Abstract
Due to the wide deployment of wireless local area networks (WLAN), received signal strength (RSS)-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL) by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM). Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR) algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization.
Highlights
Nowadays, people spend more than 80% of their time in the indoor environment, where the signal from the Global Positioning System (GPS) is generally difficult to receive
Different from the existing work in the literature, we propose a novel indoor mapping and localization approach, namely the page rank-based indoor mapping and localization (PRIMAL), which is independent of location fingerprinting and motion sensing
After the mobility and signal graphs are obtained, we propose to use the page rank (PR) algorithm to construct the mapping from the signal graph into the mobility graph with the purpose of investigating the relation between the physical layout and signal distribution in the target environment
Summary
People spend more than 80% of their time in the indoor environment, where the signal from the Global Positioning System (GPS) is generally difficult to receive. In this circumstance, many indoor localization systems are proposed to guarantee the performance of a variety of location-based services (LBSs), like the guidance of shopping routes, security and healthcare for the elderly, and asset management in warehouses and modern buildings. The RSSs at a batch of pre-calibrated reference points (RPs) with known physical coordinates are collected and stored as the location fingerprints into the radio map.
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