E-ISSN:2250-0758
P-ISSN:2394-6962

Research Article

Artificial Intelligence

International Journal of Engineering and Management Research

2025 Volume 15 Number 2 April
Publisherwww.vandanapublications.com

Leveraging Artificial Intelligence for Enhanced Internet of Things Applications

Jain L1*, Bhardwaj S2, Puri M3, Sandhu MK4
DOI:10.5281/zenodo.15314664

1* Leena Jain, Department of Computer Science, Saroop Rani Govt. College for Women, Amritsar, Punjab, India.

2 Sushil Bhardwaj, Department of Computer Applications, RIMT University, Mandi Gobindgarh, Punjab, India.

3 Maalti Puri, Department of Electronics and Communication Engineering, Khalsa College of Engineering and Technology, Amritsar, Punjab, India.

4 Mandeep Kaur Sandhu, Department of Computer Science, Guru Nanak Dev University College, Pathankot, Punjab, India.

This paper explores the symbiotic relationship between Artificial Intelligence (AI) and the Internet of Things (IoT), highlighting the significant role that AI plays in enhancing IoT applications. The paper begins by providing an overview of both AI and IoT technologies and their individual capabilities. It then delves into the ways in which AI augments IoT systems, including data analytics, predictive modeling, anomaly detection, and autonomous decision-making.

Keywords: Artificial Intelligence, Internet of Things, Machine Learning

Corresponding Author How to Cite this Article To Browse
Leena Jain, Department of Computer Science, Saroop Rani Govt. College for Women, Amritsar, Punjab, India.
Email: 		Jain L, Bhardwaj S, Puri M, Sandhu MK, Leveraging Artificial Intelligence for Enhanced Internet of Things Applications. Int J Engg Mgmt Res. 2025;15(2):40-46.
Available From
https://ijemr.vandanapublications.com/index.php/j/article/view/1729

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2025-01-03 2025-01-31 2025-02-28 2025-03-31 2025-04-19
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
None Nil Yes 4.28

© 2025 by Jain L, Bhardwaj S, Puri M, Sandhu MK and Published by Vandana Publications. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

Int J Engg Mgmt Res 2025;15(2)40
Download PDFBack To Article1. Introduction2. Motivation for
Integrating AI with IoT3. Case Studies and
Real-World Examples4. Challenges5. Future Directions6. ConclusionReferences
Jain L, et al. Leveraging Artificial Intelligence

1. Introduction

Brief Overview of AI and IoT Technologies:

Artificial Intelligence refers to the simulation of human intelligence processes by computer systems. It encompasses various techniques and algorithms that enable machines to perceive their environment, learn from data, reason and make decisions, and interact with humans and other systems. AI techniques include machine learning [1], deep learning[2], natural language processing[3], computer vision, and robotics[4]. AI systems are capable of performing tasks that typically require human intelligence, such as problem-solving, pattern recognition, language translation, and decision-making[4]. The Internet of Things is a network of interconnected devices embedded with sensors, software, and other technologies that enable them to collect, exchange, and analyze data. These devices can range from everyday objects such as smartphones, wearable devices, and home appliances to industrial machinery and infrastructure components[5]. IoT systems enable the seamless integration of physical objects into digital networks, allowing for remote monitoring, control, and automation of various processes. Key components of IoT include sensors, actuators, connectivity technologies (such as Wi-Fi, Bluetooth, and cellular networks), and cloud computing platforms for data storage and analysis[6,7].

2. Motivation for Integrating AI with IoT

The motivation for integrating AI with IoT lies in the potential synergies and enhanced capabilities that arise from combining these two technologies:

Data-Driven Insights: IoT generates vast amounts of data from connected devices, sensors, and systems. AI algorithms can analyze this data to extract meaningful insights, identify patterns, and uncover hidden correlations that may not be apparent through traditional analysis methods[8].

Predictive Analytics: By leveraging AI techniques such as machine learning, IoT systems can predict future events, trends, and outcomes based on historical data. This enables proactive decision-making, predictive maintenance, and optimization of processes in various domains, leading to improved efficiency and cost savings[9].

