In recent years, research in wireless sensor networks and their application in health care and environmental monitoring have attracted significant interest. In such applications, the accuracy of the distance estimation between a patient and a beacon node is crucial for determining patient location. In this study, the distance between the mobile node (carried by the Alzheimer’s patient) and the beacon node was measured using the received signal strength indicator (RSSI) with ZigBee technology in indoor environments. The distance estimation was determined by two path loss models: a log-normal shadowing model (LNSM) and a derived model using a polynomial function (the POLYN function) obtained with the MATLAB curve fitting tool. Next, particle swarm optimization (PSO) was merged with the polynomial function (called the PSO–POLYN function) to obtain the optimal coefficient values for the POLYN function. The resulting path loss model can improve the distance error between a patient with Alzheimer’s disease and a beacon node. The results revealed that the merging of the PSO–POLYN model enhanced the mean absolute error (MAE) by 20% relative to the LNSM, where the MAE for distance was 1.6 m for the PSO–POLYN model and 2 m for the LNSM. In addition, after applying PSO, the correlation coefficient (R²) of the regression line between RSSI and the estimated distance improved to 0.99, while that obtained through the LNSM was 0.94. The presented method based on PSO–POLYN outperformed models in the literature, both in terms of MAE and correlation coefficient in indoor environments.

近年来，对无线传感器网络及其在医疗保健和环境监测中的应用的研究引起了极大的兴趣。在这样的应用中，患者与信标节点之间的距离估计的准确性对于确定患者位置至关重要。在这项研究中，在室内环境中使用ZigBee技术使用接收信号强度指示器（RSSI）测量了移动节点（由阿尔茨海默氏症患者携带）与信标节点之间的距离。距离估计由两个路径损耗模型确定：对数正态阴影模型（LNSM）和使用通过MATLAB曲线拟合工具获得的多项式函数（POLYN函数）的派生模型。下一个，粒子群优化（PSO）与多项式函数（称为PSO–POLYN函数）合并，以获得POLYN函数的最佳系数值。所得的路径损耗模型可以改善患有阿尔茨海默氏病的患者和信标结之间的距离误差。结果表明，相对于LNSM，PSO-POLYN模型的合并使平均绝对误差（MAE）提高了20％，其中PSO-POLYN模型的距离MAE为1.6 m，LNSM的距离为2 m。另外，应用PSO后，相关系数（结果表明，相对于LNSM，PSO-POLYN模型的合并使平均绝对误差（MAE）提高了20％，其中PSO-POLYN模型的距离MAE为1.6 m，LNSM的距离为2 m。另外，应用PSO后，相关系数（结果表明，相对于LNSM，PSO-POLYN模型的合并使平均绝对误差（MAE）提高了20％，其中PSO-POLYN模型的距离MAE为1.6 m，LNSM的距离为2 m。另外，应用PSO后，相关系数（RSSI和估计距离之间的回归线的R ²）改进为0.99，而通过LNSM获得的回归线的R ²）为0.94。无论是在室内环境中的MAE还是相关系数方面，基于PSO-POLYN提出的方法都优于文献中的模型。