Big Data in 5G Distributed Applications

Case Study Team


Id Name Institution (type*) Related WG Role in the team
1  Valentina Nejkovic  University of Nis, RS (A)  WG1 Coordinator
2  Ari Visa Tampere University of Technology, FI (A)  WG2 Vice-Coordinator

Team members

Id Name Institution (type*) Related WG
1 Milorad Tosic University of Nis, Serbia (A) WG1
2 Nenad Petrovic University of Nis, Serbia (A) WG1
3 Mikko Valkama Tampere University of Technology, Finland (A) WG2
4 Mike Koivisto Tampere University of Technology, Finland (A) WG2
5 Jukka Talvitie Tampere University of Technology, Finland (A) WG2
6 Pierre Kuonen University of Applied Sciences of Western Switzerland, Switzerland (A) WG2
7 Daniel Grzonka Cracow University of Technology, Poland (A) WG1
8 Jacek Tchorzewski Cracow University of Technology, Poland (A) WG1
9 Francisco Gortazar Rey Juan Carlos University, Spain (A) WG1

*A-academia, I-industry

Addressed Problem

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 development.

Existing Solution(-s) (Models, Tools)

  • Hadoop,
  • Spark,
  • machine learning,
  • deep learning,
  • blockchain

 Proposed Solution(-s) (Models, Tools)

The introduction of big data techniques in 5G distributed applications poses a challenge, as these techniques usually require huge computational resources. In general there’s a need of high performance computing infrastructure. This infrastructure would typically be available as a private or public cloud or grid, with the latencies usually high this infrastructure provide. Cloud or grid resources will allow for consuming, storing and processing huge amounts of data. This data shall be prepared for consumption from the edge network. Depending on the nature of the data needed by end-users, we can envision two kinds of data processing methods: online and offline. Offline methods are easier to handle as the processing can be performed in the cloud or grid. This are supposed to be processes that are not critical. Online processing is used when a response is needed in a given amount of time, and therefore both the time required to give a response and the latency would have an high impact on the set of use cases that will be available for 5G.
For online processing, in those cases where common off-the-shelf hardware is available at the edge, general big data solutions can be run on top of this commodity hardware, assuming that the constrained resources available are enough.

Practical Scenarios (-s)

We identify the following possible scenarios.

1) Scenario of 5G coordination mechanisms.
The 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 heterogeneous technologies.
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 adaptability.

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.

6) Ultra/high definition live video streaming in wireless networks
In recent years, video streaming, both on-demand and live has become an important part of our everyday lives – from social networks, content delivery platforms to industrial, robotic and experimentation systems. Due to rise of processor power and camera sensor resolution of consumer devices, such as smartphones, the image quality criteria perceived by consumers has dramatically increased. High Definition video is becoming a must for all the use cases where video streaming is involved. Not only that, but also new video formats are emerging, such as stereoscopic 3D, 360-degree video and Ultra High Definition Video which contain even more data that has to be transmitted. Therefore, Internet service providers, mobile carriers and content providers are encountering many issues, as transmission of such content requires significantly larger bandwidth. Additionally, the issues become even more challenging due to device mobility, which can affect the Quality of Service and Quality of Experience, especially when it comes to live video broadcast in varying network conditions. Here, we identify a potential use case of novel networking paradigms – SDN and VNF, in combination with Big Data technologies. Large amount of network equipment and status data is analyzed. The results of data analysis are semantically annotated and stored into RDF triple store, so semantic reasoning can be performed in order to draw new conclusions which could lead to re-deployment of virtual networking assets (illustrated in Fig.1), generation of SDN rules, parameter tuning or other optimizations with objective to satisfy user-defined QoS parameters and maintain the quality of high definition live video streaming in varying network conditions, where devices are moving intensively (such as mobile robotic and experimentation systems).
We can conclude that there are still many open questions in case of ultra/high definition live video streaming using wireless networks, which makes it suitable for future research and application of next generation networking in synergy with Big Data and semantic technologies.

7) Multi-party trust based on blockchain for process monitoring
IoT and smart objects are key-enabler technologies for monitoring of complex business processes, especially in logistics domain and industrial production systems. However, most of these processes involve multiple parties. In absence of central authority, the trust between these parties becomes an important issue. Despite the fact that artifact-driven monitoring enables to effectively keep track of the execution of processes where multiple organizations are involved, it does not fully solve the problem of trust among them. As the devices involved in monitoring process might belong to different organizations, there is still a possibility that one of the parties can misconfigure its devices in order to achieve some own goal in an illegal way, with possibility to disrupt the process execution itself, affecting the final outcome. Blockchain technology is recognized as a solution for issues related to trust in multi-party process monitoring systems. Blockchain provides a shared immutable ledger, which guarantees that the information can be accessed and validated by all the participants of a process both during its execution and after it is completed, which builds the trust among them.
This use case represents a potential area where we can make use of synergy of various novel technologies and paradigms, such as IoT, Big Data and next generation networking together with block chain.

Supplementary Material