Current - Issue

1. Thammawichai, M., & Kerrigan, E. C. (2018). Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors. Real-
Time Systems, 54(3), 132–165.
2. Sheikh, S. Z., & Pasha, M. A. (2018). Energy-efficient multicore scheduling for hard real-time systems: A survey. ACM Transactions
on Embedded Computing Systems, 17(6), 1–26.
3. Esmaili, A., Nazemi, M., & Pedram, M. (2019). Modeling processor idle times in MPSoC platforms to enable integrated dynamic power
management, DVFS, and task scheduling. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 38(12),
2338–2351.
4. Arabnejad, H., & Barbosa, J. G. (2020). A hybrid algorithm for task scheduling on heterogeneous multiprocessor embedded systems.
Applied Soft Computing, 91, 106202.
5. hen, J.-J., Shi, J., von der Brüggen, G., & Ueter, N. (2020). Scheduling of real-time tasks with multiple critical sections in multiprocessor
systems. ACM Transactions on Embedded Computing Systems, 19(5), 1–28.
6. Topcuoglu, H., Hariri, S., & Wu, M. Y. (2021). Performance-effective and low-complexity task scheduling for heterogeneous computing
systems. Journal of Parallel and Distributed Computing, 154, 57–70.
7. Muthukumar, S., Israni, R. K., Parekh, C., & Mishra, A. (2025). AI-enhanced energy management solutions for smart micro-grid
organism. In Advances in Smart Energy Systems. Springer.
8. Singh, R., Kumar, A., & Sharma, P. (2021). Machine learning-based resource allocation for heterogeneous multicore embedded systems.
Microprocessors and Microsystems, 83, 104032
9. Wang, L., Zhang, Y., & Li, X. (2021). Intelligent task scheduling for edge computing using deep reinforcement learning. Future
Generation Computer Systems, 121, 98–110.
10. Kumar, S., Verma, R., & Gupta, N. (2022). Dynamic workload balancing in multiprocessor embedded systems using machine learning.
Journal of Systems Architecture, 128, 102527.
11. Rotaeche, R., Ballesteros, A., & Proenza, J. (2023). Speeding task allocation search for reconfigurations in adaptive distributed embedded
systems using deep reinforcement learning. Sensors, 23(1), 548.
12. Sharma, D., & Patel, R. (2023). Intelligent communication-aware scheduling for heterogeneous multiprocessor architectures. IEEE Access,
11, 98654–98668.
13. Moulik, S., & Sharma, Y. (2024). FRESH: Fault-tolerant real-time scheduler for heterogeneous multiprocessor platforms. Future
Generation Computer Systems, 161, 214–225.
14. Israni, R. K., Nedunchezhian, T., Tanna, P., & Soni, S. (2025). AI-endorsed techniques for smart energy utilization: A holistic
review. In Digital Transformation and Sustainability of Business (pp. 271–274). CRC Press/Taylor & Francis.
15. Chakraborty, S., Sharma, Y., & Moulik, S. (2024). TREAFET: Temperature-aware real-time task scheduling for FinFET-based
multicores. ACM Transactions on Embedded Computing Systems, 23(4), 1–31
16. Partitioned scheduling with shared resources on imprecise mixed-criticality multiprocessor systems. (2025). IEEE Transactions on Computer-
Aided Design of Integrated Circuits and Systems, 44(1), 65–76.
17. Nair, P. P., & Devaraj, R. (2026). A survey of machine learning-driven task scheduling approaches for multiprocessor systems.
Journal of Systems Architecture, 171, 103628.
18. Sumathi, M. S., Prathiba, N., Raghunandan, G. H., & Savitha, M. M. (2026). Lightweight explainable energy-aware deep learning-based task
scheduling for microcontroller embedded systems. Discover Computing, 29, Article 416.
19. Saravanakrishnan, B., Skandan, G. R. J., & Reddy, B. N. K. (2026). Advanced task scheduling algorithm for enhanced energy efficiency
on multi-core embedded platforms. Computers & Electrical Engineering, 130, 110886.
20. Reddy, N. A., & Gokulnath, B. V. (2026). Design of an improved method for task scheduling using proximal policy optimization and graph
neural networks. IEEE Access.
21. Kumar, A., Singh, P., & Verma, S. (2025). Intelligent AI-based dynamic task mapping and resource optimization for heterogeneous
embedded multiprocessor systems. Journal of Supercomputing, 81(4), 1–24.