Access the full text.
Sign up today, get DeepDyve free for 14 days.
(2020)
Decentralized computation of floading for multi-user mobile edge computing: a deep reinforcement learning approach
Xuan Li, Jin Li, Siu-keung Yiu, Chong-zhi Gao, Jinbo Xiong (2019)
Privacy-preserving edge-assisted image retrieval and classification in IoTFrontiers of Computer Science, 13
Joseph Azar, A. Makhoul, M. Barhamgi, R. Couturier (2019)
An energy efficient IoT data compression approach for edge machine learningFuture Gener. Comput. Syst., 96
Vahid Mirjalili, S. Raschka, A. Namboodiri, A. Ross (2017)
Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images2018 International Conference on Biometrics (ICB)
Thorsten Wittkopp, Alexander Acker (2021)
Decentralized Federated Learning Preserves Model and Data Privacy
Jian-Qiang Hu, Wei Liang, Zhiyong Zeng, Yong Xie, Jianxun Yang (2019)
A framework for Fog-assisted healthcare monitoringComput. Sci. Inf. Syst., 16
I. Azimi, Janne Takalo-Mattila, A. Anzanpour, A. Rahmani, J. Soininen, P. Liljeberg (2018)
Empowering Healthcare IoT Systems with Hierarchical Edge-Based Deep Learning2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
Marcin Hoffmann, P. Kryszkiewicz, G. Koudouridis (2020)
Modeling of Real Time Kinematics localization error for use in 5G networksEURASIP Journal on Wireless Communications and Networking, 2020
Olivia Choudhury, A. Gkoulalas-Divanis, T. Salonidis, I. Sylla, Yoonyoung Park, Grace Hsu, Amar Das (2019)
Differential Privacy-enabled Federated Learning for Sensitive Health DataArXiv, abs/1910.02578
Tao Han, Lijuan Zhang, Sandeep Pirbhulal, Wanqing Wu, V. Albuquerque (2019)
A novel cluster head selection technique for edge-computing based IoMT systemsComput. Networks, 158
Miguel Peña, Isabel Fernández (2019)
SAT-IoT: An Architectural Model for a High-Performance Fog/Edge/Cloud IoT Platform
P. Ray, D. Dash, D. De (2019)
Edge computing for Internet of Things: A survey, e-healthcare case study and future directionJ. Netw. Comput. Appl., 140
Jianji Ren, Haichao Wang, Tingting Hou, Shuai Zheng, Chaosheng Tang (2019)
Federated Learning-Based Computation Offloading Optimization in Edge Computing-Supported Internet of ThingsIEEE Access, 7
Yong Wang, Fei Liu, Zimao Pang, Abdelrhman Hassan, Wen-Hua Lu (2019)
Privacy-preserving content-based image retrieval for mobile computingJ. Inf. Secur. Appl., 49
Bahareh Farahani, Mojtaba Barzegari, F. Aliee (2019)
Towards Collaborative Machine Learning Driven Healthcare Internet of ThingsProceedings of the International Conference on Omni-Layer Intelligent Systems
Yun Liu, Ming-Ming Cheng, Xiaowei Hu, Kai Wang, X. Bai (2016)
Richer Convolutional Features for Edge DetectionIEEE Transactions on Pattern Analysis and Machine Intelligence, 41
Jamal Maktoubian, Keyvan Ansari (2019)
An IoT architecture for preventive maintenance of medical devices in healthcare organizationsHealth and Technology, 9
A. Sangaiah, D. Medhane, Tao Han, M. Hossain, G. Muhammad (2019)
Enforcing Position-Based Confidentiality With Machine Learning Paradigm Through Mobile Edge Computing in Real-Time Industrial InformaticsIEEE Transactions on Industrial Informatics, 15
Chih-Wei Shiu, Yu-Chi Chen, Wien Hong (2019)
Reversible Data Hiding in Permutation-based Encrypted Images with Strong PrivacyKSII Trans. Internet Inf. Syst., 13
Yifeng Zheng, Huayi Duan, Xiaoting Tang, Cong Wang, Jiantao Zhou (2019)
Denoising in the Dark: Privacy-Preserving Deep Neural Network-Based Image DenoisingIEEE Transactions on Dependable and Secure Computing, 18
Amin Azari, Mustafa Ozger, C. Cavdar (2018)
Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine LearningIEEE Communications Magazine, 57
Ayana Kawamura, Yuma Kinoshita, H. Kiya (2019)
Privacy-preserving machine learning using EtC images, 11515
Min Chen, Wei Li, Yixue Hao, Yongfeng Qian, I. Humar (2018)
Edge cognitive computing based smart healthcare systemFuture Gener. Comput. Syst., 86
A. Dastjerdi, Harshit Gupta, R. Calheiros, S. Ghosh, R. Buyya (2016)
Fog Computing: Principles, Architectures, and ApplicationsArXiv, abs/1601.02752
Size Hou, Xin Huang (2019)
Use of Machine Learning in Detecting Network Security of Edge Computing System2019 IEEE 4th International Conference on Big Data Analytics (ICBDA)
Jin Wang, Jia Hu, G. Min, Wenhan Zhan, Q. Ni, N. Georgalas (2019)
Computation Offloading in Multi-Access Edge Computing Using a Deep Sequential Model Based on Reinforcement LearningIEEE Communications Magazine, 57
Musaed Alhussein, G. Muhammad (2019)
Automatic Voice Pathology Monitoring Using Parallel Deep Models for Smart HealthcareIEEE Access, 7
M. Rahman, Md Rashid, M. Hossain, Elham Hassanain, Mohammed Alhamid, M. Guizani (2019)
Blockchain and IoT-Based Cognitive Edge Framework for Sharing Economy Services in a Smart CityIEEE Access, 7
Farzad Samie, L. Bauer, J. Henkel (2019)
From Cloud Down to Things: An Overview of Machine Learning in Internet of ThingsIEEE Internet of Things Journal, 6
Wenjuan Tang, Kuan Zhang, Deyu Zhang, Ju Ren, Yaoxue Zhang, X. Shen (2019)
Fog-Enabled Smart Health: Toward Cooperative and Secure Healthcare Service ProvisionIEEE Communications Magazine, 57
A. Abdellatif, Amr Mohamed, C. Chiasserini, Mounira Tlili, A. Erbad (2019)
Edge Computing for Smart Health: Context-Aware Approaches, Opportunities, and ChallengesIEEE Network, 33
N. Islam, Y. Faheem, I. Din, Muhammad Talha, M. Guizani, Mudassir Khalil (2019)
A blockchain-based fog computing framework for activity recognition as an application to e-Healthcare servicesFuture Gener. Comput. Syst., 100
Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.Design/methodology/approachThe exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).FindingsThe primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.Originality/valueThe utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.
International Journal of Intelligent Computing and Cybernetics – Emerald Publishing
Published: Feb 2, 2022
Keywords: Edge computing; Deep learning; Cloud computing; Fog computing; Electronic health record; Tensor processing unit; Graphics processing unit; Point of presence
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.