A Novel Hybrid Quantum-Classical Optimization Approach for Next-Generation Internet of Things Networks
Subject Areas : ICTShahrzad Sedaghat 1 * , mehdi Azadimotlagh 2 , amir hosein jahangir 3
1 -
2 - Department of Computer Engineering, Jam Faculty of Engineering, Persian Gulf University, Bushehr, Iran
3 - Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
Keywords: Internet of Things, Fog Computing, Dynamic Task Allocation, Quantum Optimization, Latency, Energy Consumption, Quality of Service.,
Abstract :
The exponential growth of the Internet of Things (IoT) has introduced significant computational challenges, particularly in optimizing dynamic task allocation within hybrid edge, fog, and cloud networks. The combinatorial nature of this problem often leads to a combinatorial explosion when traditional algorithms are applied to large-scale scenarios, rendering them inefficient in finding optimal or near-optimal solutions. This research proposes a novel quantum-classical hybrid combinatorial optimization framework to address this multi-objective problem, considering conflicting criteria such as latency, energy consumption, and Quality of Service (QoS). The proposed model transforms the task allocation problem into a Quadratic Unconstrained Binary Optimization (QUBO) formulation and leverages the Quantum Approximate Optimization Algorithm (QAOA) for its solution. Numerical simulation results, obtained using the open-source Qiskit simulator, demonstrate that the proposed approach significantly outperforms prior methods based on quantum machine learning and traditional architectures. Comparative analysis reveals that the proposed approach consistently maintains the best solution quality with the gentlest degradation slope as the problem scales. Furthermore, the computational time of this method exhibits a much slower, near-linear growth, in contrast to the exponential growth experienced by traditional algorithms. These findings clearly establish the strategic superiority of quantum optimization for solving complex and large-scale optimization problems in next-generation IoT networks.
[1] H.-Z. Xu et al., “Quafu-Qcover: Explore combinatorial optimization problems on cloud-based quantum computers,” Chinese Phys. B, vol. 33, no. 5, p. 50302, 2024.
[2] A. Mahapatra, R. Pradhan, S. K. Majhi, and K. Mishra, “Quantum ML-Based Cooperative Task Orchestration in Dew-Assisted IoT Framework,” Arab. J. Sci. Eng., vol. 50, no. 15, pp. 11975–12002, 2025, doi: 10.1007/s13369-024-09661-8.
[3] M. I. Hossain, S. A. Sumon, H. M. Hasan, F. Akter, M. B. Badhon, and M. N. U. Islam, “Quantum-edge cloud computing: A future paradigm for iot applications,” arXiv Prepr. arXiv2405.04824, 2024.
[4] S. Akter, M. B. Badhon, M. K. I. Bhuiyan, H. M. Hasan, F. Akter, and M. N. U. Islam, “Quantum-edge cloud computing for iot: Bridging the gap between cloud, edge, and quantum technologies,” Edge, Quantum Technol. (September 29, 2024), 2024.
[5] B. Narang, H. Virwani, and N. Jain, “Comprehensive Review on the Synergies and Challenges of Quantum Computing, Cloud Computing, and Fog Computing in New Era,” in Proceedings of the 5th International Conference on Information Management \& Machine Intelligence, in ICIMMI ’23. New York, NY, USA: Association for Computing Machinery, 2024. doi: 10.1145/3647444.3652433.
[6] Y. Mahmoudi, N. Zioui, and H. Belbachir, “A new quantum-inspired clustering method for reducing energy consumption in IOT networks,” Internet of Things, vol. 20, p. 100622, 2022, doi: https://doi.org/10.1016/j.iot.2022.100622.
[7] S. Jannu et al., “Energy Efficient Quantum-Informed Ant Colony Optimization Algorithms for Industrial Internet of Things,” IEEE Trans. Artif. Intell., vol. 5, no. 3, pp. 1077–1086, 2024, doi: 10.1109/TAI.2022.3220186.
[8] K. Zanbouri, H. M. R. Al-Khafaji, N. J. Navimipour, and Ş. Yalçın, “A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm,” IEEE Multimed., vol. 30, no. 3, pp. 74–86, 2023, doi: 10.1109/MMUL.2023.3247522.
[9] T. Hasan et al., “Edge Caching in Fog‐Based Sensor Networks through Deep Learning‐Associated Quantum Computing Framework,” Comput. Intell. Neurosci., vol. 2022, no. 1, p. 6138434, 2022.
[10] T. A. Ahanger, F. Dahan, U. Tariq, and I. Ullah, “Quantum inspired task optimization for IoT edge fog computing environment,” Mathematics, vol. 11, no. 1, p. 156, 2022.
[11] M. Bhatia, S. K. Sood, and S. Kaur, “Quantum-based predictive fog scheduler for IoT applications,” Comput. Ind., vol. 111, pp. 51–67, 2019.
[12] S. N. Ghorpade, M. Zennaro, B. S. Chaudhari, R. A. Saeed, H. Alhumyani, and S. Abdel-Khalek, “Enhanced differential crossover and quantum particle swarm optimization for IoT applications,” IEEE Access, vol. 9, pp. 93831–93846, 2021.
[13] S. Khan, N. Younas, M. Alhussein, W. J. Khan, M. S. Anwar, and K. Aurangzeb, “Quantum Inspired Adaptive Resource Management Algorithm for Scalable and Energy Efficient Fog Computing in Internet of Things (IoT).,” Comput. Model. Eng. Sci., vol. 142, no. 3, 2025.
[14] M. Hayeri Khyavi, “Emerging technologies in future generations of high performance computing: introduction, taxonomy and future research directions,” Journal of Information and Communication Technology, vol. 55, no. 55, p. 26, 2023.
[15] M. Esnaashari, F. Ehsani Boshla, M. Dehyadegari, and B. Samadi, “High Performance Computing: Next Generation Requirements and Research Axes,” Journal of Information and Communication Technology, vol. 51, no. 51, p. 209, 2022.
[16] M. Azimzadeh, A. Rezaee, S. Jafarali Jassbi, and M. Esnaashari, “Community-Based Multi-Criteria Placement of Applications in the Fog Environment,” Journal of Information and Communication Technology, vol. 59, no. 59, p. 17, 2024.
[17] A. G. Delavar, R. Akraminejad, and S. Mozafari, “WSTMOS: A Method For Optimizing Throughput, Energy, And Latency In Cloud Workflow Scheduling,” Journal of Information and Communication Technology, vol. 57, no. 57, p. 62, 2023.
[18] Z. Abbasi, S. Fatahi, and M. J. Ershadi, “An approach to prioritize quality dimensions of based on cloud computing using Multiple Criteria Decision Making method,” Journal of Information and Communication Technology, vol. 53, no. 53, p. 224, 2023.