big data and next generation network challenges - phdassistance
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Big Data is defined as data whose dynamics, such as volume, velocity, truthfulness, and diversity, are substantially expanded and impossible to be handled by typical data management systems. Modern data analytics techniques are utilised to manage such large amounts of data. With the introduction of next-generation networks, the number of wireless devices is fast expanding. According to a CISCO index released in 2014, the number of wireless devices now outnumbers the world’s population [1]. The proliferation of data generated by such a varied spectrum of linked devices is unsurprising. Learn More:https://bit.ly/3yNpy7Y Contact Us: Website: https://www.phdassistance.com/ UK: +44 7537144372 India No:+91-9176966446 Email: [email protected]TRANSCRIPT
BIG DATA ANDNEXT GENERATIONNETWORKCHALLENGESAn Academic presentation byDr. Nancy Agnes, Head, Technical Operations, PhdassistanceGroup www.phdassistance.comEmail: [email protected]
Today's OutlineIntroduction
Major Milestones of Next Generation Networks
Next-Generation Networks: Current Standards and TechnologyEnablers
Data Analytics Perspective on Next Generation Networks
Current State-of-the-Art and Open Issues
Conclusion
With the advancement of next-generationcellular networks, such as 5G, the attentionhas switched to addressing increased datarate needs, micro cell potential, and millimetrewave spectrum.
High data speeds, minimal latency, and thehandling of large amounts of data are thegoals of next-generation networks.
These objectives will almost certainlynecessitate newer architecture designs,upgraded technology with probable backwardcompatibility, improved security algorithms, andthe ability to make intelligent decisions.
In this study, we identify the potential that 5Gnetworks can give, as well as the underlyingproblems that must be overcome in order for 5Gto be implemented and realised.
Introduction Big Data is defined as data whose dynamics, suchas volume, velocity, truthfulness, and diversity, aresubstantially expanded and impossible to be handledby typical data management systems.
Modern data analytics techniques are utilised tomanage such large amounts of data.
With the introduction of next-generation networks,the number of wireless devices is fast expanding.
According to a CISCO index released in 2014,the number of wireless devices now outnumbersthe world's population. The
Proliferation of data generated by such a variedspectrum of linked devices is unsurprising.
Modern data analytics techniques(big dataanalytics) will be used to efficiently handle andextract meaningful insights from such a largesupply of data.
Discussions on next-generation networks (5G) havegotten a lot of interest in the research community inthe previous few years.
Given that 4G is already a globally acceptedtechnology, possibilities and difficulties for 5G andits underlying technologies are being investigated.
Over the previous few years, several advanceshave been explored in the literature. Ultra-densenetworks, huge MIMO (Multiple-Input Multiple-Output), and millimeter-waves (mmWaves) are themost important of these
The number of wireless devices expectedto reach in the hundreds of billions withthe introduction of 5G.
As a result of the bandwidth-hungryapplications running on these devices, therequired data rates are also increasing.
Figure 1 shows a comparison of theexponential development in data rate.
Figure: Evolution of mobile generation networks.
Major Milestones ofNext GenerationNetworks
While there are various standards for 5G, not all of themmust be met at the same time and may vary dependingon the underlying conditions.
In video transmission, for example, a large data rate isessential, but latency and dependability can bedisregarded.
Latency and dependability are required inautonomous vehicles; however data ratecan be reduced marginally.
Figure 2 depicts a graphic representationof the required 5G functionalities.
The next generation networks will havenear-zero latency and maximumthroughput.
These characteristics necessitate quickand dependable inter-cell or intra-cellhand-off.
Hand-off is the technique of exchanging mobile network dynamics such as frequency,time slots, spreading code, or a combination of them without compromising servicequality.
As a result, for next-generation networks, hand-off management is crucial.
Accessing numerous radio access technologies makes handoff management morechallenging because of the basic requirements of next-generation networks such asextreme densification and high mobility.
Figure 3 depicts a handoff. PhD Assistance experts has experience in handlingdissertation and assignment in computer science research with assured 2:1distinction. Talk to Experts Now
Figure: Hand-off in next-generation networks (inspired from [8]).
Next-Generation Networks: CurrentStandards and Technology Enablers
The developments in future generation networks (5G)are driven by evolutionary technologies such as mm-Wave spectrum, ultra-dense networking, massiveMIMO, and unique application requirements [3].
Table 2 summarises the industry standardizationsand aspirations that are now in the works.
