Abstract

The use of a localization system is necessary in Wireless Sensor Networks (WSN) either for communication protocols (geographic routing) or for various applications (person tracking, battlefield tele-monitoring, enemy detection, etc.). The objective of a localization method in such environment is to find the locality (or position) of all sensors deployed randomly in a multidimensional field in order to accomplish a specific task. We specify in this paper an improved localization method in WSNs called FOA-L (Fruit Fly Optimization Algorithm for node’s Localization). The proposed method applies the Fruit fly Optimization Algorithm (FOA) to minimize the error between estimated and real locations of the unknown sensors. In the proposed localization scheme, we initialize a group of flies in the search area and they are given a random value of direction and distance. Then, we find out the flies with the highest smell value using fitness in order to estimate the location of the target node. Simulation results concerning performance evaluation show that the proposed technique FOA-L has the better localization accuracy than the well-known localization algorithms such as Particle Swarm Optimization and Chicken Swarm Optimization as well as a faster computation time, which contributes in reducing the cost of the sensor’s localization.

中文翻译：

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基于果蝇优化算法的无线传感器网络分布式定位改进方法

摘要

在无线传感器网络 (WSN) 中，定位系统的使用对于通信协议（地理路由）或各种应用（人员跟踪、战场远程监控、敌人检测等）是必要的。在这种环境中，定位方法的目标是找到在多维领域中随机部署的所有传感器的位置（或位置），以完成特定任务。我们在本文中指定了一种改进的 WSN 定位方法，称为 FOA-L（用于节点定位的果蝇优化算法）。所提出的方法应用果蝇优化算法 (FOA) 来最小化未知传感器的估计位置和实际位置之间的误差。在提议的本地化方案中，我们在搜索区域初始化一组苍蝇，并给它们一个随机的方向和距离值。然后，我们使用适应度找出具有最高气味值的果蝇，以估计目标节点的位置。性能评估的仿真结果表明，所提出的技术 FOA-L 具有比粒子群优化和鸡群优化等众所周知的定位算法更好的定位精度，以及更快的计算时间，有助于降低成本。传感器定位。