Guo Jianing, Sun Yunshan, Liu Ting, Li Yanqin, Fei Teng
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China.
Sensors (Basel). 2025 Jan 10;25(2):396. doi: 10.3390/s25020396.
In existing coverage challenges within wireless sensor networks, traditional sensor perception models often fail to accurately represent the true transmission characteristics of wireless signals. In more complex application scenarios such as warehousing, residential areas, etc., this may lead to a large gap between the expected effect of actual coverage and simulated coverage. Additionally, these models frequently neglect critical factors such as sensor failures and malfunctions, which can significantly affect signal detection. To address these limitations and enhance both network performance and longevity, this study introduces a perception model that incorporates path loss and false alarm probability. Based on this perception model, the optimization objective function of the WSN node optimization coverage problem is established, and then the intelligent optimization algorithm is used to solve the objective function and finally achieve the optimization coverage of sensor nodes. The study begins by deriving a logarithmic-based path loss model for wireless signals. It then employs the Neyman-Pearson criterion to formulate a maximum detection probability model under conditions where the cost function and prior probability are unknown, constraining the false alarm rate. Simulated experiments are conducted to assess the influence of various model parameters on detection probability, providing comparative analysis against traditional perception models. Ultimately, an optimization model for WSN coverage, based on combined detection probability, is developed and solved using an intelligent optimization algorithm. The experimental results indicate that the proposed model more accurately captures the signal transmission and detection characteristics of sensor nodes in WSNs. In the coverage area of the same size, the coverage of the model constructed in this paper is compared with the traditional 0/1 perception model and exponential decay perception model. The model can achieve full coverage of the area with only 50 nodes, while the exponential decay model requires 54 nodes, and the coverage of the 0/1 model is still less than 70% at 60 nodes. According to the simulation experiments, it can be basically proved that the WSN node optimization coverage strategy based on the proposed model provides an effective solution for improving network performance and extending network lifespan.
在无线传感器网络现有的覆盖挑战中，传统的传感器感知模型往往无法准确呈现无线信号的真实传输特性。在诸如仓库、住宅区等更复杂的应用场景中，这可能导致实际覆盖的预期效果与模拟覆盖之间存在较大差距。此外，这些模型常常忽略诸如传感器故障和失灵等关键因素，而这些因素会显著影响信号检测。为解决这些局限性并提高网络性能和寿命，本研究引入了一种结合路径损耗和误报概率的感知模型。基于此感知模型，建立了无线传感器网络节点优化覆盖问题的优化目标函数，然后使用智能优化算法求解该目标函数，最终实现传感器节点的优化覆盖。该研究首先推导了基于对数的无线信号路径损耗模型。然后采用奈曼 - 皮尔逊准则，在成本函数和先验概率未知的条件下制定最大检测概率模型，以约束误报率。进行了模拟实验，以评估各种模型参数对检测概率的影响，并与传统感知模型进行对比分析。最终，开发了基于组合检测概率的无线传感器网络覆盖优化模型，并使用智能优化算法进行求解。实验结果表明，所提出的模型能更准确地捕捉无线传感器网络中传感器节点的信号传输和检测特性。在相同大小的覆盖区域内，将本文构建的模型与传统的0/1感知模型和指数衰减感知模型的覆盖情况进行比较。该模型仅用50个节点就能实现区域的全覆盖，而指数衰减模型需要54个节点，0/1模型在60个节点时覆盖率仍低于70%。根据模拟实验，可以基本证明基于所提出模型的无线传感器网络节点优化覆盖策略为提高网络性能和延长网络寿命提供了一种有效的解决方案。
