Paper Info
Title
SWIM: Speed-Aware WiFi-Based Passive Indoor Localization for Mobile Ship Environment
Abstract
Accurate and pervasive device-free indoor localization with meter-level resolution is critical for large cruise and passenger ships due to safety-critical rescue and evacuation requirements when accidents occur. However, existing localization techniques would severely suffer on ships because of their unique mobility characteristics. In this paper, we take the first attempt to build a ubiquitous passive localization system using WiFi fingerprints for the mobile ship environment. By conducting extensive experiments and measurements during several cruise trips, we identified a major influence factor on the fingerprints in the mobile environment: varying the ship speeds may significantly change the patterns of fingerprints at runtime. Since it may be too expensive to identify the fingerprints associated with different speeds, we propose an efficient localization method, namely SWIM, which calibrates the fingerprints from only a single-speed scenario to multiple-speed scenarios using a signal reconstruction analysis. SWIM is designed to learn the predictive fingerprint variation introduced by environmental speed changes and reconstruct the original fingerprints to adapt to the runtime speed scenarios. We have implemented and extensively evaluated SWIM on actual cruise ships. Experimental results demonstrate that SWIM improves localization accuracy from 63.2 to 82.9 percent, while reducing the overall system deployment cost by 87 percent.
Year
DOI
Venue
2021
10.1109/TMC.2019.2947667
IEEE Transactions on Mobile Computing
Keywords
DocType
Volume
Channel state information (CSI),device-free indoor localization,Mobile ship environment,WiFi
Journal
20
Issue
ISSN
Citations 
2
1536-1233
1
PageRank 
References 
Authors
0.35
0
6
Name
Order
Citations
PageRank
Mozi Chen111.02
Kezhong Liu211.02
Jie Ma3259.21
Yu Gu410.35
Zheng Dong5519.62
Cong Liu678056.17