aggregation for analyzes and summaries, and advanced change management protocols. It enables the linking of building information models to and from external data sources, and open access to relevant parts of building data over the lifecycle of a building. It has a great potential to foster the evolution of a building-related data and applications within the Smart City ecosystems.
* start designing cognitive transport protocols that can adapt to dynamic changes in network performance and availability; today algorithms are too static to make full use of the available resources. 
* Software defined networking (SDN) is a new centralized paradigm for deploying and designing networks. All decisions about packet forwarding are made by a network controller that has full knowledge of the network topology. It provides the network an operating system that makes the network programmable. With this, the network can be centrally managed, and new features can be deployed by just writing new network application on top of the controller. The three main drivers for the adoption of SDN are cloud computing, big data, and mobile computing. According to SDN central report, Software defined networking is expected to grow ten fold in the next few years to approximately $35 billion by 2018. The transition to Software Defined Networking brings with it two major research questions: First, how does the change from distributed networking protocols to centralized network operating system change the security of the system? Does the change bring new vulnerabilities that need to be understood? Second, is it possible to create new security features at the network level that would have been impossible (or next to impossible) with the traditional distributed approach.
* Scalable security architectures and protocols are needed for secure device discovery, for associating the devices securely with online servies, for communicating data and instructions securely, and for updating software and managing the device configuration and ownership over their lifecycle
* Personalized learning environments, and services in general. Other examples: customer-relationships management interfaces, general-purpose interfaces to company databases, as well as personal and public databases such as emails, and interfaces to recommendation engines spreading to most on-line retail and services
* Smart cities and sensors
*  The objective of censorship-resistant communications is to enable communication and content storage/sharing without the reliance on fixed infrastructure. Politically it has two goals: Digital Inclusion, extending the reach of Internet in an affordable way towards human right for everyone and Freedom of Speech, resisting censorship and providing anonymity.
* Enabling technologies }}

{{piilotettu|
'''Added from email communication '''

* mobile technologies and networks (5G) & applications & open data: Novel mobile technologies, networks and applications need to be further developed into more robust services, also relying on open data, to ensure the functioning and efficiency in the society. Particularly authority and public sector innovations and encouragements to invest and produce such services are foreseen and required. Resilience of these often utmost critical services in case of various crises should be further developed.
* cyber security: Cyber security activities should take into account product development  (e.g. ICT and telecommunication network security solutions), complience between actors,  information management, exercises among actors, legislation changes, education in all levels in society as appropriate, increasing employment, privacy and ethical issues. These activities require a multi-disciplinary approach to address the challenges approrpriately. Societal recilience in case of various cyberthreats and incidents needs to be improved. 
* Transport stress point: MaaS
}}



=== Criteria for selecting priorities for the next work programme exercise ===

Delivering on the Europe 2020 objectives of smart, sustainable and inclusive growth depends on research and innovation as key facilitators of social and economic prosperity and of environmental sustainability. Linking EU research and innovation closer to policy objectives sets the framework and specific objectives to which Horizon 2020 research and innovation funding should contribute, such as the Europe 2020 Strategy, the Innovation Union and other flagship initiatives.

With research and innovation being one of the main sources of future growth, the work programme 2016-2017 should thus build on the emerging improved economic situation which allows the EU to build on its competitive advantages to seize new opportunities and create new jobs, besides underpinning key EU policies and objectives. The work programme 2016-2017 should be developed to help the EU capture these opportunities building on the largest single market in the world and a leading position in many fields of knowledge and key technologies. To help focus resources and effort, the focus area approach will be continued. It is expected that some of the existing focus areas will be retained/re-defined, while others will not be continued and new ones introduced.

Besides focus areas, the work programme will also need to address other measures, equally important for economic prosperity: jobs, competitiveness, productivity gains, and overall development of society.

The overarching strategic programming document will in particular contain proposals for the focus areas for the next work programme, while each scoping paper will contain proposals for the priorities to be covered in 2016-2017 on the basis of the inputs received, including how these could then be translated into calls and focus areas. 

