A Novel Hybrid Quantum-Classical Optimization Approach for Next-Generation Internet of Things Networks
Shahrzad Sedaghat
1
(
)
mehdi Azadimotlagh
2
(
Department of Computer Engineering, Jam Faculty of Engineering, Persian Gulf University, Bushehr, Iran
)
amir hosein jahangir
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.
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