School of Electronic Communication and Electrical Engineering, Changsha University, Kaifu District, Changsha, China.
Hunan Engineering Technology Research Center of Optoelectronic Health Detection, Changsha, China.
Comput Intell Neurosci. 2022 Jul 31;2022:3692984. doi: 10.1155/2022/3692984. eCollection 2022.
In this work, we report performance optimization of a wireless sensor network (WSN) based on the plain silver surface plasmon resonance imaging (SPRi) sensor. At the sensor node level, we established the refractive index-thickness models for both gold and silver in the sensor and calculated the depth-width ratio (DWR) and penetration depth (PD) values of the sensor of different gold and silver thicknesses by the Jones transfer matrix and Kriging interpolation. We optimized the DWR and PD simultaneously by using the multi-objective optimization genetic algorithm (MOGA). In the following performance optimization of WSN, we simultaneously optimized the transmission success rate and information dimension with the number of nodes and transmission failure rate of the sensor node as variables by the same algorithm. By calculating the information dimension and the transmission success rate of each Pareto optimal solution, we obtained the number of nodes and transmission failure probability of the node available for practical deployment of WSN. The above results indicate that the Pareto optimal solution set obtained from MOGA can help to provide the best solution for the optimization of some certain performance parameters and also assist us in making the trade-off decision in the structure design and network deployment if optimal values of all the performance parameters can be obtained simultaneously.
在这项工作中，我们报告了基于普通银表面等离子体共振成像（SPRi）传感器的无线传感器网络（WSN）的性能优化。在传感器节点级别，我们在传感器中建立了金和银的折射率-厚度模型，并通过琼斯传输矩阵和克里金插值计算了不同金和银厚度的传感器的深度-宽度比（DWR）和穿透深度（PD）值。我们使用多目标优化遗传算法（MOGA）同时优化了 DWR 和 PD。在随后的 WSN 性能优化中，我们同时使用节点数量和传感器节点传输失败率作为变量，通过相同的算法对传输成功率和信息维数进行了优化。通过计算每个 Pareto 最优解的信息维数和传输成功率，我们获得了 WSN 实际部署中可用的节点数量和节点传输失败概率。上述结果表明，MOGA 获得的 Pareto 最优解集可以帮助提供某些特定性能参数优化的最佳解决方案，如果可以同时获得所有性能参数的最优值，也可以帮助我们在结构设计和网络部署方面做出权衡决策。
