In the present era of sustainable innovation, the circular economy paradigm dictates the optimal use and exploitation of existing finite resources. At the same time, the transition to smart infrastructures requires considerable investment in capital, resources and people. In this work, we present a general machine learning approach for offering indoor location awareness without the need to invest in additional and specialised hardware. We explore use cases where visitors equipped with their smart phone would interact with the available WiFi infrastructure to estimate their location, since the indoor requirement poses a limitation to standard GPS solutions. Results have shown that the proposed approach achieves a less than 2m accuracy and the model is resilient even in the case where a substantial number of BSSIDs are dropped.

翻译：在可持续创新的当下，循环经济范式决定了有限资源的最优利用与开发。与此同时，向智慧基础设施的转型需要大量资本、资源和人力投入。本文提出了一种通用机器学习方法，可在无需额外专用硬件投资的情况下实现室内定位感知。我们探索了访客利用智能手机与现有WiFi基础设施交互来估算自身位置的应用场景——这是因为室内环境对标准GPS方案构成了限制。实验结果表明，该方法可实现低于2米的定位精度，且即使在大量BSSID丢失的情况下，模型仍保持稳健性。