THE INTERNET OF THINGS is a unique system of connected objects that can collect, process, and transfer data with the help of wireless networks without the assistance of humans. The main challenge with IoT testing is the fact that it covers many industries and use cases, with massive cross-platform deployment of embedded technologies. Many alliances are trying to create a single protocol to allow all global devices to communicate with each other, yet, there are still many standards one can choose from. The devices can rely on Zigbee, Thread, Bluetooth Mesh, or Wi-Fi. as well as LoRa, or others and the more standards, the more challenging it is to do proper testing. Experts predict there will be 41 billion IoT devices by 2027, with another 127 devices connected every second. That’s a lot of things. With the proliferation of IoT devices, connected device testing has become more important than ever. As is the case with any product connected or not it’s always good to thoroughly test it to ensure any issues won’t adversely impact the performance of the rest of the system. Testing brings predictability to the system and reveals any potentially harmful bugs, ensuring the device meets a high standard of quality and the expectations of end users.

1. TEAM LEAD:
 M.KARTHIKEYAN
TEAM MEMBERS:
 KANNAN.S
 BARATH RAO
 JOTHAM ISAAC JESUDASAN
 KAVI SHEKHAR
TEAM NAME: INGENIOUS TINKERERS
COLLEGE: MEENAKSHI SUNDARARAJAN
ENGINEERING COLLEGE
Test automation asserting IoT

2. ABSTRACT
 Introduction
 Problem statement
 Solutions
 Implementation
 Algorithms
 Applications of IA
 5G triangle
 Frameworks
 Reasons to go for automation
 Challenges
 SWOT ANALYSIS
 Future scope
 Conclusion
 References

3. INTRODUCTION
 THE INTERNET OF THINGS is a unique system of connected objects
that can collect, process, and transfer data with the help of wireless
networks without the assistance of humans. The main challenge with IoT
testing is the fact that it covers many industries and use cases, with massive
cross-platform deployment of embedded technologies. Many alliances are
trying to create a single protocol to allow all global devices to communicate
with each other, yet, there are still many standards one can choose from.
The devices can rely on Zigbee, Thread, Bluetooth Mesh, or Wi-Fi. as well
as LoRa, or others and the more standards, the more challenging it is to do
proper testing.
 Experts predict there will be 41 billion IoT devices by 2027, with another
127 devices connected every second. That’s a lot of things. With the
proliferation of IoT devices, connected device testing has become more
important than ever. As is the case with any product connected or not it’s
always good to thoroughly test it to ensure any issues won’t adversely
impact the performance of the rest of the system. Testing brings
predictability to the system and reveals any potentially harmful bugs,
ensuring the device meets a high standard of quality and the expectations of
end users.

4. PROBLEM STATEMENT
HOW CAN WE AUTOMATE TESTING OF IOT DEVICES IN
A 5G READY ENVIRONMENT?

5. SOLUTIONS
 Intelligent automation is the combination of artificial intelligence ,machine learning and
process automation that is used to create smart business process and workflows that think,
learn and adapt on their own. It provides next generation tools and services there by
eliminating repetitive and replicable tasks. These systems are designed to learn from
experience, adapt to new situations, and make decisions based on real-time data
INTELLIGENT AUTOMATION

6. IMPLEMENTING INTELLIGENT AUTOMATION IN AUTOMATING
TESTING OF IOT DEVICES
By incorporating AI and machine learning techniques, intelligent automation can help to improve the efficiency and
effectiveness of testing IoT devices. Some ways in which intelligent automation can be used for testing IoT devices
include:
1. Test case generation: AI algorithms can be used to generate test cases for IoT devices, allowing for more
comprehensive testing and identifying potential issues that might have been missed by manual testing.
2. Test case execution: AI-powered robots can be used to execute test cases on IoT devices, allowing for faster and more
efficient testing.
3. Test case optimization: Machine learning algorithms can be used to analyze test results and adapt testing strategies
accordingly, optimizing the testing process and identifying areas that need improvement.
4. Predictive testing: AI-based models can be used to predict the behavior of IoT devices, allowing for testing to be done
in a simulated environment, which can be more efficient, and cost-effective.
5. Security testing: Machine learning-based models can be used to identify potential security threats and vulnerabilities
in IoT devices, allowing for more effective testing of security-related issues.
6. Continuous testing: AI-based tools can monitor IoT devices and provide real-time feedback and analytics, which can
be used for continuous testing and identifying issues as soon as they arise

7. ALGORITHMS
These are several algorithms used for implementing intelligent automation technique
 Genetic Algorithm: It is a search heuristic used to generate test cases, it mimics the process of natural
selection by iteratively generating a population of test cases, evaluating the fitness of the test cases, and
selecting the most fit test cases for reproduction.
 Neural Networks: Neural networks are a type of machine learning algorithm that can be used to generate
test cases, predict the behavior of IoT devices, and identify potential security threats and vulnerabilities.
 Decision Trees: Decision trees are a type of machine learning algorithm that can be used to generate test
cases, predict the behavior of IoT devices, and identify potential security threats and vulnerabilities.
 Reinforcement learning: Reinforcement learning algorithms can be used to optimize testing strategies,
by analyzing test results and adapt testing strategies accordingly.
 Random Forest: Random Forest algorithm is an ensemble of decision trees, which can be used to predict
the behavior of IoT devices, and identify potential security threats and vulnerabilities.
 Q-learning: Q-learning algorithm is a type of reinforcement learning algorithm that can be used to
optimize testing strategies, by learning from the device's behavior and adapting testing strategies
accordingly.

