School of Computer Science and Engineering, Southeast University, Nanjing 210096, China.
Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China.
Sensors (Basel). 2021 Oct 31;21(21):7254. doi: 10.3390/s21217254.
For wireless sensor networks (WSN) with connection failure uncertainties, traditional minimum spanning trees are no longer a feasible option for selecting routes. Reliability should come first before cost since no one wants a network that cannot work most of the time. First, reliable route selection for WSNs with connection failure uncertainties is formulated by considering the top-k most reliable spanning trees (RST) from graphs with structural uncertainties. The reliable spanning trees are defined as a set of spanning trees with top reliabilities and limited tree weights based on the possible world model. Second, two tree-filtering algorithms are proposed: the k minimum spanning tree (KMST) based tree-filtering algorithm and the depth-first search (DFS) based tree-filtering algorithm. Tree-filtering strategy filters the candidate RSTs generated by tree enumeration with explicit weight thresholds and implicit reliability thresholds. Third, an innovative edge-filtering method is presented in which edge combinations that act as upper bounds for RST reliabilities are utilized to filter the RST candidates and to prune search spaces. Optimization strategies are also proposed for improving pruning capabilities further and for enhancing computations. Extensive experiments are conducted to show the effectiveness and efficiency of the proposed algorithms.
对于存在连接故障不确定性的无线传感器网络 (WSN),传统的最小生成树不再是选择路由的可行选择。由于没有人希望网络大部分时间无法正常工作,因此可靠性应该优先于成本。首先,通过考虑具有结构不确定性的图中前 k 个最可靠的生成树 (RST),为具有连接故障不确定性的 WSN 制定可靠的路由选择。可靠的生成树被定义为基于可能世界模型的具有最高可靠性和有限树权重的一组生成树。其次,提出了两种树过滤算法:基于 k 个最小生成树 (KMST)的树过滤算法和基于深度优先搜索 (DFS)的树过滤算法。树过滤策略使用显式权重阈值和隐式可靠性阈值对树枚举生成的候选 RST 进行过滤。第三,提出了一种创新的边过滤方法,利用作为 RST 可靠性上界的边组合来过滤 RST 候选并修剪搜索空间。还提出了优化策略,以进一步提高修剪能力和增强计算能力。进行了广泛的实验以证明所提出算法的有效性和效率。