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Evolutionary Method of Sink Node Path Planning Guided by the Hamiltonian of Quantum Annealing Algorithm

In order to solve the NP-hard problem of mobile sink path planning in wireless sensor networks (WSN) where the communication range is modeled as a circular area and overlaps with each other, this paper proposes a sink node path planning method guided by the Hamiltonian of quantum annealing algorithm (EMGH) to balance the energy consumption of wireless sensor networks, improve the network life and solve the energy hole problem. First of all, this paper analyzes the problem in theory, and transforms the characteristics of the problem into a mathematical model. The mathematical model considers that the sensor network itself is a travelling salesman problem (TSP), but also requires the shortest path of the mobile sink node. Then, the path of each node in the sensor network is iterated by quantum annealing algorithm, and an optimal TSP path is obtained by using quantum tunneling effect and quantum circuit to achieve parallelism. Finally, in the case of compressing the solution space, the moving path of the mobile sink node is quantum coded, and the Hamiltonian in the quantum annealing algorithm is taken as the guiding factor, and then the individual dimensions are moved, which improves the accuracy of the algorithm and speeds up the convergence speed of the algorithm. Finally, the feasibility of EMGH is verified by simulation experiments and comparison with other algorithms, which provides a reference for the optimization and improvement of path planning method.