8. APPLICATIONS OF IA
Intelligent automation can be used in a variety of applications, including:
1. Process automation: Intelligent automation can be used to automate repetitive tasks and processes, such as
data entry, financial transactions, and customer service.
2. Robotics: Intelligent automation can be used to control and program robots, allowing them to perform tasks
that would otherwise be difficult or impossible for humans to do.
3. Predictive maintenance: Intelligent automation can be used to predict when equipment is likely to fail,
allowing for preventative maintenance to be carried out before a failure occurs.
4. Quality control: Intelligent automation can be used to monitor and analyze data from production lines,
allowing for real-time quality control and identifying defects before they become a problem.
5. Cybersecurity: Intelligent automation can be used to monitor networks for suspicious activity, identify security
threats, and respond to them in real-time.
6. Self-driving cars: Intelligent automation can be used to control the steering, acceleration and braking of the
car, and make decisions on the road.

9. Intelligent automation techniques are being used to automate testing of IoT devices in various
industries. Some examples include:
1. Smart Home Devices: Automated testing using AI and machine learning algorithms is used to
test smart home devices such as thermostats, lights, and security cameras.
2. Industrial IoT: Intelligent automation is used to test industrial IoT devices such as sensors,
actuators, and control systems.
3. Healthcare IoT: Intelligent automation is used to test medical devices such as heart monitors,
blood glucose meters, and wearable fitness trackers.
4. Automotive IoT: Intelligent automation is used to test connected cars and other automotive IoT
devices such as sensors, cameras, and navigation systems.
5. Smart Cities: Intelligent automation is used to test IoT devices such as smart meters, traffic
lights, and air quality sensors, which are used to monitor and control various aspects of smart
cities.
6. Intelligent Transportation Systems: Intelligent automation is used to test the performance,
security, and reliability of various IoT devices used in Intelligent Transportation Systems such as
traffic cameras, sensors, and control systems

10. 5G TRIANGLE
 Enhanced mobile broadband (eMBB), which
primarily focuses on higher data rates with a peak
data rate improvement from 4G’s one Gbit/s to 20
Gbit/s.
 Ultra-reliable low-latency communications
(URLLC), also called critical machine-type
communications, is a new usage category that
requires enhanced capabilities for high reliability,
low latency (as low as 1 ms), and high mobility (up
to 500 km per hour).
 Massive machine-type communications (mMTC)
or massive Internet of Things (mIoT) is intended to
serve extremely high-density deployments of low-
complexity, low-power consumption IoT devices
(one million devices in a square kilometer)
transmitting low volumes of non-delay-sensitive
data

11. FRAMEWORK TO TEST 5G
NETWORK DEVICES
 A framework for conducting 5G testing refers to a set of
guidelines, specifications, and procedures that are used
to test and evaluate the performance, functionality, and
compliance of 5G networks and devices. The framework
provides a standardized approach for conducting testing,
and can include test cases, test procedures, and test
tools. It ensures that the testing process is consistent,
reliable, and accurate, and that the results can be easily
compared and evaluated.
 A framework for conducting 5G testing can cover
various aspects of the technology, including radio
access, core network, device conformance, security,
performance, and interoperability. It can also include
different types of testing, such as functional testing,
performance testing, and conformance testing, to ensure
that the 5G network and devices meet industry standards
and specifications.
 3GPP (3rd Generation Partnership Project): This is an international
standardization organization that develops specifications for 5G
networks and devices. The 3GPP framework covers various aspects of
5G testing, including radio access, core network, and device
conformance.
 OMA (Open Mobile Alliance): This is an industry organization that
develops specifications for 5G devices, including test cases and test
procedures for device certification.
 ETSI (European Telecommunications Standards Institute): This is a
European standards organization that develops specifications for 5G
networks and devices, including test cases and test procedures for
conformance testing.
 NGMN (Next Generation Mobile Networks): This is an industry
organization that develops specifications for 5G networks and devices,
including test cases and test procedures for network and device
interoperability testing.
 ATIS (Alliance for Telecommunications Industry Solutions): This is an
industry organization that develops specifications for 5G networks and
devices, including test cases

12. Why we need automated testing for IoT devices?
 Complexity: IoT devices often have multiple components and interfaces, such as hardware, software, and
networks. Automated testing can help ensure that all of these components are functioning correctly and that
the device operates as intended.
 Volume of testing: IoT devices generate large amounts of data and can have many different use cases.
Automated testing can help ensure that all of the data is accurate and that the device can handle the volume
of data.
 Time and cost savings: Automated testing can save time and costs compared to manual testing. Automated
tests can be run quickly and repeatedly, reducing the time and resources needed for testing.
 Consistency: Automated tests can be run consistently, ensuring that the same tests are performed every time
and that the results are comparable. This can help identify any inconsistencies and improve the quality of the
device.
 Repeatability: IoT devices are subject to updates, upgrades and changes in the environment. Automated
testing can help ensure that devices continue to function correctly after these changes.
 Safety and security: IoT devices are increasingly being used in critical applications such as healthcare,
transportation, and manufacturing. Ensuring that these devices are safe and secure is essential. Automated
testing can help identify potential safety and security issues before they are deployed in the field.

