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

Abstract

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.

Implementation of Quantum Annealing: A Systematic Review

“…It was discovered that 75 combinations of metrics and state-of-theart methods were mapped and compared out of which 60 (80%) stated that the QA performed better, 7 (9.33%) stated they have equivalent performance, and 8 (10.67%) showed that QA has lower performance. The most widely used stateof-the-art method for comparison was found to be simulated annealing as indicated by 21 (42%) of the 50 research which include [130], [149], [152], [154], [161], [168], [171], [189], [212], [217], [218], [223], [229], [238], [239], [242], [243], [246], [249], [251], [252]. This is due to the fact that QA is analogous to simulated annealing (classical approach) but in substitution of thermal activation by quantum tunneling.…”

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm

“…The proposed method needs to be implemented with the help of commercially available quantum annealing hardware and a purely classical solver, "qbsolv" released by D-Wave. In [29], the mobile sink node collects data from all nodes within the communication range along the planned path and returns to the initial location after completing one round. The problem of finding the shortest path for a mobile sink node is essentially a traveling salesman problem (TSP).…”

“…Different from the simulated annealing algorithm which jumps over the barrier to eliminate the local optimum, the quantum annealing algorithm introduces a penetration field into the quantum system and generates quantum wave motion to penetrate the potential barrier itself, then reaches the lowest energy state [29]. In the early stage of annealing, the kinetic energy term is relatively large, providing a large disturbance that can fully traverse the entire solution space.…”

“…To overcome this problem, the quantum annealing (QA) algorithm was developed by replacing the temperature parameter of the SA algorithm with the intensity of the tunneling field. The quantum wave motion is generated through the mechanism of quantum tunneling, enabling the attainment of an optimal point across the potential barrier [29]. WSNs consist of a multitude of nodes and complex network topologies, and the network environment changes constantly over time.…”