Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO.
Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO.
复制标题
使用改进的 PSO 通过 BLE 传感器优化 CNN 进行室内定位
DOI：
10.3390/s21061995
复制
发表时间：
2021-03-12
期刊：
影响因子：
--
通讯作者：
Xu S
中科院分区：
文献类型：
--
作者：
Sun D;Wei E;Ma Z;Wu C;Xu S
在过去的几十年里，室内导航吸引了商业开发人员和研究人员。本地化工具、方法和框架的开发使得当前的通信服务和应用能够通过并入位置数据来优化。对于诸如工作流分析的临床应用，已经采用蓝牙低功耗（BLE）信标来映射室内环境中的个体的位置。为了映射位置，某些现有方法使用接收信号强度指示符（RSSI）。当使用RSSI传感器监测室内位置时，设备需要配置为允许动态干扰模式。在本文中，我们的目标是探索一种替代方法，在复杂的室内建筑环境中使用BLE传感器监测移动用户的室内位置。我们开发了一种基于卷积神经网络（CNN）的定位模型，该模型基于由来自x轴和y轴的接收信号指示器的数量组成的2D图像。通过这种方式，就像一个像素一样，我们与每个10 × 10的矩阵进行交互，这些矩阵包含坐标的空间信息，并建议传感器的可能移动，添加传感器和删除传感器。为了开发CNN，我们采用了一种神经进化方法，通过增强的粒子群优化（PSO）来动态优化和创建网络中的多个层。对于CNN的优化，将PSO得到的全局最优解直接赋予CNN各层的权值。此外，我们在PSO中采用动态惯性权重，而不是恒定的惯性权重，以保持CNN层的长度对应于来自BLE传感器的RSSI信号。实验是在一个建筑环境中进行的，其中十三个信标设备已安装在不同的位置，以记录坐标。为了进行评估比较，我们进一步采用了机器学习和深度学习算法来预测用户在室内环境中的位置。实验结果表明，所提出的优化的基于CNN的方法显示出较高的准确性（97.92%，2.8%的误差）跟踪移动用户的位置在一个复杂的建筑物，而无需复杂的校准相比，最近的其他方法。
Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.
登录
查看更多内容
影响因子：
3.9
作者：
Beritelli, Francesco;Capizzi, Giacomo;Scaglione, Francesco
通讯作者：
Scaglione, Francesco
影响因子：
--
作者：
Ferroni P;Zanzotto FM;Scarpato N;Riondino S;Guadagni F;Roselli M
通讯作者：
Roselli M
DOI：
10.1016/j.future.2018.12.064
发表时间：
2019-06-01
影响因子：
7.5
作者：
Alhakbani，Noura;Hassan，Mohammad Mehedi;Fortino，Giancarlo
通讯作者：
Fortino，Giancarlo
影响因子：
3.9
作者：
AL-Madani, Basem;Orujov, Farid;Venckauskas, Algimantas
通讯作者：
Venckauskas, Algimantas
影响因子：
6.4
作者：
Hornbeck, Thomas;Naylor, David;Polgreen, Philip M.
通讯作者：
Polgreen, Philip M.