Real-Time Decision-Making: AI enables IoT devices to make autonomous decisions in real-time based on the analysis of sensor data and environmental conditions. This autonomy is particularly valuable in critical applications such as healthcare, transportation, and manufacturing, where timely responses are essential to prevent or mitigate risks[10,11,12,13,14,15].

Personalization and Customization: AI-powered IoT systems can personalize experiences and tailor services based on individual preferences, behavior, and context [16]. This leads to more engaging user interactions, improved user satisfaction, and increased customer loyalty in areas such as smart homes, wearables, and e-commerce.

Adaptive and Self-Learning Systems: AI enables IoT devices to adapt to changing conditions, learn from experience, and continuously improve their performance over time. This self-learning capability is beneficial in dynamic environments where conditions may evolve, such as smart grids, supply chain management, and environmental monitoring [17,18,19].

Efficient Resource Utilization: AI-driven optimization algorithms can analyze IoT data to optimize resource allocation, energy consumption, and workflow efficiency. This results in cost reductions, energy savings, and sustainability benefits across various sectors, including smart cities, agriculture, and industrial automation[20].

3. Case Studies and Real-World Examples

Here are examples of successful AI-enabled IoT implementations across various industries and domains:

I. Healthcare:
a. Remote Patient Monitoring: AI-enabled IoT devices, such as wearable health trackers and remote monitoring systems, collect and analyze vital signs and health data in real-time. This allows healthcare providers to remotely monitor patients' health status, detect anomalies, and intervene proactively, improving patient outcomes and reducing hospital read missions [21, 22].
b. Predictive Diagnostics: AI algorithms analyze medical imaging data from IoT-enabled devices, such as MRI machines and CT scanners,


Int J Engg Mgmt Res 2025;15(2)41
Jain L, et al. Leveraging Artificial Intelligence

to assist radiologists in diagnosing diseases and identifying abnormalities earlier. This enhances diagnostic accuracy, reduces interpretation time, and improves patient care[23].

II. Smart Cities:
a. Traffic Management: AI-driven IoT systems collect and analyze data from traffic sensors, surveillance cameras, and GPS devices to optimize traffic flow, detect congestion, and predict traffic patterns. This enables city authorities to implement dynamic traffic control measures, reduce commute times, and improve road safety [28,29].
b. Waste Management: IoT-enabled waste bins equipped with sensors and AI algorithms monitor waste levels in real-time. These systems optimize waste collection routes, schedule pickups based on demand, and reduce operational costs while minimizing environmental impact[34,35].

III. Manufacturing:
a. Predictive Maintenance: AI-enabled IoT sensors monitor equipment health and performance metrics in manufacturing plants. By analyzing historical data and detecting patterns indicative of impending equipment failures, predictive maintenance systems can schedule maintenance activities proactively, minimize downtime, and prevent costly breakdowns [43,44].
b. Quality Control: AI algorithms analyze sensor data from IoT-enabled production lines to detect defects, deviations, and anomalies in real-time. This ensures product quality consistency, reduces waste, and improves overall manufacturing efficiency [45].

IV. Agriculture:
a. Precision Agriculture: AI-driven IoT systems integrate data from soil sensors, weather stations, drones, and satellite imagery to optimize irrigation, fertilization, and crop management practices. By providing farmers with actionable insights and recommendations, these systems increase crop yields, conserve resources, and promote sustainable agriculture[46,47].
b. Livestock Monitoring: IoT devices equipped with biometric sensors and GPS trackers monitor the health, behavior, and location of livestock animals. AI algorithms analyze this data to detect signs of illness, identify optimal feeding schedules, and prevent livestock losses, improving animal welfare and farm productivity [48, 49].

V. Retail:
a. Personalized Marketing: AI-powered IoT devices,

such as smart shelves and beacons, capture customer behavior data in retail stores. By analyzing this data in real-time, retailers can deliver personalized promotions, product recommendations, and targeted advertising to shoppers, enhancing the shopping experience and driving sales [60,61,62,63,64,65,66,67].
b. Inventory Management: IoT sensors track inventory levels, product movements, and shelf conditions in retail warehouses and stores. AI algorithms analyze this data to optimize inventory replenishment, reduce stockouts, and minimize excess inventory, leading to improved supply chain efficiency and cost savings [68,69,70,71,72,73].