This paper suggests selection criteria to help identify the areas, including the focus areas, and actions to be rolled-out in the next work programme on the basis of the Specific Programme and building on those areas also supported under the 2014-2015 work programme. The following criteria to be used cumulatively for the selection of priorities are proposed:

* Maximising EU added value – focusing on areas which cannot be effectively addressed at national level, mobilising resources to build scale and critical mass, improving leverage and synergies with national programmes, aligning with major EU level political initiatives and objectives, contributing to the implementation of EU wide research and innovation agendas;
* Priority areas addressing and anticipating key trends – like societal change and aging population; ICT and big data; globalisation; productivity developments; resource constraints and environmental concerns; security and sustainability of energy supply; urbanisation, etc. on the basis of available evidence such as foresight and other assessments of research and innovation trends and market opportunities, building on existing research, innovation and business strengths; and identifying areas of high potential for world class scientific, technological and innovative breakthroughs;
* Providing strong potential for impact and uptake as well as leverage industrial participation – addressing the longer-term competitiveness and prosperity of the EU and the well-being of its citizens and enhancing industrial participation, including small and medium-sized enterprises through clearly defined impacts addressing the demand side, tackling the barriers to innovation and market deployment and uptake, and translating scientific leadership into industrial advantage; around which collaborations should be built between industry, businesses, universities and research institutions, public authorities, etc., to the benefit of society at large;
* Addressing key novelties and providing genuinely cross-cutting approaches – ensuring the embedding of key novelties such as covering the full research and innovation cycle, social sciences and humanities, gender aspects, climate and sustainable development, etc., and that challenges and areas cutting across different specific objectives and parts of Horizon 2020 are identified and integrated;
* Improving international cooperation – focusing on key strategic and targeted areas of mutual benefit and providing synergies with international initiatives/projects.



== Draft note from the Finnish Ministry of Employment and the Economy - Latest version ==




<center>Draft note from the Finnish Ministry of Employment and the Economy
'''Digitalization and services – new infrastructure for the economy'''
Some rationale and suggestions for H2020</center>


''Digitalization changes the world empowering the customers and end-users. It underlines the role of services and innovative business models and modifies the value chains and networks in all sectors. Value is not created in a vacuum but by constantly interacting with users that through digitalization are enabled to massively co-create and influence on service quality. Digitalization facilitates cross-fertilization of different fields of research and economy, rapid global scaling-up of the business and creation of hyper-scalable services that use big data and cloud as platforms for growth. Europe has largely missed these opportunities so far due to lack of digital single market, declining skills base, ignorance of the growing user and customer power, too rigid grip on existing markets and underinvestment in projects that create disruptive business opportunities. Digital, data-driven, service-oriented innovation must be boosted across all sectors of the economy. Therefore digitalization and services need to be strong cross-cutting themes in all the areas and work programmes in Horizon 2020.'' 



=== Everything will be digital, networked and global ===

Europe should aim at being the future haven for companies developing digitalized, scalable services. The share of services has grown to 70 to 80 of GDP in advanced economies. Services represent more than two thirds of the FDI projects in Europe, which is not only significant, but almost 50 percent more than a decade ago. Digitalization offers new opportunities for creating innovative services that can be scaled up (or down) according to the needs of the customer at low or no delivery cost. Through the digital representation of the real world created by the internet of things (IoT), this new hyper-scalable business dynamics will become dominant not only in the gaming and internet worlds as in the past, but also in e.g. industrial, automotive, domestic and health businesses. The market fragmentation hampering the innovation take-up and growth should be tackled by completing the digital single market without delay. We have to ensure enough European winners in this game as the winner really takes all the global profits in the business. Intangible assets and capabilities of businesses to efficiently develop and utilize them are opening doors to new capital and business partners. More and more jobs are created in IPR-intensive industries. Europe should aim at being the best home environment for companies, whose revenue models are based on intangible value creation, to orchestrate their global growth and reinvest their profits.
  

=== Digitalization and services will keep manufacturing in Europe ===

Digitalization, new business models, service-oriented thinking and better knowledge management are needed for keeping European manufacturing industry competitive.  To reach its full potential, European industry needs to combine advanced manufacturing with smart services. Service-oriented high-tech companies can add value and open up new opportunities to market growth both for themselves and their customers in complex and dynamic global networks.  European industry now has a golden opportunity to improve its competitiveness by adopting key enabling technologies and using them in creation of hyper-scalable smart services. Digitalization enables value capturing from global value networks to Europe. Developing services that bring together ICT, design, and e.g. cleantech will provide further means for job creation and improving sustainability in practically all industries. 


