SA-PSO based optimizing reader deployment in large-scale RFID Systems
SA-PSO based optimizing reader deployment in large-scale RFID Systems
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DOI：
10.1016/j.jnca.2015.02.011
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Ming Tao;Shuqiang Huang;Yang Li;Min Yan;Yuyu Zhou
中科院分区：
文献类型：
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作者：
Ming Tao;Shuqiang Huang;Yang Li;Min Yan;Yuyu Zhou
RFID技术有望给各个工业领域带来革命性的变化，大规模RFID系统的应用已经成为一种趋势。阅读器部署是RFID网络规划（RNP）中最具挑战性的问题之一，它需要满足一系列约束条件并优化一系列目标，从而使大规模RFID系统以最优方式运行。以覆盖、信号干扰和负载均衡为优化目标，推导出目标范围，将读写器部署问题转化为多目标组合优化问题。然后，通过引入模拟退火算法中的大都会规则，提出了一种改进的粒子群算法（SA-PSO）来解决该问题，该算法可以限制初始粒子和新粒子在迭代过程中的位置变化，加快算法的收敛速度。仿真结果表明，SA-PSO算法在覆盖优化方面具有上级的优势，通过优化信号干扰和读写器间的负载均衡，提高了系统的整体性能。
The RFID technologies are expected to revolutionize various industrial areas and the application of large-scale RFID systems has been a trend. Satisfying a set of imperative constrains and optimizing a set of objectives, the reader deployment is the primary problem for the most challenging RFID network planning (RNP), and needs to be reasonably solved to operate the large-scale RFID systems in an optimal fashion. By taking the coverage, signal interference and load balance as the optimization objectives and deducing the objective ranges, the reader deployment is conducted as a problem of multi-objectives combination optimization. And then, by introducing the Metropolis rule of Simulated Annealing Algorithm, an improved Particle Swarm Algorithm is proposed (SA-PSO) to solve this problem, which can restrict the position change of original and new particles in the iteration process and accelerate the convergence speed of the algorithm. The simulation results show that the addressed SA-PSO algorithm is superior to the compared algorithms in coverage optimization, and the whole system performance can be enhanced by optimizing the signal interference and load balance among the deployed readers.