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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Face recognition systems have been deployed in building entrances, railway stations, and airports, enabled by cloud/fog computing or edge computing.
“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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Cloud‐Based Privacy‐Preserving Remote ECG Monitoring and Surveillance
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Self-citationJoint offloading and scheduling decisions for DAG applications in mobile edge computing
Liang, J., Li, K., Liu, C., & Li, K. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.081 · Cited by 69 · see citing works
Utilizing Homomorphic Encryption to Implement Secure and Private Medical Cloud Computing
Kocabaş, Ö., & Soyata, T. (2015) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2015.78 · Cited by 68 · see citing works
Self-citationPham, X., Nguyen, T.-D., Nguyen, V., & Huh, E. (2019) · Symmetry · doi:10.3390/sym11010058 · Cited by 66 · see citing works
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Joint Channel Allocation and Resource Management for Stochastic Computation Offloading in MEC
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Game-theoretic Analysis of Computation Offloading for Cloudlet-based Mobile Cloud Computing
Ma, X., Lin, C., Xiang, X., & Chen, C. (2015) · ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) · doi:10.1145/2811587.2811598 · Cited by 67 · see citing works
Assessment of cloud-based health monitoring using Homomorphic Encryption
Kocabaş, Ö., Soyata, T., Couderc, J., Aktaş, M. K., Xia, J., & Huang, M. C. Y. (2013) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd.2013.6657078 · Cited by 66 · see citing works
Self-citationLarge Scale Cloudlets Deployment for Efficient Mobile Cloud Computing
Tawalbeh, L., Jararweh, Y., Ababneh, F., & Dosari, F. (2015) · Journal of Networks · doi:10.4304/jnw.10.01.70-76 · Cited by 66 · see citing works
An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds
Shi, T., Yang, M., Li, X., Lei, Q., & Jiang, Y. (2015) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2015.07.005 · Cited by 63 · see citing works
Layer Dependency-Aware Learning Scheduling Algorithms for Containers in Mobile Edge Computing
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QoE-aware user allocation in edge computing systems with dynamic QoS
Lai, P., He, Q., Cui, G., Xia, X., Abdelrazek, M., Chen, F., Hosking, J., Grundy, J., & Yang, Y. J. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.06.029 · Cited by 63 · see citing works
Ragona, C., Granelli, F., Fiandrino, C., Dzmitry Kliazovich, A., & Bouvry, P. (2015) · Globecom · doi:10.1109/glocom.2015.7417039 · Cited by 62 · see citing works
A unified face identification and resolution scheme using cloud computing in Internet of Things
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A Cloud-Guided Feature Extraction Approach for Image Retrieval in Mobile Edge Computing
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Delay-Aware Cooperative Task Offloading for Multi-UAV Enabled Edge-Cloud Computing
Bai, Z., Lin, Y., Cao, Y., & Wang, W. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3232375 · Cited by 60 · see citing works
- 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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