Department of Electrical Engineering, Faculty of Engineering, Mahidol University, 999 Salaya, Nakhorn Pathom 73170, Thailand.
Department of Electrical Engineering and Energy Management, Faculty of Engineering, Kasem Bundit University, 1761 Phatthanakan 37 Alley, Suan Luang, Bangkok 10250, Thailand.
Sensors (Basel). 2023 Mar 28;23(7):3525. doi: 10.3390/s23073525.
Path loss models are essential tools for estimating expected large-scale signal fading in a specific propagation environment during wireless sensor network (WSN) design and optimization. However, variations in the environment may result in prediction errors due to uncertainty caused by vegetation growth, random obstruction or climate change. This study explores the capability of multi-boundary fuzzy linear regression (MBFLR) to establish uncertainty relationships between related variables for path loss predictions of WSN in agricultural farming. Measurement campaigns along various routes in an agricultural area are conducted to obtain terrain profile data and path losses of radio signals transmitted at 433 MHz. Proposed models are fitted using measured data with "initial membership level" (μAI). The boundaries are extended to cover the uncertainty of the received signal strength indicator (RSSI) and distance relationship. The uncertainty not captured in normal measurement datasets between transmitter and receiving nodes (e.g., tall grass, weed, and moving humans and/or animals) may cause low-quality signal or disconnectivity. The results show the possibility of RSSI data in MBFLR supported at an μAI of 0.4 with root mean square error (RMSE) of 0.8, 1.2, and 2.6 for short grass, tall grass, and people motion, respectively. Breakpoint optimization helps provide prediction accuracy when uncertainty occurs. The proposed model determines the suitable coverage for acceptable signal quality in all environmental situations.
路径损耗模型是在无线传感器网络 (WSN) 设计和优化过程中估算特定传播环境中预期大规模信号衰落的重要工具。然而，由于植被生长、随机障碍物或气候变化引起的不确定性，环境的变化可能会导致预测错误。本研究探讨了多边界模糊线性回归 (MBFLR) 在建立与 WSN 路径损耗预测相关变量之间的不确定性关系方面的能力，该研究在农业领域的各种路线上进行了测量活动，以获取地形剖面数据和在 433MHz 传输的无线电信号的路径损耗。使用测量数据和“初始隶属度水平” (μAI) 对提出的模型进行拟合。边界扩展到覆盖接收信号强度指示 (RSSI) 和距离关系的不确定性。发射机和接收节点之间的正常测量数据集无法捕捉到的不确定性（例如，高草、杂草、移动的人和/或动物）可能会导致信号质量差或连接中断。结果表明，在 μAI 为 0.4 时，MBFLR 支持 RSSI 数据的可能性，短草、高草和人运动的均方根误差 (RMSE) 分别为 0.8、1.2 和 2.6。断点优化有助于在出现不确定性时提供预测精度。所提出的模型确定了在所有环境情况下可接受信号质量的合适覆盖范围。
