This document discusses big data, internet of things (IoT), and analytics in networks. It begins with an introduction to the rise of interconnected devices and vast amounts of data generated through IoT. It then outlines a plan to discuss big data characteristics, the concept of IoT, and different types of analytics in networks. Specific sections cover background on big data and IoT, performance, security, and predictive analytics, and case studies are provided on applying network monitoring in smart cities and industrial IoT. The document concludes that network analytics plays a critical role in IoT deployments by providing insights to improve decision-making, efficiency, and user experience.

1. Big Data Internet of things analytics
in Network
By Armelle Zeba, Kilaru Rathithya, Ifeoluwa Odedele
For
Group 4 CIS 515

2. Introduction
• The rapid advancement of technology in recent
years has given rise to the proliferation of
interconnected devices, commonly known as the
Internet of Things (IoT). This vast network of
smart devices, sensors, and machines has
transformed numerous industries, enabling
seamless communication and data exchange
between physical objects and digital systems. As
the IoT ecosystem continues to expand, the sheer
volume of data generated by these
interconnected devices has become
unprecedented, leading to the emergence of a
new field known as gig data analytics.

3. Plan
A) Background Description B) Big Data
B1-VOLUME Velocity & Variety in
B2-Drivers for BIG DATA &SOURCES
C) Internet of things
C1- Vision & Concept
C2-Technological standards
governing smart manufacturing
D) Analytics in Network
D1- Performance Analytics
D2- Security Analytics
D3- Users behavior Analytics
D4- Predictive Analytics.

4. Background
description
• The concept of the Internet of Things traces its roots
back to the early 1980s when computer scientist and
innovator Mark Weiser first introduced the idea of
ubiquitous computing. Weiser envisioned a world
where everyday objects would be embedded with
sensors and seamlessly communicate with each other,
creating a "calm technology" environment that
integrates into our daily lives.
• Over the years, advancements in technology,
including the miniaturization of sensors, improvements
in connectivity, and the proliferation of wireless
networks, paved the way for the realization of Weiser's
vision. The exponential growth of the internet and the
availability of affordable computing power further
accelerated the development of the IoT ecosystem.

5. Big Data
• Big data refers to extremely large
datasets that cannot be processed or
analyzed using traditional data
processing tools and techniques. These
datasets are typically so large and
complex that they require specialized
hardware, software, and algorithms to
process, store, and analyze them.

6. VOLUME
Velocity &
Variety in
• The term "big data" is often associated with the three Vs: volume, velocity,
and variety. Volume refers to the sheer amount of data being generated and
collected, velocity refers to the speed at which this data is being generated
and must be processed, and variety refers to the diverse types of data that
are being generated and collected, including structured, semi-structured,
and unstructured data

7. Drivers for BIG DATA
&SOURCES
• Big data is generated by a wide range of sources, including social media, mobile devices, sensors, and IoT
devices. It has numerous applications across industries, including healthcare, finance, retail, and transportation. With
the help of big data analytics tools, organizations can gain insights and make data-driven decisions that can help them
optimize their operations, improve customer experience, and drive innovation.
• Drivers for Big data
• Big Data emerged in the last decade from a combination of business needs and technology.
• innovations. A number of companies that have Big Data at the core of their strategy have become.
• very successful at the beginning of the 21st century. Famous examples include Apple, Amazon,
• Facebook and Netflix.
• Several business drivers are at the core of this success and explain why Big Data has quickly.
• risen to become one of the most coveted topics in the industry. Six main business drivers can be.
• identified:
• 1. The digitization of society.
• 2. The plummeting of technology costs.
• 3. Connectivity through cloud computing.
• 4. Increased knowledge about data science.
• 5. social media applications.

8. Internet of
things
• The Internet of Things (IoT) is a term used to describe the
network of physical objects or "things" that are embedded with
sensors, software, and other technologies that allow them to
connect and exchange data with other devices and systems over
the internet. These objects can be anything from cars, appliances,
wearable devices, industrial equipment, and even buildings.
• The IoT has the potential to transform many industries and
aspects of daily life by enabling more efficient and automated
systems, improving decision-making processes, and enhancing
the overall user experience. For example, IoT sensors can be used
in smart homes to monitor and control energy usage, in
healthcare to monitor patient health remotely, and in agriculture
to optimize crop growth and irrigation.

