Wireless communications are prone to the interference, so the data transmission among the nodes in wireless sensor networks deployed in complex environments has the obvious uncertainty. This paper adopts probability theory to extend the existed interference model, and gives an interference analysis model and implements it through the cross-layer method. In addition, the isotonic property of the interference-aware routing metric is proved. Then, a probabilistic routing algorithm is proposed and its correctness and time-space complexity are analyzed. Simulation results show that the proposed algorithm can achieve better packet delivery ratio, throughput, jitter and average delay in dense deployment under the different loads at the expense of the comparable average length of paths compared with the Adhoc On-Demand Distance Vector （AODV） algorithm.
针对目前多层社会网络(multi-layered social network,MSN)的社团发现算法较少、社团划分结果较粗糙等特点,提出了一种基于边聚类的多层社会网络社团发现(CLEDCC)算法。该算法综合考虑每层关系网中的任意两节点邻居及节点本身的关系强弱,并分别针对人造稀疏网、稠密网以及真实数据集进行仿真。实验表明,所提出的CLEDCC算法能有效地避免参数不确定性问题,并比跨层边聚类系数(CLECC)算法的社团划分结果更精准。
