摘要： 基于位置的服务是物联网最具发展潜力的应用之一，提供可靠的节点定位信息已成为衡量物联网技术标准的重要指标。为有效地对未知节点进行定位，针对果蝇优化算法定位精度低以及收敛速度慢的问题，提出一种基于果蝇算法的物联网节点定位改进方法。采用边界盒算法限制果蝇优化算法定位的初始范围，同时重构算法的味道浓度函数，选择合适的测量节点数量以及种群规模，实现算法的动态特性与定位精度的平衡。实验结果表明，与果蝇优化、粒子群优化等算法相比，该算法能够有效提高定位精度和收敛速度，并且稳定性较高，可满足节点定位需求。

关键词: 定位, 物联网, 果蝇优化算法, 边界盒算法, 初始范围, 味道浓度

Abstract: The location-based service is one of the most potential applications of Internet of Things(IoT).Providing reliable localization information becomes an important indicator to measure the technology standard of IoT.In order to effectively locate the unknown nodes and improve the low localization accuracy and slow convergence speed of Fruit Fly Optimization Algorithm(FOA),this paper proposes an improved localization method of IoT nodes based on fruit fly algorithm.This method adopts the bounding-box algorithm to set the initial range of FOA localization and rebuilds the smell concentration function.Then the appropriate number of measurement nodes and the scale of population are selected to realize the balance between dynamic characteristics and localization accuracy.Experimental results show that compared with algorithms such as FOA and Particle Swarm Optimization(PSO),the proposed algorithm has better localization accuracy and convergence speed.Moreover,the improved stability can meet the localization requirements.

Key words: localization, Internet of Things(IoT), Fruit Fly Optimization Algorithm(FOA), bounding-box algorithm, initial range, smell concentration

中图分类号: 

• TP393