9. VISION & CONCEPT
• The view of the concept of IoT in smart manufacturing can be
easily categorized into two different perspectives, which are thing-
centric and the internet-centric. The thing centric can clear be
taken from an angle in which the technology mainly depends on
the smart devices as a center stage. This kind of device is aiming at
making sure that every organization and the companies employ the
use of the tools in monitoring the daily activities of the company
and the movement of the employees in high-risk missions. On the
internet-centric, it considers mostly internet services as the center
stage. It is also clear from these that Things are primarily
responsible for generating the data. In smart manufacturing, most
of the devices will, in a great way, ensure that the company realizes
great achievement and, at the same time, ensure that the set goals
are achieved (Santhosh, Srinivsan, & Ragupathy, 2020).

10. Technological standards
governing smart
manufacturing
• Technical measures can be
considered very important in IoT
smart manufacturing, and they are
essential in the invention,
economic growth, innovation, and
commercial transactions.

11. Analytics in
Network
• Analytics in network refers to the process of collecting, analyzing,
and interpreting data related to network performance, security, and
user behavior. Network analytics can provide insights into the health
and efficiency of a network, help identify potential issues before
they become critical problems, and improve network security.

12. Performance
analytics
• This involves collecting data on
network performance metrics such
as bandwidth utilization, latency,
and packet loss. Performance
analytics can help network
administrators identify areas
where network capacity may need
to be increased or where network
latency is causing issues.

13. Security analytics
• This involves collecting data on
network security events such as
intrusion attempts, malware
infections, and other security
incidents. Security analytics can
help identify potential security
threats and allow network
administrators to take appropriate
action to mitigate them.

14. User behavior
analytics
• This involves collecting data on
user behavior within the network,
such as the applications they use,
the websites they visit, and the
devices they connect to the
network with. User behavior
analytics can help network
administrators identify patterns of
activity that may indicate security
threats or performance issues.

15. Predictive
analytics
• This involves using machine
learning algorithms to analyze
network data and predict potential
issues before they occur. Predictive
analytics can help network
administrators proactively address
issues before they become critical
problems

16. IoT Analytics Framework for
Network Performance
Monitoring

17. Overview of Network
Performance Monitoring in IoT
• To maintain stable connectivity and maximize operational
efficiency, efficient network performance monitoring is a
must in IoT environments.
• Network performance monitoring enables organizations to
proactively detect and address issues, leading to improved
performance and enhanced user experiences.
• However, monitoring network performance in IoT comes
with its own set of challenges.
• High data volume and velocity generated by IoT devices
require efficient data collection, processing, and analysis.
• Security and privacy concerns are critical, as IoT networks
are vulnerable to unauthorized access and data breaches.
• Despite these challenges, network performance monitoring
in IoT is crucial for maintaining reliable and efficient
operations.

18. Why
Monitor
Network
Performance
in IoT?
Reliable Communication:
Ensure seamless and
uninterrupted data exchange
between IoT devices for real-
time monitoring and control.
Connectivity: Identify and
resolve connectivity issues for
continuous device connectivity.
Optimized Performance:
Address bottlenecks and
latency issues for optimal
network performance.
Enhanced User Experience:
Minimize disruptions and
deliver a smooth user
experience.
Proactive Issue Resolution:
Identify and resolve network
issues before they impact
operations.

19. Key Metrics for
Monitoring IoT Network
Performance
• Latency, Bandwidth, and Packet Loss: Monitoring
latency, bandwidth utilization, and packet loss
helps assess the speed and reliability of data
transmission in IoT networks, ensuring optimal
performance and responsiveness.
• Device Connectivity and Responsiveness:
Tracking device connectivity and responsiveness
enables the identification of potential issues and
ensures that IoT devices remain connected to the
network, delivering timely data exchange and
control.

