Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting
Abstract
Mobile edge computing helps healthcare Internet of Things (IoT) devices with energy harvesting provide satisfactory quality of experiences for computation intensive applications. We propose a reinforcement learning (RL)-based privacy-aware offloading scheme to help healthcare IoT devices protect both the user location privacy and the usage pattern privacy. More specifically, this scheme enables a healthcare IoT device to choose the offloading rate that improves the computation performance, protects user privacy, and saves the energy of the IoT device without being aware of the privacy leakage, IoT energy consumption, and edge computation model. This scheme uses transfer learning to reduce the random exploration at the initial learning process and applies a Dyna architecture that provides simulated offloading experiences to accelerate the learning process. A post-decision state learning method uses the known channel state model to further improve the offloading performance. We provide the performance bound of this scheme regarding the privacy level, the energy consumption, and the computation latency for three typical healthcare IoT offloading scenarios. Simulation results show that this scheme can reduce the computation latency, save the energy consumption, and improve the privacy level of the healthcare IoT device compared with the benchmark scheme.
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In IPFS, a file's hash is produced by its content.
“Note that in this context, this hash is produced by file content as the IPFS storage concept [34]”
From Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems (2019) · cites this paper as [34]
IPFS is a promising solution to build a file sharing platform in the blockchain network.
“we leverage a decentralized peer-to-peer file system InterPlanetary File System (IPFS), a very promising solution to build a file sharing platform in the blockchain network [34].”
From Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems (2019) · cites this paper as [34]
DRL-based security solutions can provide secure offloading from the edge device to the edge node against jamming attacks or protect user location privacy and the usage pattern privacy.
“DRL-based security solutions can provide secure offloading (from the edge device to the edge node) to against jamming attacks [261] or protect user location privacy and the usage pattern privacy [270].”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [270]
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- Authors
- Min, M., Wan, X., Xiao, L., Chen, Y., Xia, M., Wu, D., & Dai, H.
- Affiliations
- Xiamen University (CN)
- Sun Yat-sen University (CN)
- North Carolina State University (US)
- Venue
- IEEE Internet of Things Journal
- Published
- 2019-06
- Volume
- 6
- Issue
- 3
- Pages
- 4307-4316
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2895879715
- Cited by
- 213
- References
- 40
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 18 · 2023: 27 · 2022: 36 · 2021: 46 · 2020: 36 · 2019: 23 · 2018: 1
- FWCI
- 16.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- National Natural Science Foundation of China (61671396, 91638204, 61671488, 61572538)
- Southeast University (2018D08)
- Guangdong Special Support Program (2017TX04X148)
- Fundamental Research Funds for the Central Universities (17LGJC23)
- National Science Foundation (ECCS-1444009, CNS-1824518)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- Internet of Medical Things, energy harvesting, mobile edge computing, reinforcement learning, transfer learning, location privacy, computation latency, energy consumption, channel state information
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