Localization is important for a large number of Internet of Things (IoT) endpoint devices connected by LoRaWAN. Due to the bandwidth limitations of LoRaWAN, existing localization methods without specialized hardware (e.g., GPS) produce poor performance. To increase the localization accuracy, we propose a super-resolution localization method, called Seirios, which features a novel algorithm to synchronize multiple non-overlapped communication channels by exploiting the unique features of the radio physical layer to increase the overall bandwidth. By exploiting both the original and the conjugate of the physical layer, Seirios can resolve the direct path from multiple reflectors in both indoor and outdoor environments. We design a Seirios prototype and evaluate its performance in an outdoor area of 100 m $\times$ 60 m, and an indoor area of 25 m $\times$ 15 m, which shows that Seirios can achieve a median error of 4.4 m outdoors (80% samples < 6.4 m), and 2.4 m indoors (80% samples < 6.1 m), respectively. The results show that Seirios produces 42% less localization error than the baseline approach. Our evaluation also shows that, different to previous studies in Wi-Fi localization systems that have wider bandwidth, time-of-fight (ToF) estimation is less effective for LoRaWAN localization systems with narrowband radio signals.
翻译:本地化对于由LoRawAN连接的大量物端点互联网设备非常重要。 由于LoRaWAN的带宽限制, 现有的无专用硬件( 如全球定位系统) 的本地化方法造成性能差。 为了提高本地化的准确性, 我们提议了一个超级分辨率本地化方法,叫做Seirios, 名为Seirios, 配有一个新奇的算法, 通过利用无线电物理层的独特性能, 使多个非过度通信频道同步化, 从而增加整个带宽。 通过利用物理层的原始和合金, Seirios 能够解决室内和室外环境中多个反射器的直接路径。 我们设计了一个Seirios 原型, 并评估其性能, 在100 m $\time 60米的户外区域, 和25 m $\ time 15 m米的室内区域化方法, 这显示Seirios 能够分别实现4.4米户外( 80 % 样本 < 6.4 m) 和2.4 m 室内系统( 80% 样本 < 6.1 m) 。 。结果显示, Seiririos 以本地级的本地级化系统生成系统产生42% 的本地级化为较不甚小的本地级化错误, 的网络化, 也显示为不同基线的本地级化, 的网络化系统为不同的地方级平地基级评估。