Case Study Team
|Id||Name||Institution (type*)||Contact e-mail||Related WG||Role in the team|
|1||Valentina Nejkovic||University of Nis, RS (A)||Valentina.Nejkovic@elfak.ni.ac.rs||WG1||Coordinator|
|2||Milorad Tosic||University of Nis, RS (A)|| milorad.tosic
|Id||Name||Institution (type*)||Contact e-mail||Related WG|
|1||Marco Aldinucci,||University of Torino, IT (A)|| aldinuc
|2||Pierre Kuonen||University of Applied Sciences of Western CH (A)|| pierre.kuonen
|3||Ari Visa||Tampere University of Technology, FI (A)|| ari.visa
The future 5G network represents highly complex and heterogeneous network that integrates
massive amount of sensor nodes and diversity of devices such as macro and small cells with
different radio access technologies such as GSM, WCDMA, LTE, and Wi-Fi that coexist with one
another. Such network vision is expected to lead to traffic volume of tens of Exabytes per month
that further demands networks capacity 1000 times higher than now. Such traffic volume is not
supported with nowadays cellular networks. Thus, practical deployment of 5G networking
systems, in addition to traditional technology drivers, needs some new critical issues to be
resolved on different areas such as: 1) coordination mechanism, 2) power consumption, 3)
networking behavior prediction etc. Because of the high scale of 5G systems combined with their
inherent complexity and heterogeneity, Big Data techniques and analysis will be the main enabler
of the new 5G critical issues.
In this work we recognize and identify 5G use cases, list basic requirements for their application
Existing Solution(-s) (Models, Tools)
Proposed Solution(-s) (Models, Tools)
1) Semantic Driven Big Data in 5G Networking
Practical Scenarios (-s)
5G network indicate the need for coexistence of multiple wireless technologies in the same
environment. The problem that raises in such environments is mutual interference among multiple
wireless networks, which is consequence of an overlapping in usage of the same set of resources.
Typically, such case happens when same radio frequencies are used for multiple communication
channels that are based on different radio technologies. Coordination protocols defined by the
technology standards traditionally address the problem when networks use same technology.
New coordination concepts are needed in the case of co-existing networks based on
We identify the following possible scenario. In a Home Network setting, a typical home can have
several rooms each equipped with WiFi enabled HDTV set and a number of streaming audio
appliances. At the same time and in the same building, a sensor network is used for home
automation including presence detection, temperature and lighting regulation, doorbell indication
and security and safety monitoring. Most homes also have at least one microwave oven and a
number of Bluetooth Low Energy gadgets. During the typical evening, all of these devices are
active and have to be actively coordinated in order to provide satisfactory level of service.
2) Scenario of power consumption.
A number of recently finished as well as currently on-going 5G related EU projects confirm a
diversity of usage and applications of power consumption, efficiency and reliability in WSNs.
These projects delivered a number of algorithms and protocols for reducing energy consumption
that show the importance of the power consumption. Further, the design of the 5G wireless
networks have to consider energy efficiency as very important pillar in order to optimize
economic, operational, and environmental concerns. In presence of enormous high traffic
volume, data-driven techniques such as intelligent distribution of frequently accessed content
over the network nodes and content caching can result in relevant energy consumption
reductions and prolong the lifetime of nodes that are low on battery energy.
3) Scenario of networking behavior prediction.
Big Data Analytics solutions can predict how the needs in resources use change among places
and throughout the time within a complex large-scale system. A 5G network that adopt such
solution would have ability to learn from the previous situations and states and intelligently
adopt to new demands. Particularly, using appropriate learning techniques the system will
enable devices to learn from past observations in their surroundings.
For example, we identify the following use case scenario: a) In a Smart City, traffic lights and
pedestrian crossings (i.e. various presence detectors) are IEEE 802.15.4 technology equipped
while community WiFi network is mounted on a number of light posts lining the same street.
During rush hours, there is a high demand for WiFi traffic due to a large number of people using
personal devices potentially impacting traffic management system; b) Mobile users consume
images, videos and music, which increase thought time. In such a case, network congestion is a
consequence of the high dynamics in demands that exceeds the system potential for
4) Positioning and location-awareness in future 5G networks
In many places positioning needs help from mobile communication networks. Cities with
skyscrapers are one example of the problematic regions. Autonomous vehicles, transportation,
traffic control need this kind of service. If we consider the problem from the smart city point of
view we notice that there are many new user groups as pedestrians, cleaning and maintenance
services, management and administration. The world is rapidly changing.
5) Trusted Friend Computing
TFC is a new concept that allows IT resources to be shared with other users. The main
objective is to enable a community of users (called “friends”) to securely share their IT
resources without the need for a central organization that collects and stores all the information.
In this concept, IT resource owners define who they trust to access their IT resources (data or
computing power). The community is built around the use of a specific professional software
application. To build such a community, the TFC model uses the notion of “confidence link”. A
confidence link is a two-way channel that allows two friends to communicate securely at all
times. Alll of all friends as well as all trusted links form a connected graph whose nodes are the
friends and arcs are the trusted links. We call such a graphl a “trusted community of friends” or
more simply a “community”. None of the friends in the community has an overall view of the
infrastructure. Each friend knows only his or her direct friends, i. e. the users with whom he or
she has established a relationship of trust. Thanks to this “trusted community of friends”, friends
can securely share their IT resources for specific purposes related to the software application
around which the community was built.