The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture
Abstract
Face recognition applications for airport security and surveillance can benefit from the collaborative coupling of mobile and cloud computing as they become widely available today. This paper discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The challenge lies with how to perform task partitioning from mobile devices to cloud and distribute compute load among cloud servers (cloudlet) to minimize the response time given diverse communication latencies and server compute powers. Our preliminary simulation results show that optimal task partitioning algorithms significantly affect response time with heterogeneous latencies and compute powers. Motivated by these results, we design, implement, and validate the basic functionalities of MOCHA as a proof-of-concept, and develop algorithms that minimize the overall response time for face recognition. Our experimental results demonstrate that high-powered cloudlets are technically feasible and indeed help reduce overall processing time when face recognition applications run on mobile devices using the cloud as the backend servers.
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“For example, face recognition systems have been deployed in building entrance, railway station, and airport, enabled by cloud/fog computing [100] or edge computing [101].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [100]
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Self-citation
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Cited by
Sorted by citations
Efficient multi-user computation offloading for mobile-edge cloud computing
Chen, X., Jiao, L., Li, W., & Fu, X. (2016) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2015.2487344 · Cited by 2,675
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(2016) · Cited by 844
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Self-citation
Metadata
- Authors
- Soyata, T., Muraleedharan, R., Funai, C., Kwon, M., & Heinzelman, W.
- Affiliations
- University of Rochester (US)
- Rochester Institute of Technology (US)
- Venue
- 2012 IEEE Symposium on Computers and Communications (ISCC)
- Publisher
- IEEE
- Published
- 2012-07
- Pages
- 000059-000066
- ISSN
- 1530-1346 (ISSN-L)
- OpenAlex
- W2136877695
- Cited by
- 420
- References
- 25
- Citations by year
- 2025: 5 · 2024: 15 · 2023: 17 · 2022: 24 · 2021: 30 · 2020: 54 · 2019: 51 · 2018: 52 · 2017: 45 · 2016: 39 · 2015: 46 · 2014: 30 · 2013: 10 · 2012: 2
- FWCI
- 23.61
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Symposium on Computers and Communications (ISCC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Keywords
- face recognition, mobile cloud computing, cloudlets, task decomposition, load balancing, response time minimization, airport security, surveillance, real-time processing
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