These examples demonstrate how AI-enabled IoT implementations are transforming industries and domains by enabling data-driven decision-making, automation, and optimization of processes, ultimately leading to increased efficiency, productivity, and autonomy.

4. Challenges

Integrating AI into IoT systems offers numerous benefits, but it also presents several challenges and limitations that need to be addressed:

a. Data Privacy Concerns:
Collecting and processing vast amounts of data from IoT devices raises privacy concerns regarding the collection, storage, and use of personal information. Users may be apprehensive about sharing sensitive data, such as health or location information, especially if they perceive risks to their privacy or security. Ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) in the European Union or the California Consumer Privacy Act (CCPA) in the United States, is essential to mitigate privacy risks and maintain user trust.

b. Interoperability Issues:
IoT ecosystems often comprise heterogeneous devices and protocols from different manufacturers, leading to interoperability challenges. Integrating AI algorithms with diverse IoT platforms and devices may require standardized communication protocols, data formats, and interoperability frameworks to ensure seamless integration and compatibility. Lack of interoperability can hinder data sharing, hinder system scalability, and increase development complexity, resulting in fragmented IoT deployments and suboptimal performance.


Int J Engg Mgmt Res 2025;15(2)42
Jain L, et al. Leveraging Artificial Intelligence

c. Ethical Considerations:
AI-powered IoT systems raise ethical concerns related to algorithmic bias, fairness, transparency, and accountability. Biased algorithms may perpetuate discrimination or exacerbate existing inequalities, particularly in decision-making processes related to healthcare, finance, and criminal justice. Ensuring fairness and transparency in AI models requires robust governance frameworks, algorithmic auditing, and ethical guidelines to promote responsible AI development and deployment.

d. Security Risks:
IoT devices are often vulnerable to security threats, such as hacking, malware attacks, and unauthorized access. AI-driven IoT systems may amplify security risks if malicious actors exploit vulnerabilities in AI algorithms or manipulate data inputs to subvert decision-making processes. Implementing robust cybersecurity measures, such as encryption, authentication, access control, and intrusion detection, is essential to protect AI-enabled IoT systems from cyber threats and safeguard sensitive data.

e. Data Quality and Reliability:
AI algorithms rely on high-quality, reliable data to produce accurate and actionable insights. However, IoT data streams may suffer from noise, inaccuracies, or inconsistencies due to sensor errors, environmental factors, or communication disruptions.

Ensuring data quality and reliability requires data validation, cleaning, and preprocessing techniques to filter out irrelevant information, correct errors, and maintain data integrity throughout the AI pipeline.

f. Resource Constraints:
IoT devices often have limited computational resources, memory, and battery life, posing challenges for deploying AI algorithms with high computational complexity and energy consumption requirements. Developing lightweight AI models optimized for edge computing and resource-constrained IoT devices can mitigate resource constraints while enabling real-time processing and decision-making at the network edge.

5. Future Directions

To overcome the challenges associated with integrating AI into IoT systems and unlock their full potential, future research should focus on the following directions and areas for innovation:

i. Privacy-Preserving AI:
Develop novel techniques for privacy-preserving AI that enable secure and decentralized data processing while protecting user privacy and confidentiality. This includes research on federated learning, differential privacy, and homomorphic encryption to ensure data privacy in AI-driven IoT environments.

ii. Interoperability Standards:
Establish interoperability standards and protocols for seamless integration of AI algorithms with heterogeneous IoT devices and platforms. Research efforts should focus on developing open-source frameworks, APIs, and interoperability protocols to facilitate data exchange, communication, and interoperability across diverse IoT ecosystems.

iii. Ethical AI Governance:
Advance research in ethical AI governance to address algorithmic bias, fairness, transparency, and accountability in AI-driven IoT systems. This includes developing ethical guidelines, auditing frameworks, and governance mechanisms to promote responsible AI development and deployment while mitigating risks of discrimination and bias.

