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In agriculture, soil moisture wireless sensor networks (SMWSNs) are used to monitor the growth of crops for obtaining higher yields. The purpose of this paper is to improve the network efficiency of SMWSNs. Therefore, we propose a novel network efficiency evaluation mechanism which is suitable for soil moisture sensors and design a sensor target allocation model (STAM) for the actual agricultural situation. After that, a quantum clone elite genetic algorithm (QCEGA) is proposed; then, QCEGA is applied to optimize the STAM for obtaining optimal results. QCEGA uses the parallel mechanism of quantum computing to encode individuals, integrates the quantum revolving gate in quantum computing and the concept of cloning in biology to avoid the algorithm from falling into local optimum, and applies the elite strategy to speed up the convergence of the algorithm. Subsequently, the proposed algorithm is compared with simulated annealing (SA) and particle swarm optimization (PSO). Under the novel network efficiency evaluation mechanism, the simulation results demonstrate that the network efficiency based on QCEGA is higher than that of SA and PSO; what is more, QCEGA has better convergence performance. In comparison with traditional wireless sensor network efficiency evaluation approaches, our methods are more in line with the development of modern agriculture and can effectively improve the efficiency of SMWSNs, thus ensuring that crops can have a better growth condition.

Quantum Clone Elite Genetic Algorithm-Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks