The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
ParkSense: a smartphone based sensing system for on-street parking
Abstract
Studies of automotive traffic have shown that on average 30% of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city is a considerable investment. In this paper, we present ParkSense, a smartphone based sensing system that detects if a driver has vacated a parking spot. ParkSense leverages the ubiquitous Wi-Fi beacons in urban areas for sensing unparking events. It utilizes a robust Wi-Fi signature matching approach to detect driver's return to the parked vehicle. Moreover, it uses a novel approach based on the rate of change of Wi-Fi beacons to sense if the user has started driving. We show that the rate of change of the observed beacons is highly correlated with actual user speed and is a good indicator of whether a user is in a vehicle. Through empirical evaluation, we demonstrate that our approach has a significantly smaller energy footprint than traditional location sensors like GPS and Wi-Fi based positioning while still maintaining sufficient accuracy.
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Cites
RADAR: an in-building RF-based user location and tracking system
Bahl, P., & Padmanabhan, V. N. (2002) · Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064) · doi:10.1109/infcom.2000.832252 · Cited by 8,380
Cited by
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914030 · Cited by 594
Grassi, G., Jamieson, K., Bahl, P., & Pau, G. (2017) · Proceedings of the Second ACM/IEEE Symposium on Edge Computing · doi:10.1145/3132211.3134452 · Cited by 73
Smartphone-based real time vehicle tracking in indoor parking structures
Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., & Luo, G. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2684167 · Cited by 48
Parkus: a novel vehicle parking detection system
Carnelli, P., Yeh, J., Sooriyabandara, M., & Khan, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i2.19090 · Cited by 27
Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study
El hafidy, A., Rachad, T., Idri, A., & Zellou, A. (2021) · Mobile Information Systems · doi:10.1155/2021/6677075 · Cited by 21
Metadata
- Authors
- Nawaz, S., Efstratiou, C., & Mascolo, C.
- Venue
- Proceedings of the 19th annual international conference on Mobile computing & networking - MobiCom '13
- Publisher
- ACM Press
- Published
- 2013
- Pages
- 75
- OpenAlex
- W2026414543
- Cited by
- 158
- References
- 1
- Type
- conference abstract
- Language
- en
- Open access
- closed
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- Nawaz, S., Efstratiou, C., & Mascolo, C. (2013). ParkSense: a smartphone based sensing system for on-street parking. Proceedings of the 19th annual international conference on Mobile computing & networking - MobiCom '13. https://doi.org/10.1145/2500423.2500438
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
