Leveraging Artificial Intelligence for Enhanced Internet of Things Applications

Authors

  • Leena Jain Department of Computer Science, Saroop Rani Govt. College for Women, Amritsar, Punjab, India
  • Sushil Bhardwaj Department of Computer Applications, RIMT University, Mandi Gobindgarh, Punjab, India
  • Maalti Puri Department of Electronics and Communication Engineering, Khalsa College of Engineering and Technology, Amritsar, Punjab, India
  • Mandeep Kaur Sandhu Department of Computer Science, Guru Nanak Dev University College, Pathankot, Punjab, India

DOI:

https://doi.org/10.5281/zenodo.15314664

Keywords:

Artificial Intelligence, Internet of Things, Machine Learning

Abstract

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.

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References

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

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

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

Iqbal H. Sarker. (2022). AI based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3, 158.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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.

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

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

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

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.

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

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.

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.

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

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

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

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.

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.

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

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

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.

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.

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

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.

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

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.

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.

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.

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

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.

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.

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

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.

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

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.

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

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.

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.

Published

2025-04-26
CITATION
DOI: 10.5281/zenodo.15314664
Published: 2025-04-26

How to Cite

Jain, L., Bhardwaj, S., Puri, M., & Sandhu, M. K. (2025). Leveraging Artificial Intelligence for Enhanced Internet of Things Applications. International Journal of Engineering and Management Research, 15(2), 40–46. https://doi.org/10.5281/zenodo.15314664

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