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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
The case for vm-based cloudlets in mobile computing
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.82 · Cited by 3,660
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,458
Robust real-time face detection
Viola, P., & Jones, M. (2005) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937709 · Cited by 898
Energy efficiency of mobile clients in cloud computing
Miettinen, A., & Nurminen, J. K. (2010) · Cited by 759
(2008) · Apress eBooks · doi:10.1007/978-1-4302-1832-6_1 · Cited by 610
A virtual cloud computing provider for mobile devices
Huerta‐Cánepa, G., & Lee, D. (2010) · doi:10.1145/1810931.1810937 · Cited by 452
Hyrax: Cloud Computing on Mobile Devices using MapReduce
Marinelli, E. (2009) · Cited by 400
Programming Massively Parallel Processors
(2017) · Elsevier eBooks · doi:10.1016/c2015-0-02431-5 · Cited by 374
Flagged first, then by citations
Research works printed but not linked: 12
- cho, 2009, Fpgas, Elseiver Science, 1
- bradski, 2008, OpenCV Computer Vision with OpenCV Library
- greene, 2011, Amazon Some Data Lost in Cloud Outage Is Not Recoverables
- keller, 2011, The Atlantic Cloud-powered Facial Recognition Is Terrifying
- 2008, Hadoop
- huerta-canepa, 2010, Ad hoc virtual cloud computing providers for mobile devices, Proceedings of ACM Mobile Cloud Computing and Services (MCS)
- 0, Windows Azure
- 0, Amazon Web Services (AWS)
- 0, Atom In-Order Processor
- 2011, Mobile Assistance Using Infrastructure (MAUI)
- xiao, 2011, CasCap: Cloudassisted context-aware power management for mobile devices, Proceedings of ACM Mobile Cloud Computing and Services (MCS)
- 2011, GeForce GT 500 Series
Cited by
Sorted by citations
Items 301–400 of 419
SmartFace: Efficient face detection on smartphones for wireless on-demand emergency networks
Lampe, P., Baumgärtner, L., Steinmetz, R., & Freisleben, B. (2017) · doi:10.1109/ict.2017.7998256 · Cited by 5
Mobility Prediction-Based Service Scheduling Optimization Algorithm in Cloudlets
Shi, L., Fu, X., & Li, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68542-7_53 · Cited by 5
LACE: Low-Cost Access Control Based on Edge Computing for Smart Buildings
Huang, H., Tan, H., Xu, X., Zhang, J., & Zhao, Z. (2023) · Electronics · doi:10.3390/electronics12020412 · Cited by 4
Digital twin evolution, application areas and enabling technology
Sama, N. U., Jhanjhi, N. Z., Humayun, M., & Rahman, A. U. (2024) · AIP conference proceedings · doi:10.1063/5.0168361 · Cited by 5
Computing Offloading for RIS-Aided Internet of Everything: A Cybertwin Version
Gu, X., Zhang, G., Duan, W., Dang, S., Wen, M., & Ho, P. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3370557 · Cited by 5
Effective task scheduling in proximate mobile device based communication systems
Wu, L., Du, X., Zhang, H., Yu, W., & Wang, C. (2015) · International Conference on Communications · doi:10.1109/icc.2015.7248867 · Cited by 5
Cloud-assisted data management in wireless body area networks
Quwaider, M., & Jararweh, Y. (2016) · International Journal of Computational Science and Engineering · doi:10.1504/ijcse.2017.081173 · Cited by 5
A Cloud-Based Big Data System to Support Visually Impaired People
Temuçin, H., Keçeli, A. S., Kaya, A., Yalıç, H. Y., & Teki̇nerdoğan, B. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813314-9.00001-3 · Cited by 5
Joint Optimization of Offloading and Communication Resources in Mobile Edge Computing
Du, C., Chen, Y., Li, Z., & Rudolph, G. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9003099 · Cited by 5
Conceptualizing a Real-Time Remote Cardiac Health Monitoring System
Page, A., Hassanalieragh, M., Soyata, T., Aktaş, M. K., Kantarcı, B., & Andreescu, S. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-0571-6.ch007 · Cited by 5
Self-citationEdge/Cloud-Assisted Feature Extraction in IoT Devices
Ding, C., Li, Y., & Wang, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3184839 · Cited by 5
Performance based Analysis of Cloudlet Architectures in Mobile Cloud Computing
