Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture

By Tolga Soyata, R. Muraleedharan, C. Funai, Minseok Kwon, Wendi Rabiner Heinzelman. International Symposium on Computers and Communications, 2012

435 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a278ebb84cf54c8c07a470e091dcb9b535922e14

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges (2015, background): “…such could architectures and discuss issues that impact long term medical data storage on the cloud. a) Hybrid Cloud/Cloudlet Architecture: Cloudlet is a limited resource computing and storage platform that eliminates the need to outsource resource intensive tasks to the enterprise cloud [43]–[45].”
Computation Offloading Toward Edge Computing (2019, background): “” Cloudlets are deployed at coffee shops, hospitals [56], and airports [57] to provide proximity computing, and they are now well known as typical edge infrastructures.”
Decentralized Computation Offloading Game for Mobile Cloud Computing (2014, methodology): “For the computation task, we use the face recognition application in [1], where the data size for the computation offloading Bn = 420 KB and the total number of CPU cycles Dn = 1000 Megacycles.”
Collaborative Computation Offloading for Multiaccess Edge Computing Over Fiber–Wireless Networks (2018, background): “they have one trait in common, i.e., computation intensive and high energy consumption [3]–[5].”
Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices (2015, background): “Cloud-Vision [50] extends the idea of code partitioning to server farms and aims to minimize the response time given heterogeneous communication latencies and server compute power.”
Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds (2017, methodology): “R ESOURCE-INTENSIVE mobile applications, such as e-Health, face recognition, natural language processing, interactive gaming, and augmented reality, are fast developing and increasingly outgrowing the limited capabilities of mobile devices [1].”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, methodology): “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].”
Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges (2013, background): “MOCHA leverages two partitioning algorithms: fixed and greedy.”
Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing (2015, methodology): “The CPU computational capability fmn of a mobile device usern is randomly assigned from the set{0.5, 0.8, 1.0} GHz to account for the heterogenous computing capability of mobile devices, and the computational capability allocated for a usern on the cloud isf cn = 10 GHz [2].”
Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing (2016, methodology): “As expected, SMDs are taken as the dominant future computing devices for supporting computationintensive applications, such as interactive gaming, image/video processing, e-commerce, and online social network services [1], [2].”