改进的基于统计学的滑动窗口无参数的累积和算法

为解决IEEE802.15.4无线传感器网络(WSN)中节点自私行为的检测问题，将最低检测延迟作为决策目标，提出了一种改进的基于统计学的滑动窗口无参数的累积和(SWN-CUSUM)算法。算法通过跟踪来自数据包两次成功传输之间的延迟特征序列，以此来判断无线传感器网络中的节点是否存在自私行为。最后通过NS2仿真工具验证算法的有效性。研究结果表明：改进的算法不仅弱化了阈值对算法性能的影响，还缩小了用于检测自私行为的滑动窗口大小，同时所提算法相对于原SWN-CUSUM算法在计算量及检测延迟上均有改善，证明改进的算法可以有效、快速地检测IEEE802.15.4无线传感器网络中的节点自私行为。

Improved sliding window non-parameter cumulation sum algorithm

To solve the detection problem of selfish behavior in IEEE802.15.4 Wireless Sensor Network (WSN), an improved Sliding Window Non-parameter Cumulation Sum (SWN-CUSUM) algorithm based on statistics was proposed to decrease the detection delay. By tracing the delay characteristic sequence between successful transmissions, the algorithm could distinguish if there was a selfish behavior in the WSNs. The NS2 simulation tool was conducted to validate the feasibility of the proposed algorithm. The experimental results show that the improved algorithm not only weakens the impact of the threshold on the performance of the algorithm, but also reduces the size of sliding window used to detect selfish behavior, and the improved algorithm makes improvement in the calculation and the detection delay than the primitive SWN-CUSUM algorithm, so the improved algorithm can detect effectively and rapidly the selfish behavior of nodes in IEEE802.15.4 WSNs.