EDBT 2026 Demo / reviewers in the wild / expert
Yunkai Zhai
dblp:79/8584
· DBLP profile ↗
6ranked-venue papers
0as first author
5since 2021 · last 2025
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 3 · 3 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Physical-layer communications · 87% Internet of things and sensor networks · 13% | |
| Theoretical computer science
1 paper |
Coding theory · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications › multiple access › non-orthogonal multiple access
multi-carrier NOMA |
0.5 | 1 | 2021 | Sparse Vector Coding-Based Multi-Carrier NOMA for In-Home Health Networks · IEEE J. Sel. Areas Commun. 2021 |
Physical-layer communications › multiple access
non-orthogonal multiple access |
0.5 | 1 | 2021 | Sparse Vector Coding-Based Multi-Carrier NOMA for In-Home Health Networks · IEEE J. Sel. Areas Commun. 2021 |
Coding theory › error-correcting codes
sparse vector coding |
0.5 | 1 | 2021 | Sparse Vector Coding-Based Multi-Carrier NOMA for In-Home Health Networks · IEEE J. Sel. Areas Commun. 2021 |
Methods — techniques the papers use, named apart from their topics
symbol error rate analysis · 1.0sparse vector coding · 1.0capacity analysis · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | A Secure and Efficient Sharing Scheme for Medical IoT Data Based on Consortium BlockchainabstractInternet of things (IoT) is crucial for the hierarchical medical system, which enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. In this scenario, HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes, namely, private data collection (PDC), direct channel (DC), and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the security and performance of our proposed scheme are verified, and the results demonstrate that our scheme is secure, feasible, and efficient. Yunkai Zhai, Di Zhang 0002, BaoZhan Chen, Athanasios V. Vasilakos, M. Shamim Hossain, Shahid Mumtaz |
IEEE Internet Things J. | 3 |
| 2024 | A Controllable and Efficient Sharing Scheme for Medical IoT Data Based on Consortium BlockchainabstractInternet of Things (IoT) is crucial for the hierarchical medical system, and enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes: private data collection (PDC), direct channel, and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the performance of our proposed solution is verified by experimental results, and the results demonstrate our solution is feasible and efficient. In future work, we plan to use searchable encryption technology to make this scheme more versatile and gradually implement dynamic adjustment of permissions. Yunkai Zhai, Di Zhang 0002, Athanasios V. Vasilakos, M. Shamim Hossain, Shahid Mumtaz |
HealthCom | 3 |
| 2022 | Automated localization and severity period prediction of myocardial infarction with clinical interpretability based on deep learning and knowledge graph
Chuang Han, Shihao Pan, Wenge Que, Zhizhong Wang, Yunkai Zhai |
Expert Syst. Appl. | 5 |
| 2021 | A Security Awareness and Protection System for 5G Smart Healthcare Based on Zero-Trust ArchitectureabstractThe key features of 5G network (i.e., high bandwidth, low latency, and high concurrency) along with the capability of supporting big data platforms with high mobility make it valuable in coping with emerging medical needs, such as COVID-19 and future healthcare challenges. However, enforcing the security aspect of a 5G-based smart healthcare system that hosts critical data and services is becoming more urgent and critical. Passive security mechanisms (e.g., data encryption and isolation) used in legacy medical platforms cannot provide sufficient protection for a healthcare system that is deployed in a distributed manner and fail to meet the need for data/service sharing across "cloud-edge-terminal" in the 5G era. In this article, we propose a security awareness and protection system that leverages zero-trust architecture for a 5G-based smart medical platform. Driven by the four key dimensions of 5G smart healthcare including "subject" (i.e., users, terminals, and applications), "object" (i.e., data, platforms, and services), "behavior," and "environment," our system constructs trustable dynamic access control models and achieves real-time network security situational awareness, continuous identity authentication, analysis of access behavior, and fine-grained access control. The proposed security system is implemented and tested thoroughly at industrial-grade, which proves that it satisfies the needs of active defense and end-to-end security enforcement of data, users, and services involved in a 5G-based smart medical system. BaoZhan Chen, Siyuan Qiao, Dongqing Liu, Xiaobing Shi, Minzhao Lyu, Huimin Lu 0001, Yunkai Zhai |
IEEE Internet Things J. | 9 |
| 2021 | Sparse Vector Coding-Based Multi-Carrier NOMA for In-Home Health NetworksabstractIn-home health networks greatly rely on the massive connected monitoring devices. Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) can connect more monitoring devices and enhance the spectrum efficiency (SE) performance, which makes it an ideal solution to in-home health networks. However, conventional NOMA (C-NOMA) is mostly constrained to single-carrier scenario. The problem of multi-carrier NOMA lies in the inter-carrier interference (ICI) from neighboring carriers. In this article, we propose a sparse vector coding-based NOMA (SVC-NOMA) to suppress the ICI. We give closed-form expressions of capacity and symbol error rate (SER) performances for both C-NOMA and SVC-NOMA within the considered multi-carrier scenario. Simulation results demonstrate that compared to C-NOMA, SVC-NOMA has better capacity and SER performances. In addition, we find from our results that there is a trade-off between SVC-NOMA's ICI suppression ability and the system capacity performance. Xuewan Zhang, Liuqing Yang 0001, Zhiguo Ding 0001, Jian Song 0004, Yunkai Zhai, Di Zhang 0002 |
IEEE J. Sel. Areas Commun. | 5 |
| 2019 | A deep automated skeletal bone age assessment model via region-based convolutional neural network
Baoyu Liang, Yunkai Zhai, Chao Tong 0001, Jun Li 0045, Xianying He, Qianqian Ma |
Future Gener. Comput. Syst. | 2 |