Tracer: Taming Anomalous Events with CRFID Tags for Trajectory Management
Mitigating anomalies are crucial for trajectory management in logistics and supply chain systems. Among variant devices for trace detection, computational radio frequency identification (CRFID) tags are promising to draw precise trajectory from the data reported by their accelerometers. However, ful...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2013-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/148353 |
| _version_ | 1827079830443655168 |
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| author | Rui Li Jinsong Han Zhi Wang Jizhong Zhao Yihong Gong Xiaobin Zhang |
| author_facet | Rui Li Jinsong Han Zhi Wang Jizhong Zhao Yihong Gong Xiaobin Zhang |
| author_sort | Rui Li |
| collection | DOAJ |
| description | Mitigating anomalies are crucial for trajectory management in logistics and supply chain systems. Among variant devices for trace detection, computational radio frequency identification (CRFID) tags are promising to draw precise trajectory from the data reported by their accelerometers. However, full coverage of the processing flow using RFID readers is usually cost inefficient, sometimes impractical. In this paper, we propose to employ CRFID tags as tagging devices and develop a working system, Tracer, for precise trajectory detection. Instead of covering the entire processing area, Tracer only deploys RFID readers in essential regions to detect the mishandling, loss, and other abnormal states of items. We design a tree-indexed Markov chain framework, which leverages statistical methods to enable fine-grained and dynamic trajectory management. Results from a preliminarily deployment on a real baggage handling system and trace-driven simulations demonstrate that Tracer is effective to detect the anomalous events with low cost and high accuracy. |
| first_indexed | 2024-03-12T06:47:24Z |
| format | Article |
| id | doaj.art-938bc55a4179422eb5b3b700d75eaf2a |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2025-03-20T02:51:48Z |
| publishDate | 2013-11-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-938bc55a4179422eb5b3b700d75eaf2a2024-10-03T07:26:35ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772013-11-01910.1155/2013/148353Tracer: Taming Anomalous Events with CRFID Tags for Trajectory ManagementRui Li0Jinsong Han1Zhi Wang2Jizhong Zhao3Yihong Gong4Xiaobin Zhang5 School of Electronic and Information Engineering, Xi'an Jiaotong University, China School of Electronic and Information Engineering, Xi'an Jiaotong University, China School of Electronic and Information Engineering, Xi'an Jiaotong University, China School of Electronic and Information Engineering, Xi'an Jiaotong University, China School of Electronic and Information Engineering, Xi'an Jiaotong University, China Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, Hong KongMitigating anomalies are crucial for trajectory management in logistics and supply chain systems. Among variant devices for trace detection, computational radio frequency identification (CRFID) tags are promising to draw precise trajectory from the data reported by their accelerometers. However, full coverage of the processing flow using RFID readers is usually cost inefficient, sometimes impractical. In this paper, we propose to employ CRFID tags as tagging devices and develop a working system, Tracer, for precise trajectory detection. Instead of covering the entire processing area, Tracer only deploys RFID readers in essential regions to detect the mishandling, loss, and other abnormal states of items. We design a tree-indexed Markov chain framework, which leverages statistical methods to enable fine-grained and dynamic trajectory management. Results from a preliminarily deployment on a real baggage handling system and trace-driven simulations demonstrate that Tracer is effective to detect the anomalous events with low cost and high accuracy.https://doi.org/10.1155/2013/148353 |
| spellingShingle | Rui Li Jinsong Han Zhi Wang Jizhong Zhao Yihong Gong Xiaobin Zhang Tracer: Taming Anomalous Events with CRFID Tags for Trajectory Management International Journal of Distributed Sensor Networks |
| title | Tracer: Taming Anomalous Events with CRFID Tags for Trajectory Management |
| title_full | Tracer: Taming Anomalous Events with CRFID Tags for Trajectory Management |
| title_fullStr | Tracer: Taming Anomalous Events with CRFID Tags for Trajectory Management |
| title_full_unstemmed | Tracer: Taming Anomalous Events with CRFID Tags for Trajectory Management |
| title_short | Tracer: Taming Anomalous Events with CRFID Tags for Trajectory Management |
| title_sort | tracer taming anomalous events with crfid tags for trajectory management |
| url | https://doi.org/10.1155/2013/148353 |
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