ARCHIVES
Year 2026 · Volume 6 · Issue 5
Review Article
Edge Intelligence and Adaptive Resource Management for Smart Services: Placement, Scheduling and Dispatch From Devices to the Cloud
Kavya V1
Kavana K Kulkarani2
1 2 Teaching Assistant, Department of Computer Science and Engineering, Acharya Institute of Technology, Bengaluru, Karnataka, India.
Published Online: September-October 2026
Pages: 416-424
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260605045
References
1. A. Sangamithra, T. Margaret Mary, and G. Clinton, "Overview of edge computing and its exploring characteristics," in Cases on Edge Computing and Analytics. Hershey, PA, USA: IGI Global, 2021, pp. 73–94, doi: 10.4018/978-1-7998-4873-8.ch004.
2. J. Chen and X. Ran, "Deep learning with edge computing: A review," Proc. IEEE, vol. 107, no. 8, pp. 1655–1674, 2019, doi: 10.1109/JPROC.2019.2921977.
3. .K. Soumya, T. Margaret Mary, and G. Clinton, "Innovative concepts and techniques of data analytics in edge computing paradigms," in Research Anthology on Edge Computing Protocols, Applications, and Integration. Hershey, PA, USA: IGI Global, 2022, pp. 182–196, doi: 10.4018/978-1-7998-4873-8.ch007.
4. Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, "Edge intelligence: Paving the last mile of artificial intelligence with edge computing," Proc. IEEE, vol. 107, no. 8, pp. 1738–1762, 2019, doi: 10.1109/JPROC.2019.2918951.
5. P. Savarimalai, M. Chellappan, M. S. Nidhya, M. M. Thomas, S. Nallathambi, and M. Selvaraj, "A context-aware intelligent scheduling framework for modern operating systems using hybrid machine learning models," Journal of Computational and Cognitive Engineering., vol. 5, no. 2, pp. 321–332, 2026, doi: 10.47852/bonviewJCCE62026664.
6. .H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, and M. Alizadeh, "Learning scheduling algorithms for data processing clusters," in Proc. ACM SIGCOMM, 2019, pp. 270–288, doi: 10.1145/3341302.3342080.
7. S. Pandikumar, S. A. Pallavi, V. M, M. Santhalakshmi, and M. M. T, "Adaptive driver–passenger matching in on-demand mobility platforms using deep Q-networks," in Proc. Int. Conf. Sustainable Communication Networks and Application (ICSCN), Theni, India,
2025, pp. 261–266, doi: 10.1109/ICSCN67106.2025.11308611.
8. Z. Qin, H. Zhu, and J. Ye, "Reinforcement learning for ridesharing: An extended survey," Transp. Res. Part C Emerg. Technol., vol. 144, p. 103852, 2022, doi: 10.1016/j.trc.2022.103852.
9. 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.
10. P. P. Ray, "A review on TinyML: State-of-the-art and prospects," J. King Saud Univ. Comput. Inf. Sci., vol. 34, no. 4, pp. 1595–1623, 2022, doi: 10.1016/j.jksuci.2021.11.019.
11. .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.
12. [12] .J. Deng, J. Guo, N. Xue, and S. Zafeiriou, "ArcFace: Additive angular margin loss for deep face recognition," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp. 4690–4699, doi: 10.1109/CVPR.2019.00482.
13. V. Pandimurugan, A. Jain, and Y. Sinha, "IoT based face recognition for smart applications using machine learning," in Proc. 3rd Int. Conf. Intelligent Sustainable Systems (ICISS), 2020, pp. 1263–1266, doi: 10.1109/ICISS49785.2020.9316089.
14. .K. B. S. Prakash, M. Sudharsan, G. Meenakashi, and T. Margaret Mary, "A road map for age and gender classification using EL-methods," in Proc. 2nd Int. Conf. Recent Advances in Information Technology for Sustainable Development (ICRAIS), 2024, doi: 10.1109/ICRAIS62903.2024.10811716.
15. S. Li and W. Deng, "Deep facial expression recognition: A survey," IEEE Trans. Affect. Comput., vol. 13, no. 3, pp. 1195–1215, 2022, doi: 10.1109/TAFFC.2020.2981446.
16. V. Pandimurugan, A. Singh, and A. Tiwari, "Facial emotion recognition for students using machine learning," in Proc. Int. Conf. Computer Communication and Informatics (ICCCI), 2023, pp. 1–4, doi: 10.1109/ICCCI56745.2023.10128425.