iv. Cybersecurity and Trustworthiness:
Enhance cybersecurity and trustworthiness of AI-driven IoT systems through research on secure-by-design principles, threat modeling, and adversarial robustness. This involves developing AI algorithms resilient to cyber attacks, as well as implementing robust security measures, such as secure bootstrapping, runtime monitoring, and anomaly detection, to protect IoT devices and data from malicious actors.

v. Data Quality and Reliability:
Research methodologies and algorithms for improving data quality and reliability in IoT environments, including data validation, error detection, and anomaly correction techniques. This involves developing automated data preprocessing pipelines and quality assurance frameworks to ensure accurate and reliable data inputs for AI models.


Int J Engg Mgmt Res 2025;15(2)43
Jain L, et al. Leveraging Artificial Intelligence

vi. Edge Intelligence and Edge Computing:
Explore edge intelligence and edge computing paradigms to enable distributed AI processing and decision-making at the network edge. Research efforts should focus on developing lightweight AI models, efficient inference algorithms, and edge computing architectures optimized for resource-constrained IoT devices, enabling real-time insights and decision-making closer to the data source.

vii. Human-Centric AI:
Foster research in human-centric AI to enhance user trust, acceptance, and usability of AI-driven IoT systems. This involves studying human-AI interaction, user experience design, and socio-technical factors to ensure AI technologies are aligned with human values, preferences, and needs.

viii. Sustainability and Environmental Impact:
Investigate the environmental impact of AI-driven IoT systems and develop sustainable solutions that minimize energy consumption, carbon emissions, and electronic waste. This includes research on energy-efficient AI algorithms, renewable energy-powered IoT devices, and lifecycle assessment methodologies for assessing the environmental footprint of AI technologies.

By focusing on these research directions and areas for innovation, researchers and practitioners can overcome the challenges associated with AI-driven IoT solutions and unlock their full potential to drive innovation, improve efficiency, and enhance the quality of life in diverse domains and industries.

6. Conclusion

The paper discussed various case studies and real-world examples to illustrate the practical implications and benefits of integrating AI into IoT ecosystems. Finally, it concludes with insights into future research directions and potential challenges in harnessing the full potential of AI-driven IoT solutions.

References

1. Sarker IH. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Comput Sci., 2(3), 1–21.

2. Sarker IH. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci., 2(6), 1–20.

3. Deng L, & Liu Y. (2018). Deep learning in natural language processing. Berlin: Springer.

4. IqbalH.Sarker. (2022). AI‑based modeling: Techniques, applications andresearch issues towardsautomation, intelligent andsmart systems. SN Computer Science, 3, 158.

5. Oxford Dictionaries. Definition of “Internet of Things”. https://www.lexico.com/en/definition/internet_of_things.

6. Sfar AR, Natalizio E, Challal Y, & Chtourou Z. (2018). A roadmap for security challenges in the internet of things. Digit Commun Netw., 4(1), 118–37.

7. Minoli D, Sohraby K, & Kouns J. (2017). IoT security (IoTSec) considerations, requirements, and architectures. In: 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA. https://doi.org/10.1109/ccnc.2017.7983271.

8. Hugo Latapie, Mina Gabriel, & Ramana Kompella. (2022). Hybrid AI for IoT actionable insights & real-time data-driven networks. Proceedings of Machine Learning Research, 192, pp. 127–131.

9. Kristinn R. Th´orisson. (2021). The ‘explanation hypothesis’ in general self-supervised learning. Proceedings of Machine Learning Research, International Workshop on Self-Supervised Learning, 159, pp. 5-27.

10. Hu, S., Yao, S., Jin, H., Zhao, Y., Hu, Y., Liu, X., Naghibolhosseini, N., Li, S., Kapoor, A., & Dron, W. et al. (2015). Data acquisition for real-time decision-making under freshness constraints. In: Proceedings of the IEEE Real-Time Systems Symposium, San Antonio, TX, USA.