Jaiswal, A. S., Thakare, V. M., & Sherekar, S. S. (2015) · Cited by 5
Kanak, A. (2018) · IET Biometrics · doi:10.1049/iet-bmt.2018.5067 · Cited by 5
A near-optimal cloud offloading under multi-user multi-radio environments
Feng, G., Lv, H., Li, B., Wang, C., Lv, H., & Wang, H. (2018) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-018-0693-6 · Cited by 5
Measurement-Based Power Optimization Technique for OpenCV on Heterogeneous Multicore Processor
Jung, H., Koo, K., & Yang, H. (2019) · Symmetry · doi:10.3390/sym11121488 · Cited by 5
Edge Computing in the Extreme for Sustainability
Banerjee, S., Klingensmith, N., Liu, P., & Sridhar, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67235-9_8 · Cited by 5
Mobile Cloud Computing: the State of Art, Application Scenarios and Challenges
Hao, J., Xian, M., Wang, H., Tang, F., & Xiao, P. (2018) · International Conference on Computational Intelligence & Communication Technology (CICT) · doi:10.1109/ciact.2018.8480365 · Cited by 5
Real-time control in industrial IoT
Didic, A., & Nikolaidis, P. (2015) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 5
Design and analysis of privacy-preserving medical cloud computing systems
Kocabaş, Ö. (2016) · UR Research (University of Rochester) · Cited by 5
Seamless Live Virtual Machine Migration for Cloudlet Users with Multipath TCP
Teka, F. (2014) · doi:10.22215/etd/2014-10449 · Cited by 5
A Cloud Face Recognition System using A New Optimal Local Binary Pattern
Dahmouni, A., Moutaouakil, K. E., & Satori, K. (2017) · International Conference on Big Data, Cloud and Applications (BDCA) · doi:10.1145/3090354.3090394 · Cited by 4
A Survey on Task Scheduling Schemes in Mobile Edge Computing
Jia, M., Fan, Y., & Cai, Y. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-0852-1_33 · Cited by 4
Theoretical Foundation and GPU Implementation of Face Recognition
Dixon, W., Powers, N., Yang, S. S., & Soyata, T. (2015) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-4666-8662-5.ch011 · Cited by 4
Self-citationLightweight Multitenancy at the Network’s Extreme Edge
Liu, P., Hartung, L., & Banerjee, S. (2017) · Computer · doi:10.1109/mc.2017.3641633 · Cited by 4
A cloud-based scene recognition framework for in-home assistive robots
Menicatti, R., & Sgorbissa, A. (2017) · IEEE RO-MAN · doi:10.1109/roman.2017.8172472 · Cited by 4
Design and development of a parallelized algorithm for face recognition in mobile cloud environment
Bhatt, K. N., Dessai, S. S. N., & Yerragudi, V. S. (2021) · International Journal of Reconfigurable and Embedded Systems (IJRES) · doi:10.11591/ijres.v10.i1.pp47-55 · Cited by 4
Latency and quality-aware task offloading in multi-node next generation RANs
Younis, A., Qiu, B., & Pompili, D. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.11.026 · Cited by 4
An Energy-Efficient, Time-Constrained Scheduling Scheme in local mobile cloud
Shi, T. (2020) · Digital Scholarship - UNLV (University of Nevada Reno) · doi:10.34917/5836160 · Cited by 4
Wang, Z., Feng, X., Zhu, H., & Liu, C. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.04.040 · Cited by 4
Alling, A., Powers, N., & Soyata, T. (2015) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-4666-8853-7.ch020 · Cited by 4
Self-citationBandit Framework for Systematic Learning in Wireless Video-Based Face Recognition
Atan, O., Andreopoulos, Y., Tekin, C., & van der Schaar, M. (2014) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2014.2330799 · Cited by 4
Accelerating Mobile-Cloud Computing
Soyata, T., Ba, H., Heinzelman, W., Kwon, M., & Shi, J. (2014) · IGI Global eBooks · doi:10.4018/978-1-4666-6539-2.ch090 · Cited by 4
Self-citationEnergy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Gao, C., Kumar, N., & Easwaran, A. (2024) · IEEE Real-Time Systems Symposium (RTSS) · doi:10.1109/rtss62706.2024.00012 · Cited by 3
Instant Social Networking with Startup Time Minimization Based on Mobile Cloud Computing
Chen, L., Ho, Y.-F., & Tsai, M. (2018) · Sustainability · doi:10.3390/su10041195 · Cited by 3
Edge-Cloud Resource Trade Collaboration scheme in Mobile Edge Computing
