ARCHIVES
Year 2026 · Volume 6 · Issue 5
Review Article
From Connected Devices to Trusted Intelligence: A Survey of Secure, Energy-Efficient and AI-Enabled Internet of Things Systems
Pankaj Kumar1
Rajesh Kumar Kaushal2
1 Department of Information Science and Engineering, Acharya Institute of Technology, Bengaluru, Karnataka, India. 2 Department of Electronics and Communication Engineering, Dr. T. Thimmaiah Institute of Technology, KGF, Karnataka, India.
Published Online: September-October 2026
Pages: 430-440
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260605047
References
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3. S. Pandikumar, K. V. Shaheena, T. Dinesh, and D. L. Bhuvaneshwari, “Upgrading industrial automation with 5G and IoT,” in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications,S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, ch. 5, pp. 57–77, doi: 10.48001/978-81-980647-5-2-5.
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8. V. A. Thakor, M. A. Razzaque, and M. R. A. Khandaker, “Lightweight cryptography algorithms for resource-constrained IoT devices: A review, comparison and research opportunities,” IEEE Access, vol. 9, pp. 28177–28193, 2021, doi: 10.1109/ACCESS.2021.3052867.
9. S. Pandikumar, A. Bostani, D. Sundaranarayana, I. Fakhriddin, D. Alimjon, U. Ilkhom, and A. Juraboyev, “AI-driven nonlinear optimization for knowledge extraction and pattern analysis in IoT-enabled data mining systems,” Results Nonlinear Anal., vol. 8, no. 3, pp. 36–46, 2025, doi: 10.31838/rna/2025.08.03.005.
10. K. Shafique, B. A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, “Internet of Things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios,” IEEE Access, vol. 8, pp. 23022–23040, 2020, doi: 10.1109/ACCESS.2020.2970118.
11. M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for Internet of Things (IoT) security,” IEEE Commun. Surveys Tuts., vol. 22, no. 3, pp. 1646–1685, 2020, doi: 10.1109/COMST.2020.2988293.
12. W. Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, and A. Ahmed, “Edge computing: A survey,” Future Gener. Comput. Syst., vol. 97, pp. 219–235, 2019, doi: 10.1016/j.future.2019.02.050.
13. N. Meenakshi, V. Pandimurugan, and L. Sathishkumar, “Optimal routing methodology to enhance the life time of sensor network,” Mater. Today: Proc., vol. 46, pp. 5894–5900, 2021.
14. M. S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli, and M. H. Rehmani, “Applications of blockchains in the Internet of Things: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 21, no. 2, pp. 1676–1717, 2019, doi: 0.1109/COMST.2018.2886932.
15. S. K. Lakshmanan, L. Shakkeera, and V. Pandimurugan, “Efficient auto key based encryption and decryption using GICK and GDCK methods,” in Proc. 3rd Int. Conf. Intell. Sustain. Syst. (ICISS), 2020.
16. S. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, and A. Y. Zomaya, “Edge intelligence: The confluence of edge computing and artificial intelligence,” IEEE Internet Things J., vol. 7, no. 8, pp. 7457–7469, 2020, doi: 10.1109/JIOT.2020.2984887.
17. V. Pandimurugan, A. Jain, and Y. Sinha, “IoT based face recognition for smart applications using machine learning,” in Proc. 3rd Int. Conf. Intell. Sustain. Syst. (ICISS), 2020.
18. S. Tuli, N. Basumatary, S. S. Gill, M. Kahani, R. C. Arya, G. S. Wander, and R. Buyya, “HealthFog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments,” Future Gener. Comput. Syst., vol. 104, pp. 187–200, 2020, doi: 10.1016/j.future.2019.10.043.
19. Margaret Mary, T., Ramanathan, G., Sangamithra, A., & Soumya, K. (2020). Design and enactment of heart attack deduction using IoT measuring device. Journal of Physics: Conference Series, 1427(1), 012014. https://doi.org/10.1088/1742-6596/1427/1/012014
20. M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
21. V. Pandimurugan, R. Mandviya, A. Gadgil, K. Prakhar, and A. Datar, “IoT based smart beekeeping monitoring system for beekeepers in India,” in Proc. 4th Int. Conf. Comput. Commun. Technol. (ICCCT), 2021.
22. M. M. Dhanvijay and S. C. Patil, “Internet of Things: A survey of enabling technologies in healthcare and its applications,” Comput. Netw., vol. 153, pp. 113–131, 2019, doi: 10.1016/j.comnet.2019.03.006.
