School of Computer Engineering, Suzhou Vocational University, Suzhou 215104, China.
School of Software, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2018 Jun 30;18(7):2107. doi: 10.3390/s18072107.
Internal reliability and external safety of Wireless Sensor Networks (WSN) data transmission have become increasingly outstanding issues with the wide applications of WSN. This paper proposes a new method for access control and mitigation of interfering noise in time synchronization environments. First, a formal definition is given regarding the impact interference noise has on the clock skew and clock offset of each node. The degree of node interference behavior is estimated dynamically from the perspective of time-stamp changes caused by the interference noise. Secondly, a general access control model is proposed to resist invasion of noise interference. A prediction model is constructed using the Bayesian method for calculating the reliability of neighbor node behavior in the proposed model. Interference noise, which attacks the time synchronization, is regarded as the key factor for probability estimation of the reliability. The result of the calculations determines whether it is necessary to initiate synchronization filtering. Finally, a division of trust levels with bilinear definition is employed to lower interference noise and improve the quality of interference detection. Experimental results show that this model has advantages in system overhead, energy consumption and testing errors, compared to its counterparts. When the disturbance intensity of a WSN increases, the proposed optimized algorithm converges faster with a lower network communication load.
无线传感器网络（WSN）数据传输的内部可靠性和外部安全性随着 WSN 的广泛应用而变得越来越突出。本文提出了一种在时间同步环境中用于访问控制和减轻干扰噪声的新方法。首先，从干扰噪声引起的时间戳变化的角度，给出了干扰噪声对每个节点的时钟偏差和时钟偏移的影响的形式化定义。然后，从时间戳变化的角度，动态估计节点干扰行为的程度。其次，提出了一种通用的访问控制模型来抵抗噪声干扰的入侵。使用贝叶斯方法构建了一个预测模型，用于计算邻居节点行为在提出的模型中的可靠性。将攻击时间同步的干扰噪声视为可靠性概率估计的关键因素。计算结果决定是否需要启动同步滤波。最后，采用双线性定义划分信任级别，以降低干扰噪声并提高干扰检测质量。实验结果表明，与同类模型相比，该模型在系统开销、能耗和测试误差方面具有优势。当 WSN 的干扰强度增加时，所提出的优化算法收敛速度更快，网络通信负载更低。