Wei, W., & Zhang, Y. (2020) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2020-fall49728.2020.9348637 · Cited by 3
Offloading Strategies for Mobile Edge Computing Based on Multi-Attribute Preferences in Smart Grids
Wang, W., Yao, J., Zheng, W., & Shao, W. (2022) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec53115.2022.9734547 · Cited by 3
Efficient Resource Allocation to Improve the Quality of Services in Cloud Computing
S, S. F. A., Priya, A. A., Dinakaran, S. S., Sundari, S., & Saran Raj, S. (2023) · International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) · doi:10.1109/rmkmate59243.2023.10369116 · Cited by 3
Kossoski, C., Simão, J. M., & Lopes, H. S. (2024) · Applied Sciences · doi:10.3390/app14093566 · Cited by 3
Joint Offloading and Frequency Scaling Technology for Mobile Edge Computing
Liang, J., Liu, C., Tan, G., & Yang, L. (2019) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2019.00283 · Cited by 3
Deep reinforcement learning for partial offloading with reliability guarantees
Li, J., Chen, Z., & Liu, X. (2021) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00143 · Cited by 3
Multi-feature-based robust face detection and coarse alignment method via multiple kernel learning
Sun, B., Zhang, D., He, J. Y. D., Yu, L., & Wu, X. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2194254 · Cited by 3
A large-scale mobile facial recognition system using embedded GPUs
El-Mahdy, A., & Elsersy, R. (2014) · High Performance Computing Symposium · doi:10.5555/2663510.2663533 · Cited by 3
Powers, N., & Soyata, T. (2015) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-4666-8662-5.ch006 · Cited by 3
Self-citationFaceDate: A Mobile Cloud Computing App for People Matching
Neog, P., Debnath, H., Shan, J., Paiker, N. R., Gehani, N., Curtmola, R., Ding, X., & Borcea, C. (2017) · International Conference on Body Area Networks · doi:10.4108/eai.15-12-2016.2267654 · Cited by 3
Li, J., Zhang, W., Cheng, P., Wang, Y., & Du, X. (2022) · International Conference on Information Science and Technology · doi:10.1109/icist55546.2022.9926934 · Cited by 3
Dynamic Source ACO algorithm for energy-aware cloudlet-based mobile cloud computing architecture
Kushal, A. R., Raj, P., Prajapati, N., Prajapati, T., & Anjali, T. (2019) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2019.8819838 · Cited by 3
Software Architecture for Mobile Cloud Computing Systems
Alreshidi, A., Ahmad, A., Altamimi, A. B., Sultan, K., & Mehmood, R. (2019) · Future Internet · doi:10.3390/fi11110238 · Cited by 3
Mean-Field-Type Game for Multi-Access Edge Computing Networks
Banez, R. A., Li, L., Yang, C., & Han, Z. (2021) · Wireless networks · doi:10.1007/978-3-030-86905-2_6 · Cited by 2
Benchmarking leading-edge mobile devices for data-intensive distributed mobile cloud applications
Naqvi, N. Z., Vansteenkiste-Muylle, T., & Berbers, Y. (2015) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2015.7405493 · Cited by 2
Job Scheduling with Adaptable Computing Levels for Edge Computing
Jiang, H., & Wu, W. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05054-2_22 · Cited by 2
Task Offloading and Resource Allocation with Privacy Constraints in End-Edge-Cloud Environment
Zhu, X., Sun, W., & Li, X. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-2385-4_16 · Cited by 2
Local Decoding in Distributed Approximate Computing
Yuan, D., Guo, T., Huang, Z., & Jin, S. (2024) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit57864.2024.10619658 · Cited by 2
Fine-Grained Task-Dependency Offloading in Mobile Cloud Computing
Pan, S., Liu, C., Zeng, D., Yao, H. L., & Qian, Z. (2018) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld.2018.00173 · Cited by 2
Performance and Correctness of Mobile Cloud Computing Systems: Taxonomy and Open Challenges
Fakhfakh, F. (2019) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2019.8766588 · Cited by 2
A Probabilistic Offloading Approach in Mobile Edge Computing
Bista, B. B., Wang, J., & Takata, T. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-33506-9_24 · Cited by 2
A scheduler for mobile cloud based on weighted metrics and dynamic context evaluation
