Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization
Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization
复制标题
优化 WiFi 和 BLE 混合定位中的 AP 和信标放置
DOI：
10.1016/j.jnca.2020.102673
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发表时间：
2020
影响因子：
8.7
通讯作者：
Zhao Long
中科院分区：
文献类型：
--
作者：
Tian Yu;Huang Baoqi;Jia Bing;Zhao Long
如今，诸如WiFi、蓝牙低功耗（BLE）等的无线通信技术，已经渗透到我们的日常生活中，并且不仅提供方便的数据传输服务，而且还实现了流行的室内定位和导航服务。无线基础设施（如WiFi接入点（AP）和BLE信标）的放置对定位性能有重大影响。更重要的是，由于AP和信标可能同时用于网络接入和定位，因此在部署AP和信标时，有必要同时考虑覆盖和定位。此外，仍然存在其他关键挑战，包括优化WiFi和BLE混合定位中的AP和信标放置，以及优化现有无线网络中额外AP和/或信标的放置，这些基本上是NP完全的。针对这些问题，提出了一种基于Cramer-Rao下界（CRLB）的启发式差分进化算法。具体地说，利用CRLB作为定位的度量，同时定义覆盖度准则作为覆盖的度量，两者都被纳入差分进化算法的评价函数中。此外，而不是使用理想的对数距离路径损耗（LDPL）模型，更实用的Motley-Keenan模型，以反映在室内环境中广泛存在的障碍物的影响。在此基础上，设计并实现了一个基于Geotools的AP和信标布局优化软件。最后，进行了大量的仿真和现场实验，并进行了全面的比较，证实了所提出的算法的效率和有效性。
Nowadays, wireless communication techniques, such as WiFi, Bluetooth low energy (BLE), etc., have been pervasive in our daily lives, and not only provide convenient data transmission services, but also enable popular indoor positioning and navigation services. The placement of wireless infrastructures like WiFi access points (APs) and BLE beacons have significant influences on the performance of localization. More importantly, since APs and beacons are probably used for both network access and localization, it is necessary to take into account both coverage and localization when deploying APs and beacons. In addition, there still exist other critical challenges, including optimizing AP and beacon placement in WiFi and BLE hybrid localization and optimizing the placement of extra APs and/or beacons in an existing wireless network, which are essentially NP-complete. This paper tackles these problems of optimizing AP and beacon placement by proposing a heuristic differential evolution algorithm based on the widely used Cramer-Rao lower bound (CRLB). To be specific, the CRLB is leveraged as a metric for localization and meanwhile a coverage degree criterion is defined as a metric for coverage, both of which are incorporated into the evaluation function of the differential evolution algorithm. Furthermore, instead of using the ideal log distance path loss (LDPL) model, the more practical Motley-Keenan model is adopted to reflect the influences of obstacles that are widespread in indoor environments. On these grounds, a software is designed and implemented based on Geotools to optimize AP and beacon placement in an interactive GUI manner. Finally, extensive simulations and field experiments are conducted, and a thorough comparison confirms the efficiency and effectiveness of the proposed algorithm.