11. Abdelzaher, T.F., Amin, M.T.A., Bar-Noy, A., Dron, W., Govindan, R., Hobbs, R.L., Hu, S., Kim, J., Lee, J., & Marcus, K. et al. (2017). Decision-driven execution: A distributed resource management paradigm for the age of IoT. In: Proceedings of the IEEE International Conference on Distributed Computing Systems, Atlanta, GA, USA.

12. Lee, J., Marcus, K., Abdelzaher, T., Amin, M.T.A., Bar-Noy, A., Dron, W., Govindan, R., Hobbs, R., Hu, S., & Kim, J.-E. et al. (2018). Athena: Towards decision-centric anticipatory sensor information delivery.J. Sens. Actuator Netw.7, 5.


Int J Engg Mgmt Res 2025;15(2)44
Jain L, et al. Leveraging Artificial Intelligence

13. Kim, J.E., Abdelzaher, T.F., Sha, L., Bar-Noy, A., Hobbs, R.L., & Dron, W. (2016). On maximizing quality of information for the internet of things: A real-time scheduling perspective (Invited Paper). In: Proceedings of the IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, Daegu, Korea, pp. 202–211.

14. Kim, J.E., Abdelzaher, T.F., Sha, L., Bar-Noy, A., & Hobbs, R. (2016). Sporadic decision-centric data scheduling with normally-off sensors. In: Proceedings of the IEEE Real-Time Systems Symposium, Porto, Portugal.

15. Kim, J., Abdelzaher, T.F., Sha, L., Bar-Noy, A., Hobbs, R.L., & Dron, W. (2019). Decision-driven scheduling. Real-Time Syst., 55, 514–551.

16. Pearson, Andrew. (2020). Personalisation the artificial intelligence way.

17. El-Sabagh, H.A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. Int. J. Educ. Technol. High. Educ., 18, 53.

18. Beldagli, B., & Adiguzel, T. (2010). Illustrating an ideal adaptive e-learning: A conceptual framework. Procedia Soc. Behav. Sci., 2, 5755–5761.

19. Ennouamani, S., & Mahani, Z. (2017). An overview of adaptive e-learning systems. In: Proceedings of the Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.

20. DinaFawzy, Sherin M.Moussa, & Nagwa L. (2023). An IoT-based resource utilization framework using data fusion for smart environments. Internet of Things, 21, 100645.

21. Aborokbah, M., & S Al-Mutairi. (2018). Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—a case analysis. Elsevier.

22. Adame, T., Bel, A., Bellalta, B., Barcelo, J., & Oliver, M. (2014). IEEE 802.11ah: The Wi-Fi Approach for M2M Communications.

23. Lysenko A., Sharma A., Boroevich A.K., & Tsunoda T. (2018). An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci. Alliance., 1, e201800098. DOI:10.26508/lsa.201800098.

24. Bali, Vikram, Mathur, Sonali, Sharma, & Vishnu Gaur, Dev. (2020). Smart traffic management system using IoT enabled technology. DOI: 10.1109/ICACCCN51052.2020.9362753.

25. Li Z, Shahidehpour M, Bahramirad S, & Khodaei A. (2017). Optimizing traffic signal settings in smart cities. IEEE Transactions on Smart Grid, 8(5), pp.2382-2393.

26. Krishnan S. (2016). Traffic flow optimization and vehicle safety in smart cities. International Journal of Innovative Research in Science, Engineering and Technology, 5(5), 7814-7820.

27. K.Sreelakshmi, S.Akarsh, R.Vinayakumar, & K.P.Soman. (2019). Capsule networks and visualization for segregation of plastic and non-plastic wastes. IEEE, pp. 631-636

28. L.Huiyu, O, O. G., & Kim, S. H. (2019). Automatic classifications and recognition for recycled garbage by utilizing deep learning technology. In: Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City, pp.1-4

29. T. Wuest, D. Weimer, C. Irgens, & K. D. Thoben. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research, pp. 23-45.

30. M. Baptista, S. Sankararaman, Ivo. P. de Medeiros, & C. Nascimento. (2017). Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Elsevier, Computers & Industrial Engineering.