Silva, F. A., Maciel, P., Filho, G., & Junior, R. D. S. M. (2015) · ACM Symposium on Applied Computing · doi:10.1145/2695664.2695921 · Cited by 2
Automated human behavioral analysis framework using facial feature extraction and machine learning
Smirnov, D., Banger, S., Davis, S. R., Muraleedharan, R., & Ramachandran, R. P. (2013) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2013.6810420 · Cited by 2
Self-citationMonteiro, C. E., & Trevelin, L. C. (2015) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2015.7507131 · Cited by 2
Towards cloudlet-based mobile cloud gaming
Wu, R. Z. (2016) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic.2016.7949550 · Cited by 2
Mobile-Cloudlet Face Recognition: Two Different Approaches
Praseetha, V. M. (2018) · Journal of Computers · doi:10.17706/jcp.13.1.116-129 · Cited by 2
5G Smart Grid Network Slice Backup Method Based on the Quality of Service
Yao, J., Yu, Y., Chen, X., & Zhou, P. (2022) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec53115.2022.9734709 · Cited by 2
A Short Survey on Decision Making for Task Migrations in Mobile Cloud Environments
Zhang, W., Tan, S., & Hansen, K. (2014) · International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) · doi:10.1109/iiki.2014.20 · Cited by 2
A Scheduling Method for Tasks and Services in IIoT Multi-Cloud Environments
Zhang, W., Kosta, S., & Mogensen, P. (2023) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot58021.2023.00056 · Cited by 2
Intelligent Item Searching With Gesture-Based Interfaces Through Mobile Cloud Computing
Chen, L., Li, Y.-E., Liu, J., Chen, H., & Tsai, M. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2858788 · Cited by 2
Cloudlet-based Augmentation of Mobile Devices
Raza, A., Haq, I. U., & Shaukat, U. (2018) · International Conference on Emerging Technologies · doi:10.1109/icet.2018.8603627 · Cited by 2
Facial Recognition Adaptation as Biometric Authentication for Intelligent Door Locking System
Mohamed, A. S. A., Wahab, M. N. A., Krishnan, S. R., & Arasu, D. B. L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34032-2_24 · Cited by 2
Homomorphic Encryption-Based Cloud Privacy-Preserving in Remote ECG Monitoring and Surveillance
Seethalakshmi, V., Suganyadevi, S., Nithya, S., Rani, K. S. S., & Basavaraj, G. (2023) · Springer eBooks · doi:10.1007/978-3-031-35535-6_6 · Cited by 1
Intelligence Cloud-Based Image Recognition Service
Li, W.-S., & Kao, J.-Y. (2017) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-60717-7_47 · Cited by 1
Sun, Q., Guo, P., & Zhang, C. (2018) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/428/1/012024 · Cited by 1
Smart Lab with Automated Access
Vishnoi, M., Saxena, K., Anas, M., Singh, S. K., & Agarwal, N. (2020) · International Journal of Science and Management Studies (IJSMS) · doi:10.51386/25815946/ijsms-v3i3p108 · Cited by 1
Cooperative Cloud‐Edge Feature Extraction Architecture for Mobile Image Retrieval
He, C., & Ma, G. (2021) · Complexity · doi:10.1155/2021/7937922 · Cited by 1
Operational Cost of Running Real-Time Mobile Cloud Applications
Kocabaş, Ö., Gyampoh‐Vidogah, R., & Soyata, T. (2015) · IGI Global eBooks · doi:10.4018/978-1-4666-9466-8.ch049 · Cited by 1
Self-citationSecure Health Monitoring in the Cloud Using Homomorphic Encryption
Ames, S., Venkitasubramaniam, M., Page, A., Kocabaş, Ö., & Soyata, T. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-9863-3.ch004 · Cited by 1
Self-citationModelling the offload of AI Tasks in Mobile Clouds
Chen, Z., & He, L. (2022) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn55119.2022.9829710 · Cited by 1
Total Cost of Ownership Perspective of Cloud vs Edge Deployments of IoT Applications
Nikolaou, P., Sazeides, Y., Lampropulos, A., Guilhot, D. A., Bartoli, A., Papadimitriou, G., Chatzidimitriou, A., Gizopoulos, D., Tovletoglou, K., Mukhanov, L., et al. (2022) · Springer eBooks · doi:10.1007/978-3-030-74536-3_6 · Cited by 1
Yamin, I., Yang, G., BAKALA, M., Yamin, M. A., & Masood, U. (2024) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.e7970.12050124 · Cited by 1