17. Y. A. Qadri, A. Nauman, Y. B. Zikria, A. V. Vasilakos, and S. W. Kim, "The future of healthcare Internet of Things: A survey of emerging technologies," IEEE Commun. Surveys Tuts., vol. 22, no. 2, pp. 1121–1167, 2020, doi: 10.1109/COMST.2020.2973314.
18. .N. Arunkumar, V. Pandimurugan, M. S. Hema, H. Azath, S. Hariharasitaraman, M. Thilagaraj, and P. Govindan, "A versatile and ubiquitous IoT-based smart metabolic and immune monitoring system," Comput. Intell. Neurosci., vol. 2022, Art. no. 9441357, 2022, doi: 10.1155/2022/9441357.
19. .M. Ndiaye, S. S. Oyewobi, A. M. Abu-Mahfouz, G. P. Hancke, A. M. Kurien, and K. Djouani, "IoT in the wake of COVID-19: A survey on contributions, challenges and evolution," IEEE Access, vol. 8, pp. 186821–186839, 2020, doi: 10.1109/ACCESS.2020.3030090.
20. L. Sathishkumar, M. L. Pandala, H. Azath, M. Eswaran, V. Pandimurugan, R. Mandviya, and V. Ramakrishna, "A design using the IoT to prevent future pandemics," in Proc. 8th Int. Conf. Advanced Computing and Communication Systems (ICACCS), 2022, pp. 938–942, doi: 10.1109/ICACCS54159.2022.9785347.
21. Pandikumar, S., Jakaraddi, H. R., & Sevugapandi, N. (2025). Impact of AI in the Design of Operating System: An Overview. QTanalytics Publication (Books), 64–73. https://doi.org/10.48001/ 978-81-980647-6-9-7.
22. H. Mao et al., "Park: An open platform for learning-augmented computer systems," in Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 32, 2019.
23. .X. Wang, S. Wang, X. Liang, D. Zhao, J. Huang, X. Xu, B. Dai, and Q. Miao, "Deep reinforcement learning: A survey," IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 4, pp. 5064–5078, 2024, doi: 10.1109/TNNLS.2022.3207346.
24. X. Tang, Z. Qin, F. Zhang, Z. Wang, Z. Xu, Y. Ma, H. Zhu, and J. Ye, "A deep value-network based approach for multi-driver order dispatching," in Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2019, pp. 1780–1790, doi: 10.1145/3292500.3330724.
25. S. Pandikumar, B. Maram, C. Menaka, M. M. T, and M. K. Rajan, "Policy gradient-based reinforcement learning for driver-passenger matching in ride-hailing platforms," in Proc. Int. Conf. Visual Analytics and Data Visualization (ICVADV), Tirunelveli, India, 2026, pp. 878–884, doi: 10.1109/ICVADV67766.2026.11470467.
2. J. Chen and X. Ran, "Deep learning with edge computing: A review," Proc. IEEE, vol. 107, no. 8, pp. 1655–1674, 2019, doi: 10.1109/JPROC.2019.2921977.
3. .K. Soumya, T. Margaret Mary, and G. Clinton, "Innovative concepts and techniques of data analytics in edge computing paradigms," in Research Anthology on Edge Computing Protocols, Applications, and Integration. Hershey, PA, USA: IGI Global, 2022, pp. 182–196, doi: 10.4018/978-1-7998-4873-8.ch007.
4. Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, "Edge intelligence: Paving the last mile of artificial intelligence with edge computing," Proc. IEEE, vol. 107, no. 8, pp. 1738–1762, 2019, doi: 10.1109/JPROC.2019.2918951.
5. P. Savarimalai, M. Chellappan, M. S. Nidhya, M. M. Thomas, S. Nallathambi, and M. Selvaraj, "A context-aware intelligent scheduling framework for modern operating systems using hybrid machine learning models," Journal of Computational and Cognitive Engineering., vol. 5, no. 2, pp. 321–332, 2026, doi: 10.47852/bonviewJCCE62026664.
6. .H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, and M. Alizadeh, "Learning scheduling algorithms for data processing clusters," in Proc. ACM SIGCOMM, 2019, pp. 270–288, doi: 10.1145/3341302.3342080.
7. S. Pandikumar, S. A. Pallavi, V. M, M. Santhalakshmi, and M. M. T, "Adaptive driver–passenger matching in on-demand mobility platforms using deep Q-networks," in Proc. Int. Conf. Sustainable Communication Networks and Application (ICSCN), Theni, India,
2025, pp. 261–266, doi: 10.1109/ICSCN67106.2025.11308611.
8. Z. Qin, H. Zhu, and J. Ye, "Reinforcement learning for ridesharing: An extended survey," Transp. Res. Part C Emerg. Technol., vol. 144, p. 103852, 2022, doi: 10.1016/j.trc.2022.103852.