Table 2.Standards and technology enablers for SG
Data AnalyticsPerspective on NextGeneration Networks
Big Data and Machine Learning can rightfullybe referred to as the two pillars of 5G, given thevast volume of data generated and thesophisticated decision-making involved.
Congestion of mobile networks is unavoidable, giventhe rapid improvement of cellular technology andthe rise in the number of mobile devices.
As a result, big data faces processing issues becauseit differs greatly from ordinary data.
As previously stated, the number of devices in next-generation networks will expand by a factor of 1,000
BIG DATA IN NEXT GENERATION NETWORKS
It is clear that the amount of data transferred in 5G networks will be huge and diverse.
As a result, strategies for handling such large amounts of data would need to beinvestigated in order to optimise 5G networks.
The massive influx of data will make it difficult to meet the key needs and features of 5Gnetworks.
As a result, deploying 5G networks without dealing with huge data concerns is extremelydifficult. For dealing with this type of data, big data analytics approaches can be applied.
Artificial intelligence includes the field of machine learning. Machine learning is atechnique that allows computers to make judgments based on data input.
Data is the source of input for machine learning algorithms; this data can bediverse and come from a variety of places.
As a result, machine learning can assist in predicting future events based onhistorical data. [5] Figure depicts the many forms of machine learning andaccompanying algorithms.
OPTIMIZATION OF NEXT, GENERATION CELLULAR NETWORKS
Figure: Comparison of different types of machine learning.
Mobile networks are already complicated, and 5G networks are likely to be even more sothan their predecessors.
5G networks will be smarter and more intelligent in terms of network management,resource allocation, load balancing, cost efficiency, power efficiency, and so on, inaddition to being more sophisticated.
Machine learning techniques can be used to implement these characteristics for 5G.
As a result, we may conclude that machine learning will play a significant role in thedeployment of 5G networks.
A neural network is a type of Artificial Intelligence that functions similarly to thehuman brain.
Millions of neurons in the human brain make decisions after examining aspecific activity.
Similarly, there are many nodes in neural networks. The input layer, outputlayer, and hidden layer are all connected to these nodes.
ROLE OF NEURAL NETWORKS - NEXT GENERATION NETWORKS
The organisation and weights of these connected nodes define the output of aneural network.
Neural networks can be thought of as non-digital computers.
The field of neural networks research has been under consideration fordecades.
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Current State of theArt and Open Issues
We will briefly cover some open topics in thissection.
This also provides guidance for future research atthe intersection of big data and 5G, as tacklingthese difficulties can lead to substantial advances.
Modern big data analytics approaches can help reduce the cost of computing andcaching for next-generation networks.
Resources will be efficiently dispersed and exploited in this manner, resulting in abalance of caching and computing overhead.
For example, interim and final results should only be saved if they are useful, asstoring all of the data is expensive.
PROACTIVE CACHING AND COMPUTING:
Big Data Analytics reveals hidden knowledge in massive amounts of data. As a result,large-scale data analysis can raise security and privacy concerns.
Data should be well encrypted during the storage, administration, and processingstages to ensure that it cannot be tampered or altered.
Furthermore, authorised entities should only be able to access the data through securechannels.
As a result, security and privacy problems are important considerations for such a large-scale data analysis and should be addressed thoughtfully.
SECURITY AND PRIVACY:
Different types of big data sources exist, each with its own data rate, mobility, andpacket loss. In wireless networks, analysing diverse data is difficult.
Spatial and temporal dynamics are brought by heterogeneous data.
As a result, for large spatiotemporal data analysis in mobile networks, unusualmethodologies are necessary.
BIG HETEROGENEOUS DATA:
In this Blog, we will provide an overview of the upcoming 5G communicationnetworks.
We also go over the many requirements, problems, and design issues that mustbe addressed in order for 5G networks to be realised.
Ultra-dense networking, millimetre wave spectrum, and massive MIMO are amongthe important technologies highlighted.
We outline the obstacles that must be solved, as well as viable architecturedesigns for 5G deployment.
We also discuss the energy issue in 5G networks, and discover that it isconstantly at the top of the list of 5G network issues.
Service models for 5G are being considered during device development, and theirbackward compatibility will be critical for both users and service providers.
We also provide a big data perspective on 5G, as well as the opportunity thatmachine learning techniques provide for learning, inference, and decision makingon 5G data.
There are many more domains in 5G networks that have not been explored indepth in this text but are crucial.
The security and privacy of prospective designs, hardware, and data transferprotocols in 5G networks are key concerns that necessitate additionalinvestigation.
