File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TMC.2018.2839112
- Scopus: eid_2-s2.0-85047616893
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Article: Efficient Locality Classification for Indoor Fingerprint-Based Systems
Title | Efficient Locality Classification for Indoor Fingerprint-Based Systems |
---|---|
Authors | |
Keywords | area classification context-awareness Fingerprinting inside/outside region decision locality classification |
Issue Date | 2019 |
Citation | IEEE Transactions on Mobile Computing, 2019, v. 18, n. 2, p. 290-304 How to Cite? |
Abstract | Locality classification is an important component to enable location-based services. It entails two sequential queries: 1) whether a target is within the site or not, i.e., inside/outside region decision, and 2) if so, which area in the region the target is located, i.e., area classification. Locality classification is hence more coarse-grained and efficient as compared with pinpointing the exact target location in the region. The classification problem is challenging, because fingerprints may not exist outside the region for training. Furthermore, the target may sample an incomplete RSSI vector due to, say, random signal noise, momentary occlusion, or scanning duration. The algorithm also has to be computationally efficient. We propose INOA, a scalable and practical locality classification overcoming the above challenges. INOA may serve as a plug-in before any fingerprint-based localization, and can be incrementally extended to cover new areas or regions for large-scale deployment. Its preprocessor cherry-picks only those discriminating access points, which greatly enhances computational efficiency and accuracy. By formulating a 'one-class' classifier using ensemble learning, INOA accurately decides whether the target is within the region or not. Extensive experimental trials in different sites validate the high efficiency and accuracy of INOA, without the need of full RSSI vectors collected at the target. |
Persistent Identifier | http://hdl.handle.net/10722/343262 |
ISSN | 2023 Impact Factor: 7.7 2023 SCImago Journal Rankings: 2.755 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chow, Ka Ho | - |
dc.contributor.author | He, Suining | - |
dc.contributor.author | Tan, Jiajie | - |
dc.contributor.author | Chan, S. H.Gary | - |
dc.date.accessioned | 2024-05-10T09:06:44Z | - |
dc.date.available | 2024-05-10T09:06:44Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Mobile Computing, 2019, v. 18, n. 2, p. 290-304 | - |
dc.identifier.issn | 1536-1233 | - |
dc.identifier.uri | http://hdl.handle.net/10722/343262 | - |
dc.description.abstract | Locality classification is an important component to enable location-based services. It entails two sequential queries: 1) whether a target is within the site or not, i.e., inside/outside region decision, and 2) if so, which area in the region the target is located, i.e., area classification. Locality classification is hence more coarse-grained and efficient as compared with pinpointing the exact target location in the region. The classification problem is challenging, because fingerprints may not exist outside the region for training. Furthermore, the target may sample an incomplete RSSI vector due to, say, random signal noise, momentary occlusion, or scanning duration. The algorithm also has to be computationally efficient. We propose INOA, a scalable and practical locality classification overcoming the above challenges. INOA may serve as a plug-in before any fingerprint-based localization, and can be incrementally extended to cover new areas or regions for large-scale deployment. Its preprocessor cherry-picks only those discriminating access points, which greatly enhances computational efficiency and accuracy. By formulating a 'one-class' classifier using ensemble learning, INOA accurately decides whether the target is within the region or not. Extensive experimental trials in different sites validate the high efficiency and accuracy of INOA, without the need of full RSSI vectors collected at the target. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Mobile Computing | - |
dc.subject | area classification | - |
dc.subject | context-awareness | - |
dc.subject | Fingerprinting | - |
dc.subject | inside/outside region decision | - |
dc.subject | locality classification | - |
dc.title | Efficient Locality Classification for Indoor Fingerprint-Based Systems | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TMC.2018.2839112 | - |
dc.identifier.scopus | eid_2-s2.0-85047616893 | - |
dc.identifier.volume | 18 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 290 | - |
dc.identifier.epage | 304 | - |
dc.identifier.eissn | 1558-0660 | - |