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Investigation of multi-objective optimisation techniques to minimise the localisation error in wireless sensor networks

机译:对多目标优化技术的研究最小化无线传感器网络中的本地化误差

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Wireless Sensor Networks (WSN) have a major role in remote sensing environments. In recent trends sensors are used in various wireless technologies due to their smaller size, cheaper rates and ability to communicate with each other to create a network. The sensor network is the convergent technology of micro-electronics and electro-mechanical technologies. The localisation process can determine the location of each node in the network. Mobility-assisted localisation is an effective technique for node localisation using mobility anchor. The mobile anchor is also used to optimise the path planning for the location-aware mobile nodes. In this proposed system, a multi-objective method has been proposed to minimise the distance between the source and the target node using the Dijkstra algorithm with obstacle avoidance. The Grasshopper Optimisation Algorithm (GOA), and Butterfly Optimisation Algorithm (BOA) based multi-objective models have been implemented along with obstacle avoidance and path planning. The proposed system maximises the localisation accuracy. Also it minimises the localisation error and the computation time when comparing with existing systems.
机译:无线传感器网络(WSN)在遥感环境中具有重要作用。由于其较小的尺寸,更便宜的速度和互相通信能力来创建网络,传感器在各种无线技术中使用传感器。传感器网络是微电子和机电技术的收敛技术。本地化过程可以确定网络中每个节点的位置。移动性辅助定位是使用移动性锚点的节点定位的有效技术。移动锚还用于优化位置感知移动节点的路径规划。在该提出的系统中,已经提出了一种多目标方法,以最小化使用避免障碍物的Dijkstra算法来最小化源和目标节点之间的距离。蚱蜢优化算法(GOA)和基于蝴蝶优化算法(BOA)的多目标模型以及避免避免和路径规划。所提出的系统最大化了本地化精度。还可以最大限度地减少本地化误差和与现有系统进行比较时的计算时间。

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