CrowdSensing: A crowd-sourcing based indoor navigation using RFID-based delay tolerant network. (June 2015)

• Record Type: Journal Article
• Authors: Ji, Hao; Xie, Lei; Wang, Chuyu; Yin, Yafeng; Lu, Sanglu
• Is Part Of: Journal of network and computer applications. Volume 52(2015)
• Page Start: 79
• Page End: 89
• Publication Date: 2015-06
• Subjects: Indoor localization -- Indoor navigation -- RFID -- Delay-tolerant network -- Crowd-sourcing; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2015.02.010
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: As a supporting technology for most pervasive applications, indoor localization and navigation has attracted extensive attention in recent years. Conventional solutions mainly leverage techniques like WiFi and cellular network to effectively locate the user for indoor localization and navigation. In this paper, we investigate the problem of indoor navigation by using the RFID-based delay tolerant network. Different from the previous work, we aim to efficiently locate and navigate to a specified mobile user who is continuously moving within the indoor environment. As the low-cost RFID tags are widely deployed inside the indoor environment and acting as landmarks, the mobile users can actively interrogate the surrounding tags with devices like smart phones and leave messages or traces to the tags. These messages or traces can be carried and forwarded to more tags by other mobile users. In this way, the RFID-based infrastructure forms a delay tolerant network. By using the crowd-sourcing technology in RFID-based delay tolerant network, we respectively propose a framework, namely CrowdSensing, to schedule the tasks and manage the resources in the network. We further propose a navigation algorithm to locate and navigate to the moving target. We verify the performance of proposed framework and navigation algorithm on mobility model built on real-world human traceset. Experiment results show that our solution can efficiently reduce the average searching time for indoor navigation.