9. 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.
10. P. P. Ray, "A review on TinyML: State-of-the-art and prospects," J. King Saud Univ. Comput. Inf. Sci., vol. 34, no. 4, pp. 1595–1623, 2022, doi: 10.1016/j.jksuci.2021.11.019.
11. .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.
12. [12] .J. Deng, J. Guo, N. Xue, and S. Zafeiriou, "ArcFace: Additive angular margin loss for deep face recognition," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp. 4690–4699, doi: 10.1109/CVPR.2019.00482.
13. V. Pandimurugan, A. Jain, and Y. Sinha, "IoT based face recognition for smart applications using machine learning," in Proc. 3rd Int. Conf. Intelligent Sustainable Systems (ICISS), 2020, pp. 1263–1266, doi: 10.1109/ICISS49785.2020.9316089.
14. .K. B. S. Prakash, M. Sudharsan, G. Meenakashi, and T. Margaret Mary, "A road map for age and gender classification using EL-methods," in Proc. 2nd Int. Conf. Recent Advances in Information Technology for Sustainable Development (ICRAIS), 2024, doi: 10.1109/ICRAIS62903.2024.10811716.
15. S. Li and W. Deng, "Deep facial expression recognition: A survey," IEEE Trans. Affect. Comput., vol. 13, no. 3, pp. 1195–1215, 2022, doi: 10.1109/TAFFC.2020.2981446.
16. V. Pandimurugan, A. Singh, and A. Tiwari, "Facial emotion recognition for students using machine learning," in Proc. Int. Conf. Computer Communication and Informatics (ICCCI), 2023, pp. 1–4, doi: 10.1109/ICCCI56745.2023.10128425.
17. Y. A. Qadri, A. Nauman, Y. B. Zikria, A. V. Vasilakos, and S. W. Kim, "The future of healthcare Internet of Things: A survey of emerging technologies," IEEE Commun. Surveys Tuts., vol. 22, no. 2, pp. 1121–1167, 2020, doi: 10.1109/COMST.2020.2973314.
18. .N. Arunkumar, V. Pandimurugan, M. S. Hema, H. Azath, S. Hariharasitaraman, M. Thilagaraj, and P. Govindan, "A versatile and ubiquitous IoT-based smart metabolic and immune monitoring system," Comput. Intell. Neurosci., vol. 2022, Art. no. 9441357, 2022, doi: 10.1155/2022/9441357.
19. .M. Ndiaye, S. S. Oyewobi, A. M. Abu-Mahfouz, G. P. Hancke, A. M. Kurien, and K. Djouani, "IoT in the wake of COVID-19: A survey on contributions, challenges and evolution," IEEE Access, vol. 8, pp. 186821–186839, 2020, doi: 10.1109/ACCESS.2020.3030090.
20. L. Sathishkumar, M. L. Pandala, H. Azath, M. Eswaran, V. Pandimurugan, R. Mandviya, and V. Ramakrishna, "A design using the IoT to prevent future pandemics," in Proc. 8th Int. Conf. Advanced Computing and Communication Systems (ICACCS), 2022, pp. 938–942, doi: 10.1109/ICACCS54159.2022.9785347.
21. Pandikumar, S., Jakaraddi, H. R., & Sevugapandi, N. (2025). Impact of AI in the Design of Operating System: An Overview. QTanalytics Publication (Books), 64–73. https://doi.org/10.48001/ 978-81-980647-6-9-7.
22. H. Mao et al., "Park: An open platform for learning-augmented computer systems," in Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 32, 2019.
23. .X. Wang, S. Wang, X. Liang, D. Zhao, J. Huang, X. Xu, B. Dai, and Q. Miao, "Deep reinforcement learning: A survey," IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 4, pp. 5064–5078, 2024, doi: 10.1109/TNNLS.2022.3207346.
24. X. Tang, Z. Qin, F. Zhang, Z. Wang, Z. Xu, Y. Ma, H. Zhu, and J. Ye, "A deep value-network based approach for multi-driver order dispatching," in Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2019, pp. 1780–1790, doi: 10.1145/3292500.3330724.
25. S. Pandikumar, B. Maram, C. Menaka, M. M. T, and M. K. Rajan, "Policy gradient-based reinforcement learning for driver-passenger matching in ride-hailing platforms," in Proc. Int. Conf. Visual Analytics and Data Visualization (ICVADV), Tirunelveli, India, 2026, pp. 878–884, doi: 10.1109/ICVADV67766.2026.11470467.
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