Li Fei, Liu Min, Xu Gaowei
Department of Computer Science, Zhejiang University City College, Hangzhou 310015, China.
College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
Sensors (Basel). 2019 Jul 29;19(15):3334. doi: 10.3390/s19153334.
In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR's improvement in performance.
在许多复杂的制造环境中，运行的设备必须由无线传感器网络（WSN）进行监测，这不仅要求WSN具有较长的使用寿命，还要求将设备监测数据快速、高质量地传输到监测中心。WSN中的传统路由算法，如基于基本蚁群的路由（BABR）只要求单一最短路径，且BABR算法收敛速度慢，容易陷入局部最优，导致算法过早停滞。一种名为量子蚁群多目标路由（QACMOR）的新WSN路由算法，通过将量子计算和多目标适应度函数引入路由研究算法，可用于此类制造环境的监测。具体而言，用量子比特表示节点信息素，并通过旋转量子门来更新搜索路径的信息素。搜索路径中节点的能量消耗、传输延迟和网络负载平衡度等因素作为适应度函数来确定最优路径。在此，通过仿真分析和实际制造环境验证了QACMOR在性能上的提升。
