On scheduling of real-time sensing tasks in mobile crowd sensing

Abstract

In the area of Wireless Local Area Network (WLAN) based indoor localization, the Received Signal Strength (RSS) fingerprinting based localization technique has been studied extensively. Site survey phase in RSS fingerprinting is always considered to be time-consuming and labor intensive. To solve this problem, we propose a novel Indoor Mapping and Localization Using RSS Solely (IMLours) approach, which utilizes the spectral clustered time-stamped WLAN RSS data to characterize environmental layout, as well as conduct target localization. First of all, we use the off-the-shelf smartphones to sporadically record a batch of WLAN RSS data in indoor environment. Second, spectral clustering is applied to classify the RSS data in each sequence into different clusters. The clusters are then used to construct the logic graphs. Third, we do the mapping from logic graphs into ground-truth graph. Finally, based on the extensive experiments conducted in a real WLAN indoor environment, our proposed IMLours approach is proved to achieve satisfactory localization accuracy.