Liu Lianggui, Chen Li, Jia Huiling
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel). 2016 Feb 19;16(2):247. doi: 10.3390/s16020247.
In large-scale wireless sensor networks (WSNs), in order to enhance network security, it is crucial for a trustor node to perform social milieu oriented routing to a target a trustee node to carry out trust evaluation. This challenging social milieu oriented routing with more than one end-to-end Quality of Trust (QoT) constraint has proved to be NP-complete. Heuristic algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this challenging problem. However, existing solutions cannot guarantee the efficiency of searching; that is, they can hardly avoid obtaining partial optimal solutions during a searching process. Quantum annealing (QA) uses delocalization and tunneling to avoid falling into local minima without sacrificing execution time. This has been proven a promising way to many optimization problems in recently published literatures. In this paper, for the first time, with the help of a novel approach, that is, configuration path-integral Monte Carlo (CPIMC) simulations, a QA-based optimal social trust path (QA_OSTP) selection algorithm is applied to the extraction of the optimal social trust path in large-scale WSNs. Extensive experiments have been conducted, and the experiment results demonstrate that QA_OSTP outperforms its heuristic opponents.
在大规模无线传感器网络（WSN）中，为了增强网络安全性，对于一个信任方节点而言，执行面向社会环境的路由以找到目标受托方节点来进行信任评估至关重要。这种具有多个端到端信任质量（QoT）约束的具有挑战性的面向社会环境的路由已被证明是NP完全问题。具有多项式和伪多项式时间复杂度的启发式算法通常用于处理这个具有挑战性的问题。然而，现有解决方案无法保证搜索效率；也就是说，它们在搜索过程中很难避免获得局部最优解。量子退火（QA）利用离域化和隧穿来避免陷入局部最小值，同时不牺牲执行时间。在最近发表的文献中，这已被证明是解决许多优化问题的一种有前途的方法。在本文中，首次借助一种新颖的方法，即配置路径积分蒙特卡罗（CPIMC）模拟，将基于QA的最优社会信任路径（QA_OSTP）选择算法应用于大规模WSN中最优社会信任路径的提取。已经进行了大量实验，实验结果表明QA_OSTP优于其启发式对手。