=== Value adding service companies are ignored in European funding ===

Financing growth of service companies is challenging.  Service companies that can create scalable business and help traditional industries to growth path usually experience more difficulties in attracting financing than technology-oriented companies due to their modest financial status and difficulty of valuing their assets. An economy-scale challenge is the polarization of e.g. the business service sector, consisting of large international consulting firms and micro-sized small runner-ups at the end of the scale with the middle market practically empty. Europe should better enable promising companies to grow their share in the new service-dominated value chains.  The financial “asymmetry” and the consequent higher risk levels of development work in these kind of service companies need to be taken into account when choosing priorities for public innovation investments.


=== How to tackle these challenges in Horizon 2020? ===


==== Financing service innovation and innovation processes ====

Horizon 2020 should include pilot cases to provide base for evidence and learning on the transformational power of service innovation. In addition to having specific calls targeting service innovation, the thematic work programmes including SME programme should take it into account, applied and tailored according to the theme and challenge in question. The innovation process in service companies differs from industrial or product-based processes and the latter ones are also increasingly affected by servitization. This servitization of industry, the shift from a product-centered view of markets to a service-led model and the concept of “service-dominant logic” should be reflected in the H2020 work programme content and calls. Innovation processes can be accelerated and higher value-added created by strengthening the user and customer orientation and understanding and by introducing more design, life-cycle and cost-effectiveness thinking, collaborative practices, societal relevance and non-technology-driven initiatives in the calls. There should be more sensitivity to these issues for example when using and interpreting the “technology readiness level” scale. 

In hyper-scalable services - that not only are capable of serving many people but work better the more they are - technology maturity is not as important as who captures most users earliest in the life time of a product development. More users for your application means more data and often drives a virtuous cycle of self-reinforcement. This cycle can be described as involving an early "deployment" followed by "engagement" of prospective users leading to "test / feedback" of users – which if positive has a multiplying effect. The ability to instigate users becomes a key performance indicator influencing the investment decisions of VC’s and Business Angels looking for a promising opportunity. This is important in order to bridge the “Valley of Death” often so fatal to many technology-driven companies in Europe. The early market interaction with the users should thus be funded in H2020.
Financing new platforms and pilot environments

In order to make competitive digital and service development possible in Europe, we need to see the threats and opportunities that lie in the infrastructure and platforms, whether physical, technological, virtual or social. Existing markets that are based on old technologies and solutions need to be identified. Europe is in many fronts building new services on an established infrastructure which might in some cases slow down the deployment of new technological platforms or service systems, or the development is too incremental. We need to invest in creating new infrastructures and platforms, where we can experiment, pilot and demonstrate new ways of doing things in a systematic manner and reaching a critical mass of users.  Smart cities, smart transport  and smart manufacturing are example areas of public-private partnership activities, which consist not only from RDI funding but also from other innovation policy actions like financial market and internal market development, cutting red tape for innovative SMEs and creating smart demand, regulations and standards. For example large IoT pilots in different societal challenges in H2020 would create understanding of the needed regulation for the digital service economy.  


==== Financing business model innovations in value networks and clouds ====

It is important to acknowledge in the context of Horizon 2020, that the value chains are changing for many reasons: The shifting balance in the globalizing world economy, technologies and solutions that are developing in a disruptive way, value creation that is increasingly based on intangible assets (whether consequence of digitalization, design, IPRs, marketing or other).   Global value chains are becoming value networks, and in many cases even “value clouds” in digital economy.   At the same time, the line between B-to-B and B-to-C is blurring, and consumers’ and citizens’ role in business ecosystems is growing.  Theme areas where this is evident include smart grids and 3D printing, both expected to create space for disruptive services and business models around the new technologies. 

The growth potential of such new areas could be enhanced in funding schemes by bringing all relevant players in the innovation ecosystem together and thus enabling systemic innovation where services play a significant role. Horizon 2020 should encourage European companies to challenge existing value chains, create new business models and be a resilient mover in changing economical and industrial structures. Issues related to how to legally or otherwise protect service innovations are important, difficult and often neglected.  The focus of discussion in protecting intellectual capital has been in patents, but the discussion should be broadened to other types of intangible capital and related strategies. Launching some specific support actions and integrating IP questions and strategies to Horizon 2020 projects from this angle are needed.


