Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (7): 121-126.DOI: 10.3778/j.issn.1002-8331.1707-0198

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RSSI localization in noisy environment and its fruit fly optimization algorithm

HAO Juan, ZHANG Zhuhong, TU Xin   

  1. College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
  • Online:2018-04-01 Published:2018-04-16


郝  娟,张著洪,凃  歆   

  1. 贵州大学 大数据与信息工程学院,贵阳 550025

Abstract: This paper firstly transforms the conventional RSSI positioning model into a non-constrained expected value programming model. Secondly, one such model is solved by developing a novel fruit fly optimization algorithm in stochastic environments in order to seek the location of the unknown node. In this algorithm, the current fruit fly population is divided into sub-populations by arc grouping, and subsequently a hybrid mutation strategy is implemented to find the optimal solution. Comparative numerical experiments have validated that the algorithm, with high-efficient convergence and high positioning accuracy, is feasible in solving engineering RSSI positioning.

Key words: Received Signal Strength Indicator(RSSI), expected value programming, fruit fly optimization, arc grouping, hybrid mutation

摘要: 针对未知节点的定位过度依赖于接收信号强度指示(Received Signal Strength Indicator,RSSI)物理测量的精度问题,将传统RSSI定位模型转化为非约束期望值规划模型,进而设计随机环境下的新型果蝇优化算法寻找未知节点的位置。该算法利用弧形分组将果蝇群均衡划分为子群,对果蝇个体实施混合变异,加速寻优进程,提高收敛速度和寻优精度。比较性的数值实验显示,该算法的收敛速度快,对未知节点的定位精度高,其应用于RSSI定位问题是可行的。

关键词: 接收信号强度指示, 期望值规划, 果蝇优化, 弧形分组, 混合变异