23. T. K. S. Rathish Babu, S. Pandikumar, K. Sathishkumar, Z. Kurbonov, A. Thahaseen A., and Md. Z. Rahman AMJ,“Blockchain-enhanced secure RF link transmission in antenna-constrained industrial IoT systems,” Natl. J. Antennas Propag., vol. 7, no. 1, pp. 270–279, 2025, doi: 10.31838/NJAP/07.01.30.
24. W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Netw., vol. 34, no. 3, pp. 134–142, 2020, doi: 10.1109/MNET.001.1900287.
25. S. Pandikumar, A. Bostani, A. Srinivasan, G. Menaka, J. Chandhini, R. Sabitha, and K. Sathishkumar, “Fuzzy logic-driven nonlinear optimization for resource allocation in 6G networks,” Results Nonlinear Anal., vol. 8, no. 2,pp. 239–252, 2025, doi: 10.31838/rna/2025.08.02.019.
26. M. H. A. Hussain, B. Mokhtar, and M. R. M. Rizk, “A comparative survey on LEACH successors clustering algorithms for energy-efficient longevity WSNs,” Egypt. Informat. J., vol. 26, Art. no. 100477, 2024.
27. C. Jamunadevi, N. Rajasekaran, S. Pandikumar, R. Raja, and K. P. Narendra Prasanth, “Energy performance forecasting in buildings using machine learning,” in Proc. Int. Conf. Commun., Comput. Internet Things (IC3IoT), Chennai, India, 2024, pp. 1–6, doi: 10.1109/IC3IoT60841.2024.10550233.
28. M. S. Turan et al., “Status report on the final round of the NIST lightweight cryptography standardization process,” Nat. Inst. Standards Technol., Gaithersburg, MD, USA, NIST IR 8454, 2023, doi: 10.6028/NIST.IR.8454.
29. C. Dobraunig, M. Eichlseder, F. Mendel, and M. Schläffer, “Ascon v1.2: Lightweight authenticated encryption and hashing,” J. Cryptol., vol. 34, no. 3, Art. no. 33, 2021, doi: 10.1007/s00145-021-09398-9.
30. M. A. Almaiah, S. Yelisetti, L. Arya, N. K. Babu Christopher, K. Kaliappan et al., “A novel approach for improving the security of IoT–medical data systems using an enhanced dynamic Bayesian network,” Electronics, vol. 12, no. 20, p. 4316, 2023, doi: 10.3390/electronics12204316.
31. T. D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, and A.-R. Sadeghi, “DÏoT: A federated self-learning anomaly detection system for IoT,” in Proc. IEEE 39th Int. Conf. Distrib. Comput. Syst. (ICDCS), 2019,pp. 756–767, doi: 10.1109/ICDCS.2019.00080.
32. H. Bangali, P. Rodrigues, V. Pandimurugan, S. Rajasoundaran et al., “Prediction of middle box-based attacks in Internet of Healthcare Things using ranking subsets and convolutional neural network,” Wireless Netw., vol. 30, no. 3, pp. 1493–1511, 2024.
33. F. Cui, Y. Yue, Y. Zhang, Z. Zhang, and H. S. Zhou, “Advancing biosensors with machine learning,” ACS Sens., vol. 5, no. 11, pp. 3346–3364, 2020, doi: 10.1021/acssensors.0c01424.
34. D. S. Pandikumar, B. Sapaev, K. Jafarov, J. Saidmuradova, G. Bekhruz, S. Elmurodov, and S. Muratova, “An artificial intelligence and genetic algorithm optimized biosensor system for real-time detection of environmental toxins and their biomedical impact,” Nat. Eng. Sci., vol. 10, no. 3, pp. 561–574, 2025, doi: 10.28978/nesciences.1811144.
35. D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for Internet of Things: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 23, no. 3, pp. 1622–1658, 2021, doi: 10.1109/COMST.2021.3075439.
36. S. Pandikumar, V. Pandimurugan, A. Rama Prasath, S. Karattil, S. C. B. Jaganathan, and P. Vigneshwaran, “A novel framework for differential privacy based federated continual learning for dynamic medical data analysis,” SN Comput. Sci., vol. 7, no. 6, p. 664, 2026, doi: 10.1007/s42979-026-05245-5.
37. P. Kumar et al., “A zero-knowledge proof framework for securing federated learning in healthcare using blockchain technology,” ITM Web of Conferences, vol. 87, Art. no. 01020, 2026, doi: 10.1051/itmconf/20268701020.
2. L. Chettri and R. Bera, “A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems,” IEEE Internet Things J., vol. 7, no. 1, pp. 16–32, 2020, doi: 10.1109/JIOT.2019.2948888.
3. S. Pandikumar, K. V. Shaheena, T. Dinesh, and D. L. Bhuvaneshwari, “Upgrading industrial automation with 5G and IoT,” in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications,S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, ch. 5, pp. 57–77, doi: 10.48001/978-81-980647-5-2-5.
