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 modeling 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.

MapSense

“…Similarly, there exist several other efforts which focused on modeling the indoor environment using 3D point clouds [1][2][3]15]. For example, we introduced GraMap [1] as a QoS-aware automatic indoor modeling method using crowd-sourced 3D point clouds.…”

“…GraMap leverages region growing segmentation and indoor grammars to generate highly-accurate 2D indoor maps. Later, we introduced GreenMap [15], as an extension of GraMap, to further reduce the energy overhead through preprocessing the point clouds on the mobile devices for the sake of reporting much less number of 3D points. Specifically, GreenMap leverages the concept of approximate computing to enable energy-aware processing of the 3D point clouds on "resources-constrained" mobile devices.…”

“…To find the overall best path, the Viterbi algorithm utilizes a back pointer ϕ to the predecessor that optimally provokes the current state, where ϕ t (i) = arg max j η t −1 (j) • P t (r i | r j ) . To simplify the process of searching the best sequence, we filter out paths whose accumulative length is greater than the length of the corresponding RLS line (lines [15][16][17][18][19]. The output of the room surface extraction component is an accurate representation of the floor plan.…”