Start from Scratch: A Crowdsourcing-Based Data Fusion Approach to Support Location-Aware Applications

Abstract

As one of the most important breakthroughs for modern transportation, the indoor location-based technology has been gradually penetrating into our daily lives and underlines the foundation of the Internet of Things (IoT). To improve the positioning accuracy and efficiency, crowdsourcing has been widely applied in indoor localization in recent years. However, the crowdsourced data can hardly be fused easily to enable usable applications for the reason that the data are collected by different users, in different locations, at different times, with different noises and distortions. Although different data fusing methods have been implemented in different crowdsourcing services, we find that they may not fully leverage the data collected from multiple dimensions that can potentially lead to a better fusion results. In order to address this problem, we propose a more general solution, which can fuse the multi-dimensional crowdsourced data together and align them with the consistent time and location stamps, by using the features of the sensory data only, and thus build high quality crowdsourcing services from the raw data samplings collected from the environment. Finally, we conduct extensive evaluations and experiments using different commercial devices to validate the effectiveness of the method we proposed.

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

“…In our experiment, it is not uncommon to collect the fingerprinting datasets in days. To alleviate this issue, one can borrow the concept of crowdsourcing [65], [66] which exploits pervasive (mmWave) WiFi devices to collect training samples and labels with unconscious cooperation among volunteering users [67], and adaptive sampling which exploits adaptivity to identify highly informative fingerprinting positions and, hence, reduces the amount of labeled samples.…”

Multi-Band Wi-Fi Sensing with Matched Feature Granularity

“…Like the traditional fingerprinting-based Wi-Fi sensing method, we follow the standard procedure by collecting both Albeit simple, the offline fingerprinting phase is time-and manpower-consuming [63], [64]. To label the data, one has to associate both channel measurements with the ground-truth labels, in the form of pose gesture, occupancy pattern, or user location.…”

“…As stated previously, collecting large-scale labeled training data is time-and manpower-consuming [63], [64]. Crowdsourcing may ease the required resources but accessing the ground-truth labels (e.g., user locations and orientation) may still be limited due to privacy concerns.…”

A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

“…A few other notable application domains empowered by SPS include urban search and rescue (Dubey 2019), smart healthcare (Chen et al, 2018), simultaneous localization and mapping (Jiang et al, 2019), human mobility modeling (Noulas et al, 2012), and anomaly detection (Lyu et al, 2016). Figure 3 highlights several recent examples of representative SPS applications which encompass: (i) anomalistic crowd detection with social media and surveillance cameras; (ii) social vehicular sensor network (S-VSN)-based plate recognition; (iii) fire monitoring with UAV and crowdsensing; (iv) road damage detection with satellites and social media; (v) crime reporting with wireless sensor networks (WSN) and crowdsensing; and (vi) contact tracing with social media and wearable sensors.…”