4. D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, D. Niyato, O. Dobre, and H. V. Poor, “6G Internet of Things: A comprehensive survey,” IEEE Internet Things J., vol. 9, no. 1, pp. 359–383, 2022, doi: 10.1109/JIOT.2021.3103320
5. V. Rajaram, V. Pandimurugan, S. Rajasoundaran, P. Rodrigues et al., “Enriched energy optimized LEACH protocol for efficient data transmission in wireless sensor network,” Wireless Netw., vol. 31, no. 1, pp. 825–840, 2025.
6. N. Neshenko, E. Bou-Harb, J. Crichigno, G. Kaddoum, and N. Ghani, “Demystifying IoT security: An exhaustive survey on IoT vulnerabilities and a first empirical look on Internet-scale IoT exploitations,” IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2702–2733, 2019, doi: 10.1109/COMST.2019.2910750.
7. Divya, K. S., Margaret Mary, T., Chakrabarti, S., Prakash, V. S., & Chitra, K. S. (2024). Optimizing lightweight cryptographic protocols for secure communication in Internet of Things (IoT) devices. In 2024 IEEE 5th International Conference on Communication, Computing and Industry 6.0 (C2I6). IEEE. https://doi.org/10.1109/C2I663243.2024.10894917
8. V. A. Thakor, M. A. Razzaque, and M. R. A. Khandaker, “Lightweight cryptography algorithms for resource-constrained IoT devices: A review, comparison and research opportunities,” IEEE Access, vol. 9, pp. 28177–28193, 2021, doi: 10.1109/ACCESS.2021.3052867.
9. S. Pandikumar, A. Bostani, D. Sundaranarayana, I. Fakhriddin, D. Alimjon, U. Ilkhom, and A. Juraboyev, “AI-driven nonlinear optimization for knowledge extraction and pattern analysis in IoT-enabled data mining systems,” Results Nonlinear Anal., vol. 8, no. 3, pp. 36–46, 2025, doi: 10.31838/rna/2025.08.03.005.
10. K. Shafique, B. A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, “Internet of Things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios,” IEEE Access, vol. 8, pp. 23022–23040, 2020, doi: 10.1109/ACCESS.2020.2970118.
11. M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for Internet of Things (IoT) security,” IEEE Commun. Surveys Tuts., vol. 22, no. 3, pp. 1646–1685, 2020, doi: 10.1109/COMST.2020.2988293.
12. W. Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, and A. Ahmed, “Edge computing: A survey,” Future Gener. Comput. Syst., vol. 97, pp. 219–235, 2019, doi: 10.1016/j.future.2019.02.050.
13. N. Meenakshi, V. Pandimurugan, and L. Sathishkumar, “Optimal routing methodology to enhance the life time of sensor network,” Mater. Today: Proc., vol. 46, pp. 5894–5900, 2021.
14. M. S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli, and M. H. Rehmani, “Applications of blockchains in the Internet of Things: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 21, no. 2, pp. 1676–1717, 2019, doi: 0.1109/COMST.2018.2886932.
15. S. K. Lakshmanan, L. Shakkeera, and V. Pandimurugan, “Efficient auto key based encryption and decryption using GICK and GDCK methods,” in Proc. 3rd Int. Conf. Intell. Sustain. Syst. (ICISS), 2020.
16. S. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, and A. Y. Zomaya, “Edge intelligence: The confluence of edge computing and artificial intelligence,” IEEE Internet Things J., vol. 7, no. 8, pp. 7457–7469, 2020, doi: 10.1109/JIOT.2020.2984887.
17. V. Pandimurugan, A. Jain, and Y. Sinha, “IoT based face recognition for smart applications using machine learning,” in Proc. 3rd Int. Conf. Intell. Sustain. Syst. (ICISS), 2020.
18. S. Tuli, N. Basumatary, S. S. Gill, M. Kahani, R. C. Arya, G. S. Wander, and R. Buyya, “HealthFog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments,” Future Gener. Comput. Syst., vol. 104, pp. 187–200, 2020, doi: 10.1016/j.future.2019.10.043.
19. Margaret Mary, T., Ramanathan, G., Sangamithra, A., & Soumya, K. (2020). Design and enactment of heart attack deduction using IoT measuring device. Journal of Physics: Conference Series, 1427(1), 012014. https://doi.org/10.1088/1742-6596/1427/1/012014
20. M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
21. V. Pandimurugan, R. Mandviya, A. Gadgil, K. Prakhar, and A. Datar, “IoT based smart beekeeping monitoring system for beekeepers in India,” in Proc. 4th Int. Conf. Comput. Commun. Technol. (ICCCT), 2021.