20. Monitoring Tools and
Techniques for IoT Network
Performance
• Proactive Monitoring: Predictive
analytics and anomaly detection for
early issue detection.
• Real-time Monitoring: Network traffic
analysis and event correlation for
immediate insights.
• Data Collection and Analysis: Network
sniffers, log analysis, and machine
learning algorithms for trend
identification and optimization

21. Visualization of Network
Performance Data
• Importance of Data Visualization: Visualizing network
performance data helps in understanding complex
patterns, trends, and anomalies, providing actionable
insights for optimizing IoT network performance.
• Dashboards and Visual Analytics: Interactive
dashboards and visual analytics tools enable users to
monitor key performance metrics, analyze data trends,
and identify areas that require attention, facilitating
data-driven decision-making.
• Real-time Visualization for Immediate Insights: Real-
time visualization tools display network performance
data in real-time, allowing for immediate detection of
anomalies and performance deviations, enabling
prompt response and proactive management.

22. Benefits of Network
Performance Monitoring
• Monitoring network performance allows for early
detection of potential issues, enabling prompt
resolution and minimizing downtime, thereby
ensuring uninterrupted operations in IoT
environments.
• By monitoring network performance,
organizations can identify and optimize resource
allocation, bandwidth utilization, and data
transmission, resulting in improved operational
efficiency and cost-effectiveness.
• Network performance monitoring helps identify
security vulnerabilities, detect unauthorized
access attempts, and ensure compliance with
data privacy regulations, safeguarding IoT
networks and protecting sensitive data.

23. Case Studies: Network
Performance
Monitoring in IoT
• Example 1: Smart City
• Successful Implementation: Network performance
monitoring deployed in a smart city infrastructure enabled
real-time monitoring of traffic flow and public services,
leading to efficient resource utilization and improved city
management.
• Real-world Results: Reduced traffic congestion by 20%,
enhanced emergency response times, and increased citizen
satisfaction with public services.
• Example 2: Industrial IoT
• Successful Implementation: An industrial IoT system with
multiple devices and sensors was implemented with
network performance monitoring tools and techniques.
• Real-world Results and Impacts: The system was able to
optimize network performance and increase efficiency,
leading to reduced downtime and improved production
processes

24. Summary and
Conclusion
• Recap of Network Performance Monitoring in IoT:
• Network performance monitoring is essential for
ensuring reliable communication, optimizing
performance, and enhancing user experiences in IoT
environments.
• Key metrics for monitoring include latency, bandwidth,
packet loss, device connectivity, responsiveness,
throughput, and data transmission rates.
• Next Steps:
• Invest in robust monitoring tools and techniques to
proactively detect and resolve network issues in real-
time.
• Continually evaluate and adapt monitoring practices to
keep pace with the evolving IoT landscape and ensure
efficient resource utilization.

25. Benefits of Internet of
Things
• IoT has played an influential role in transforming different
industries:
• Cost saving
• Optimal utilization of assets
• Improved Productivity
• Efficient processes
• More business opportunities.
• Improved customer experience.
• Increased mobility and agility.
• Accelerate the Development of Cloud Technologies

26. IoT Technologies and Applications

28. Challenges
Associated with IoT
• Even though, IoT has positively transformed the
operations of businesses operating across the globe,
but there are several loopholes that make IoT
vulnerable. Following are some of the challenges
associated with IoT:
• Data Management
• Lack of Standards
• Security concerns
• Privacy concerns
• Complexity

29. Practical Application of IOT

31. Trends and the future of Internet of things

32. Recommendations
• Compliance – IoT must abide by
laws, just like any other
technology used in the
commercial world. Given that
many people view standard
software compliance as a
conflict, its intricacy makes the
issue of compliance appear to be
exceedingly difficult.