Multi-Cloud Containerized Service Scheduling Optimizing Computation and Communication
Zhang, W., Kosta, S., & Mogensen, P. (2024) · Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) · doi:10.1109/icin60470.2024.10494434 · Cited by 1
Na, M., Zhang, G., Ladi, Z., & Wang, L. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-1087-4_29 · Cited by 1
Optimizing Digital Twin Construction in Smart Factories: A Latency-Minimized MEC Approach
Chou, S.-F., & Pan, J.-J. (2025) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom65615.2025.00058 · Cited by 1
Karaoglu, B., Numanoǧlu, T., Tavlı, B., & Heinzelman, W. (2015) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-4666-8662-5.ch002 · Cited by 1
Self-citationEnhancing the storage of mobile nodes through ad hoc storage as a service in MANETs
Muralidhar, K., & Madhavi, K. (2020) · International Journal of Communication Systems · doi:10.1002/dac.4297 · Cited by 1
Survey on Computation Offloading Schemes in Resource-Constrained Mobile Edge Computing
Sun, H., Fan, Y., Yuan, S., & Cai, Y. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-3150-4_36 · Cited by 1
Measurement and Analysis of Networking Performance in Virtualised Environments
Chauhan, M. (2014) · Aaltodoc (Aalto University) · Cited by 1
Enabling energy efficient sensing and computing systems
Ba, H., & Heinzelman, W. (2015) · UR Research (University of Rochester)
Self-citationAn online weight-based clustering algorithm for faces selection
Li, Q., & Huang, D. (2015) · Advances in engineering research/Advances in Engineering Research · doi:10.2991/asei-15.2015.6
Distributed matching mechanism for resource sharing in mobile ad hoc cloud
Jin, L., & Tang, L. (2017) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2017.8171055
CELLCLOUD: TOWARDS A COST EFFECTIVE FORMATION OF MOBILE CLOUD BASED ON BIDDING INCENTIVE
Noor, S., Hasan, R., & Haque, M. E. (2015) · Services Transactions on Cloud Computing · doi:10.29268/stcc.2015.0001
Cloudlet Vision: Examined Cloudlets Performance while end-users Use Multimedia Contents
Alsaleh, A., & Alshaya, S. A. (2018) · Indian Journal of Science and Technology · doi:10.17485/ijst/2018/v11i19/122602
Smart chained-factories architecture of industry 4.0: A preliminary study
Muliawan, I. W. (2019) · AIP conference proceedings · doi:10.1063/1.5095270
Minimizing Execution Cost of User Application Codes in Mobile Device Cloud
Saha, S., Habib, M. A., Sarkar, S., Razzaque, M. A., & Rahman, M. M. (2019) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti47673.2019.9067996
Quality of Service (QoS)-aware Application Management in Fog Computing Environments
Mahmud, R. (2020) · Minerva Access (University of Melbourne)
MEC vs MCC: análise do desempenho de aplicações interativas e de tempo real
do Porto, I. S. D. E., Soares, M., Pinto, P., do Castelo, I. P. D. V., Mamede, J., & da Maia, I. U. (2020) · RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação · doi:10.17013/risti.37.116-131
Exploring hardware accelerator offload for the Internet of Things
Cooke, R. A., & Fahmy, S. A. (2020) · it - Information Technology · doi:10.1515/itit-2020-0017
A Decision Support Framework to Enhance Performance in Resource Constrained Devices
Ghosh, S., & Agrawal, D. P. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-33-6385-4_32
A Novel Service Composition Approach for Offloading in Mobile Edge Computing
Liu, D., Krishna, N., & Nayak, A. R. (2020) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn50589.2020.00131
C. Anuradha, M. P. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i6.5704
Performance Improvement of Mobile Device Using Cloud Platform
Sindhu, K., & Guruprasad, H. S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-1395-1_25
The optimization game: Mobility and Edge computing in dense networks
Ghosh, S., & Agrawal, D. P. (2021) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot51360.2021.9595890
Accelerating Task Completion in IIoT Based Smart Factory Through Optimal Restart
Wang, Q., Gupta, A., & Chan, H.-L. (2021) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon48115.2021.9589234
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