22. M. M. Dhanvijay and S. C. Patil, “Internet of Things: A survey of enabling technologies in healthcare and its applications,” Comput. Netw., vol. 153, pp. 113–131, 2019, doi: 10.1016/j.comnet.2019.03.006.
23. T. K. S. Rathish Babu, S. Pandikumar, K. Sathishkumar, Z. Kurbonov, A. Thahaseen A., and Md. Z. Rahman AMJ,“Blockchain-enhanced secure RF link transmission in antenna-constrained industrial IoT systems,” Natl. J. Antennas Propag., vol. 7, no. 1, pp. 270–279, 2025, doi: 10.31838/NJAP/07.01.30.
24. W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Netw., vol. 34, no. 3, pp. 134–142, 2020, doi: 10.1109/MNET.001.1900287.
25. S. Pandikumar, A. Bostani, A. Srinivasan, G. Menaka, J. Chandhini, R. Sabitha, and K. Sathishkumar, “Fuzzy logic-driven nonlinear optimization for resource allocation in 6G networks,” Results Nonlinear Anal., vol. 8, no. 2,pp. 239–252, 2025, doi: 10.31838/rna/2025.08.02.019.
26. M. H. A. Hussain, B. Mokhtar, and M. R. M. Rizk, “A comparative survey on LEACH successors clustering algorithms for energy-efficient longevity WSNs,” Egypt. Informat. J., vol. 26, Art. no. 100477, 2024.
27. C. Jamunadevi, N. Rajasekaran, S. Pandikumar, R. Raja, and K. P. Narendra Prasanth, “Energy performance forecasting in buildings using machine learning,” in Proc. Int. Conf. Commun., Comput. Internet Things (IC3IoT), Chennai, India, 2024, pp. 1–6, doi: 10.1109/IC3IoT60841.2024.10550233.
28. M. S. Turan et al., “Status report on the final round of the NIST lightweight cryptography standardization process,” Nat. Inst. Standards Technol., Gaithersburg, MD, USA, NIST IR 8454, 2023, doi: 10.6028/NIST.IR.8454.
29. C. Dobraunig, M. Eichlseder, F. Mendel, and M. Schläffer, “Ascon v1.2: Lightweight authenticated encryption and hashing,” J. Cryptol., vol. 34, no. 3, Art. no. 33, 2021, doi: 10.1007/s00145-021-09398-9.
30. M. A. Almaiah, S. Yelisetti, L. Arya, N. K. Babu Christopher, K. Kaliappan et al., “A novel approach for improving the security of IoT–medical data systems using an enhanced dynamic Bayesian network,” Electronics, vol. 12, no. 20, p. 4316, 2023, doi: 10.3390/electronics12204316.
31. T. D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, and A.-R. Sadeghi, “DÏoT: A federated self-learning anomaly detection system for IoT,” in Proc. IEEE 39th Int. Conf. Distrib. Comput. Syst. (ICDCS), 2019,pp. 756–767, doi: 10.1109/ICDCS.2019.00080.
32. H. Bangali, P. Rodrigues, V. Pandimurugan, S. Rajasoundaran et al., “Prediction of middle box-based attacks in Internet of Healthcare Things using ranking subsets and convolutional neural network,” Wireless Netw., vol. 30, no. 3, pp. 1493–1511, 2024.
33. F. Cui, Y. Yue, Y. Zhang, Z. Zhang, and H. S. Zhou, “Advancing biosensors with machine learning,” ACS Sens., vol. 5, no. 11, pp. 3346–3364, 2020, doi: 10.1021/acssensors.0c01424.
34. D. S. Pandikumar, B. Sapaev, K. Jafarov, J. Saidmuradova, G. Bekhruz, S. Elmurodov, and S. Muratova, “An artificial intelligence and genetic algorithm optimized biosensor system for real-time detection of environmental toxins and their biomedical impact,” Nat. Eng. Sci., vol. 10, no. 3, pp. 561–574, 2025, doi: 10.28978/nesciences.1811144.
35. D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for Internet of Things: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 23, no. 3, pp. 1622–1658, 2021, doi: 10.1109/COMST.2021.3075439.
36. S. Pandikumar, V. Pandimurugan, A. Rama Prasath, S. Karattil, S. C. B. Jaganathan, and P. Vigneshwaran, “A novel framework for differential privacy based federated continual learning for dynamic medical data analysis,” SN Comput. Sci., vol. 7, no. 6, p. 664, 2026, doi: 10.1007/s42979-026-05245-5.
37. P. Kumar et al., “A zero-knowledge proof framework for securing federated learning in healthcare using blockchain technology,” ITM Web of Conferences, vol. 87, Art. no. 01020, 2026, doi: 10.1051/itmconf/20268701020.
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