33. Conclusion
• IoT has flourished as a technology in the IT sector
thanks to the emergence of big data and artificial
intelligence. Through a variety of technologies and
applications, it has progressively brought about a lot
of technical advances in our daily lives, which in turn
helps make our lives easier and more pleasant.
• Network analytics plays a critical role in the
success of IoT deployments. By providing real-time
insights into device behavior, network performance,
and security threats, network analytics tools enable
organizations to make better decisions, improve
efficiency, and enhance the user experience.

34. References
• www.google.com
• https://en.wikipedia.org/wiki/Internet_of_Things
• Mouha, R.A.(2021) Internet of Things (IoT). Journal of Data Analysis and Information Processing, 9, 77-101. https://doi.org/10.4236/jdaip.2021.92006.
• Gayantha M.H, W.M.C.J.T. Kithulwattab,W.M.C.J.T, Rathnayakaa, R.M.K.T.(2022). The Interconnection of Internet of Things and Artificial Intelligence: A Review Sri Lankan Journal
of Applied Sciences Vol.1.1: 50-59.
• Gowda,V.D, Sridhara, Naveen,K.B ,Ramesha,M and Naveena pai G.(2020). Internet of things: internet revolution, impact, technology road map and features. Advances in
Mathematics: Scientific Journal. 9,7:4405–4414.
• Katare,G., Padihar G and Qureshi,Z (2022). "Challenges in the Integration of Artificial Intelligence and Internet of Things", International Journal of System and Software Engineering,
vol. 6, no. 2, 2022.
• P. Calyam and R. Venkatesan, "Internet of Things in Network Analytics," in IEEE Internet Computing, vol. 22, no. 1, pp. 43-51, Jan.-Feb. 2018, doi: 10.1109/MIC.2018.011591935.
• Delaney, D. T., & P. O’Hare, G. M. (2016, December 1). A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection.
https://doi.org/10.3390/s16122038
• Li, S., Da Xu, L., & Zhao, S. (2015). The internet of things: a survey. Information Systems Frontiers, 17(2), 243-259.
• Guo, B., Chen, H., & Yu, S. (2018). An IoT network performance monitoring framework based on machine learning algorithms. International Journal of Distributed Sensor Networks,
14(7), 1550147718791332.
• Bhushan, B., Arora, M., & Arora, A. (2020). An IoT network performance monitoring framework using data analytics. In 2020 11th International Conference on Computing,
Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
• Atzori, L., Iera, A., & Morabito, G. (2017). The internet of things: A survey. Computer Networks, 54(15), 2787-2805.
• Santhosh, N., Srinivsan, M., & Ragupathy, K. (2020). Internet of Things (IoT) in smart
• manufacturing. IOP Conference Series: Materials Science and Engineering, 764, 012025.
• https://doi.org/10.1088/1757-899x/764/1/012025
• https://www.coursera.org/articles/big-data-analytics?utm_medium=sem&utm_source=bg&utm_campaign=B2C_NAMER__coursera_FTCOF__branded-search-country-US-country-
CA&campaignid=278172071&adgroupid=1209463253056290&device=c&keyword=https%3A%2F%2Fwww.coursera.org%2F&matchtype=b&network=o&devicemodel=&adposition
=&creativeid=&hide_mobile_promo&msclkid=604017ee196c14c4ce4573b78aae62f9&utm_term=https%3A%2F%2Fwww.coursera.org%2F&utm_content=01%20-
%20Branded%20Search%20-%20US%20-%20dynamic%20ad%20group
• Favaretto M, De Clercq E, Schneble CO, Elger BS. What is your definition of Big Data? Researchers' understanding of the phenomenon of the decade. PLoS One. 2020 Feb
25;15(2):e0228987. doi: 10.1371/journal.pone.0228987. PMID: 32097430; PMCID: PMC7041862.
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7041862/
• https://www.coursera.org/articles/data-analytics
• Citation: Perrier L, Blondal E, Ayala AP, Dearborn D, Kenny T, Lightfoot D, et al. (2017) Research data management in academic institutions: A scoping review. PLoS ONE 12(5):
e0178261. https://doi.org/10.1371/journal.pone.0178261

35. Please feel free to ask any questions you may have!