« Previous Abstract"Organic speciation of ambient quasi-ultrafine particulate matter (PM(0.36)) in Augsburg, Germany: Seasonal variability and source apportionment"    Next AbstractThe regulation of three new members of the cytochrome P450 CYP6 family and their promoters in the cotton aphid Aphis gossypii by plant allelochemicals »

Sensors (Basel)

Title:		A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment
Author(s):		Li F; Liu M; Xu G;
Address:		"Department of Computer Science, Zhejiang University City College, Hangzhou 310015, China. lif@zucc.edu.cn. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"
Journal Title:		Sensors (Basel)
Year:		2019
Volume:		20190729
Issue:		15
Page Number:		-
DOI:		10.3390/s19153334
ISSN/ISBN:		1424-8220 (Electronic) 1424-8220 (Linking)
Abstract:		"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"
Keywords:		ant colony optimization (ACO) energy quantum-inspired evolutionary algorithms routing algorithm wireless sensor network (WSN);
Notes:		"PubMed-not-MEDLINELi, Fei Liu, Min Xu, Gaowei eng 61573257/the Scientific Research Projects of the NSFC/ 20150533B16/Hangzhou Municipal Science and Technology Bureau of social development and scientific research projects/ Y201432791/Scientific research project of Zhejiang Education Department/ Switzerland 2019/08/01 Sensors (Basel). 2019 Jul 29; 19(15):3334. doi: 10.3390/s19153334"