GreenMap: Approximated Filtering Towards Energy-Aware Crowdsensing for Indoor Mapping

Abstract

Recently, mobile crowdsensing has become an appealing paradigm thanks to the ubiquitous presence of powerful mobile devices. Indoor mapping, as an example of crowdsensingdriven applications, is essential to provide many indoor locationbased services, such as emergency response, security, and tracking/navigation in large buildings. In this realm, 3D point clouds stand as an optimal data type which can be crowdsensed—using currently-available mobile devices, e.g. Google Tango, Microsoft Hololens and Apple ARKit—to generate floor plans with different levels of detail, i.e. 2D and 3D mapping. However, collecting such bulky data from "resources-limited" mobile devices can significantly harm their energy efficiency. To overcome this challenge, we introduce GreenMap, an energy-aware architectural framework for automatically mapping the interior spaces using crowdsensed point clouds with the support of structural information encoded in formal grammars. GreenMap reduces the energy overhead through projecting the point clouds to several filtration steps on the mobile devices. In this context, GreenMap leverages the potential of approximate computing to reduce the computational cost of data filtering while maintaining a satisfactory level of modeding accuracy. To this end, we propose two approximation strategies, namely DyPR and SuFFUSION. To demonstrate the effectiveness of GreenMap, we implemented a crowdsensing Android App to collect 3D point clouds from two different buildings. We show that GreenMap achieves significant energy savings of up to 67.8%, compared to the baseline methods, while generating comparable floor plans.