==== Financing digitalization of services ====

The digitalization of services concerns both digital services around industrial products and production and digital services developed for upgrading and scaling up traditional services. There are no areas of economy or industry that would not be affected by digitalization and servitization. It offers real win-win solutions: Both the productivity and ecological gains can be huge while the value to customer increases significantly. (According to one study ICT could reduce CO2 emissions by enabling reductions in other sectors up to 15 % of total global emissions by 2020. )

Big data will be the infrastructure for future digitalized services and it should be in focus horizontally in all thematic work programmes. In the industry, the value created in the  “IoT” or “cyber-physical systems” or “industrial internet” is dependent on the ways the data can be analyzed, further programmed and servitized for the benefit of the companies and customers in the value chain. Creating digital service innovations and delivery models such as MaaS (Mobility as a Service), SaaS (Software as a Service) and EaaS (‘Everything as a Service’) requires more focus in enabling technologies, service development and the impact analysis of the servitization. Important research and innovation themes include 5G technologies, controlling heterogeneous networks, smart city applications, smart transport, sensor technologies, robotics and other IoT related technologies.  Another set of priorities is related to understanding the user angle: socio-economic modeling, human-computer interface and interaction, gamification, learning and personalized service design.  

On a third level, the profound impact of digitalization to the society is important to cover: trust, privacy and data protection issues, changing work and energy landscape as well as big data analytics that enables smart governance and decision-making at different levels of organizations and society as well as change in the culture, human behavior and ways of working caused by digitalization of the society. It is important to note that services provided e.g. in social media have changed the financing and business models based on the value of personal data, leading to greater concern for privacy issues, control of data and sharing-economy type of developments. To solve the dilemma of privacy and copyright concerns and huge unlocked business potential of data in the digital era, we need more piloting in data governance mechanisms and structures.


==== Financing better understanding of customer needs ====

In many fields, successful companies are increasingly positioning them as integrators of value chains and networks. The value is created more and more with the customer, whether supplier or end-user.  Competitive new European products and services will require deep insight of consumer’
behaviour, including their purchase and consumption patterns.  In this “Age of Customer”, gathering those insights through projects that focus on digitalization, design and services is essential.

We need to ensure that Horizon2020 offers sufficient opportunities for projects that aim to increase knowledge base on global consumers and customers in different fields of European flagship industries and unlock the innovation opportunities arising from that. Also, in the user-centered service economy the nature of work has changed. That requires new customer-oriented leadership and participation skills. This could be a fruitful theme to look deeper across the value networks both in European and global context.

== Viestinvälitys EU:n organisaatioissa ==

Tähän listaan kerätään vaikuttajaverkostoa, joka pystyy vaikuttamaan ICT-työohjelmien sisältöön.

* H2020 ICT-asiantuntijaryhmä (Tekes, Akatemia, LVM, STM)
* TEM
* CONNECT ADVISORY FORUM FOR ICT RESEARCH AND INNOVATION" (CAF), http://ec.europa.eu/digital-agenda/en/research-advisors



== Kommentoi kirjautumatta ==

{{kommentointityökalu|Op_fi4230}}



==Katso myös==

* [[:op_en:Horizon 2020]]
* Draft EU Programmes for Horizon 2020
** [http://ec.europa.eu/research/horizon2020/pdf/work-programmes/science_with_and_for_society_draft_work_programme.pdf#view=fit&pagemode=none Science with and for society]
** [http://ec.europa.eu/research/horizon2020/pdf/work-programmes/health_draft_work_programme.pdf#view=fit&pagemode=none Health, demographic change and wellbeing]
** [http://ec.europa.eu/research/horizon2020/pdf/proposals/horizon_2020_impact_assessment_report_executive_summary.pdf#view=fit&pagemode=none Executive summary of the impact assessment]
** [http://ec.europa.eu/research/horizon2020/pdf/work-programmes/food_draft_work_programme.pdf#view=fit&pagemode=none Food safety, sustainable agriculture and waters]