Njima Wafa, Ahriz Iness, Zayani Rafik, Terre Michel, Bouallegue Ridha
Conservatoire National des Arts et Métiers, CEDRIC/ LAETITIA Laboratory, 75003 Paris, France.
University of Carthage, Higher School of Communication of Tunis, LR-11/TIC-03 Innov'COM Laboratory, 2083 Ariana, Tunisia.
Sensors (Basel). 2019 Jul 15;19(14):3127. doi: 10.3390/s19143127.
Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches.
当前，室内定位是与物联网（IoT）相关的最具挑战性的问题之一。大多数先进的室内定位解决方案需要很高的计算复杂度才能实现令人满意的定位精度，并且无法满足物联网设备的内存限制。在本文中，我们基于卷积神经网络（CNN）开发了一个定位框架，该框架将在线预测复杂度转移到离线预处理步骤。受此类网络在图像分类领域出色性能的启发，室内定位问题被表述为基于3D无线电图像的区域识别。其目的是通过确定传感器节点的位置区域来准确地对其进行定位。基于接收信号强度指示（RSSI）指纹构建3D无线电图像。仿真结果证明了所使用的不同参数、优化算法和模型架构的选择是合理的。考虑到定位精度和计算复杂度之间的权衡，我们提出的方法优于其他流行方法。
