无人机辅助的基于前馈神经网络的节点定位算法

针对无线传感网络(WSNs)的节点定位问题，提出无人机辅助的基于前馈神经网络的节点定位(UAV-NN)算法。UAV-NN算法利用无人机(UAV)作为锚节点，并由UAV周期地发射beacon信号，利用极端学习机(LEM)训练单隐藏前向反馈的神经网络(SLFN)，未知节点接收来自UAV发射的beacon信号，并记录其接收信号强度指示(RSSI)，已训练的SLFN再依据RSSI值估计节点位置。仿真结果表明，相比于传统的基于RSSI定位算法，提出的UAV-NN算法无需部署地面锚节点；相比其他传统的机器学习算法，UAV-NN算法通过引用ELM，减少了定位误差。

UAV-assisted localization algorithm based on Feedforward Neural Network

Aiming at the problem of node location in Wireless Sensor Network(WSNs), Unmanned Aerial Vehicle-assisted localization algorithm based on Feedforward Neural Network(UAV-NN) is proposed. The localization is performed by using mobile Unmanned Aerial Vehicles(UAVs) as the anchor nodes to send the beacon signals every period of time, thus every unknown node can estimate its current position based on the Received Signal Strength Indicator(RSSI) values of the received beacon signals by training the Single hidden-Layer Feedforward Neural Network(SLFN) using Extreme Learning Machine(ELM) technique. The proposed method requires fewer anchor nodes and no ground anchor node compared to traditional RSSI based localization technique to yield better accuracy. Simulation results show that this technique is capable of performing real-time localization for unknown nodes with less localization error by using ELM compared to other traditional machine learning algorithms.