13. CHALLENGES
Some of the challenges need to be tackled:
 Variety of devices: IoT devices come in many different forms and sizes, and each may have unique testing requirements. This makes
it difficult to develop a one-size-fits-all testing solution.
 Complexity of systems: IoT devices are often part of larger systems that include other devices, networks, and cloud-based services.
This complexity makes it difficult to test all aspects of the system and ensure that it works as expected.
 Interoperability: 5G networks will need to support a wide range of IoT devices and use cases, which can make it difficult to ensure
that devices from different manufacturers work seamlessly together. Automated testing will need to take into account these
interoperability issues.
 Latency: 5G networks have lower latency than previous generation networks, this means that the devices need to respond very fast to
the commands, automated testing will need to take into account these latency issues.
 Bandwidth: 5G networks offer much higher bandwidth than previous generation networks, this means that the devices need to handle
larger amount of data. Automated testing will need to take into account these bandwidth requirements.
 Security concerns: IoT devices often collect and transmit sensitive data, making them a target for hackers. Automated testing must
take into account these security concerns and ensure that the devices are protected against potential attacks.
 Continuous testing: IoT devices require continuous testing to ensure they are working properly and to identify any issues as soon as
they arise.
Overall, automating testing for IoT devices in a 5G environment will require more advanced testing tools and techniques, and a
deeper understanding of the unique challenges that 5G networks pose for IoT devices.

14. SWOT ANALYSIS
STRENGTH: WEAKNESS:
 Efficiency Initial investment
 Consistency Complexity
 Scalability Limited flexibility
 Continuous testing Limited real world testing
 Security Dependency of quality of automation
 Identifying bugs
OPPORTUNITES: THREATS:
 Advancements in technology Competition
 Increased demand for IoT devices Security concerns
 Remote testing Limited resources
 Crowd testing Dependency on the quality of automation

15. Future scope
Advancements in automated testing of IoT devices are primarily focused on making the testing process more efficient,
accurate, and cost-effective. Here are some of the future advancements in automated testing of IoT devices
 As I have mentioned before Artificial intelligence (AI) and machine learning (ML) integration can take these automation to
next level. AI and ML can be used to analyze test results and predict potential failures, making the testing process more
efficient and accurate, and also in future it can provide some excellent results.
 Smart test automation: Smart test automation is a testing approach that uses AI and ML to automate the testing process. It
can adapt to changing conditions and optimize test cases to improve the efficiency of the testing process.
 Cloud-based testing: Cloud-based testing allows for remote testing and eliminates the need for physical test environments.
This can save time and costs, and also allows for testing on a wide range of devices and operating systems.
 Internet of Things (IoT) testing platforms: IoT testing platforms are specialized testing environments that can be used to
test IoT devices. These platforms can be used to simulate different network conditions and test the performance of IoT
devices under different scenarios.
 Cross-platform compatibility testing: With the increasing number of IoT devices and platforms, compatibility testing will
become more important. Automated testing can be used to ensure that IoT devices work seamlessly across different
platforms. Overall, the advancement in automated testing of IoT devices will lead to more efficient, accurate, and cost-
effective testing, which will help ensure the quality of IoT devices and their safe deployment in the market

16. CONCLUSION
 In conclusion, automating testing for IoT devices in a 5G environment presents several challenges,
including complexity, security, interoperability, latency, bandwidth, mobility, edge computing and
virtualization. To overcome these challenges, automated testing will need to take into account the
unique requirements of 5G networks and IoT devices, and use advanced testing tools and techniques.
Additionally, it will require a deeper understanding of the technologies and use cases involved, and the
ability to adapt testing strategies as the technology and use cases evolve. As the number of IoT devices
continues to grow, and 5G networks become more widely adopted, automated testing will become
increasingly important for ensuring the performance, security and reliability of IoT devices in a 5G
environment.

17. REFERENCES
https://www.ericsson.com/en/blog/2018/12/testing-5g-networks-challenges-and-
opportunities
https://go.gale.com/ps/i.do?id=GALE%7CA652826941&sid=googleScholar&v=2.1&it=r&li
nkaccess=abs&issn=15551229&p=AONE&sw=w&userGroupName=anon%7E37cdfdfe
https://www.functionize.com/blog/automating-iot-device-testing
https://www.ibm.com/in-en/topics/intelligent-automation

18. THANK YOU
BY TEAM:
INGENIOUS TINKERERS