31. A. Albers, B. Gladysz, T. Pinner, V. Butenko, & T. StArmlinger. (2016). Procedure for defining the system of objectives in the initial phase of an industry 4.0 project focusing on intelligent quality control systems. Procedia CIRP, 52, 262–267.

32. Bhat, S.A., & Huang, N.-F. (2021). Big data and AI revolution in precision agriculture: Survey and challenges. IEEE Access, 9, 110209–110222.

33. Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—A worldwide overview. Comput. Electron. Agric., 36, 113–132.

34. Yost, M.A., Kitchen, N.R., Sudduth, K.A., Sadler, E.J., Drummond, S.T., & Volkmann, M.R. (2017). Long-term impact of a precision agriculture system on grain crop production. Precis. Agric., 18, 823–842.


Int J Engg Mgmt Res 2025;15(2)45
Jain L, et al. Leveraging Artificial Intelligence

35. Khosla, R. (2010). Precision agriculture: Challenges and opportunities in a flat world. In: Proceedings of the 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane, QLD, Australia.

36. McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precis. Agric., 6, 7–23.

37. Bendre, M.R., Thool, R.C., & Thool, V.R. (2015). Big data in precision agriculture: Weather forecasting for future farming. In: Proceedings of the 2015 1st International Conference on Next Generation Computing Technologies (NGCT), Dehradun, India, pp. 744–750.

38. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big data in smart farming—A review. Agric. Syst., 153, 69–80.

39. R. Gupta, & R. Garg. (2015). Mobile applications modelling and security handling in cloud-centric Internet of Things. Second International Conference on Advances in Computing and Communication Engineering.

40. E. Pantano, & H. Timmermans. (2014). What is smart for retailing?. Procedia Environmental Sciences. 12th International Conference on Design and Decision Support Systems in Architecture and Urban Planning, pp. 101–107.

41. E. Kasznik. (2015). 5 ways the 'Internet of Things' transformed the vending machine. http://www.bizjournals.com/bizjournals/how-to/technology/2015/04/the-internet-of-things-is-transforming-vending.html?page=all.

42. J. Liu, Y. Gu, & S. Kamijo. (2015). Customer behavior recognition in retail store from surveillance camera. IEEE International Symposium on Multimedia.

43. W. Zhou, F. Alexandre-Bailly, & S. Piramuthu. (2016). Dynamic organizational learning with IoT and retail social network data. 49th Hawaii International Conference on System Sciences, IEEE, 1530-1605/16.

44. D. R. Gnimpieba, A. Nait-Sidi-Moh, D. Durand, & J. Fortina. (2015). Using internet of things technologies for a collaborative supply chain: Application to tracking of pallets and containers, Procedia Computer Science; International Workshop on Mobile Spatial Information Systems (MSIS 2015), 56, pp. 550–557.

45. http://www.comqi.com/internet-things-reinventing-retail/.

46. EunSu Lee, & Kambiz farahmand. (2010). Simulation of a base stock inventory management system integrated with transportation strategies of a logistics network. Proceedings of the 2010 Winter Simulation Conference.

47. O. Jukic, & I. Hedi. (2014). Inventory management system for water supply network. MIPRO, Opatija, Croatia.

48. Liling Xia. (2011). The design and implementation of distributed inventory management system based on the intranet architecture. Proceedings of the IEEE, International Conference on Information and Automation, Shenzen, China.

49. Zheng Li, & Li Jialing. (2006). Supply chain management, Beijing. China Central Radio and TV University Press, 3, pp. 129–132.

50. Xiaojun Jing, & Peng Tang. (2013). Research and design of the intelligent inventory management system based on RFID. Sixth International Symposium on Computational Intelligence and Design.

51. Ding Long-gang. (2011). Based on RFID, Wi-Fi, Bluetooth, ZigBee of things of electromagnetic compatibility and interference coordination. Internet of Things Technology, 1, 59–61.

Disclaimer / Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of Journals and/or the editor(s). Journals and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Int J Engg Mgmt Res 2025;15(2)46