Athanasios Vasilakos

• Chude-Okonkwo, Uche A. K. & Vasilakos, Athanasios (2024). CRISPR-Enabled Graphene-Based Bio-Cyber Interface Model for In Vivo Monitoring of Non-Invasive Therapeutic Processes. IEEE Transactions on Nanobioscience. ISSN 1536-1241. 23(2), p. 300–309. doi: 10.1109/TNB.2023.3348201.
• Liu, Zhonghua; Wang, Tiansheng; Zhu, Fa; Chen, Xingchi; Pelusi, Danilo & Vasilakos, Athanasios (2024). Domain adaptive learning based on equilibrium distribution and dynamic subspace approximation. Expert Systems With Applications. ISSN 0957-4174. 249. doi: 10.1016/j.eswa.2024.123673.
• Gambín, Ángel Fernández; Yazidi, Anis; Vasilakos, Athanasios; Haugerud, Hårek & Djenouri, Youcef (2024). Deepfakes: current and future trends. Artificial Intelligence Review. ISSN 0269-2821. 57(3). doi: 10.1007/s10462-023-10679-x.
• Zhang, Jixian; Zong, Mingyi; Vasilakos, Athanasios & Li, Weidong (2024). UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization. IEEE Transactions on Network and Service Management. ISSN 1932-4537. 21(1), p. 324–340. doi: 10.1109/TNSM.2023.3301522.
• Diro, Abebe; Kaisar, Shahriar; Vasilakos, Athanasios; Anwar, Adnan; Nasirian, Araz & Olani, Gaddisa (2024). Anomaly detection for space information networks: A survey of challenges, techniques, and future directions. Computers & Security. ISSN 0167-4048. 139. doi: 10.1016/j.cose.2024.103705.
• Gong, Xuchao; Duan, Hongjie; Yang, Yaozhong; Tan, Lizhuang; Wang, Jian & Vasilakos, Athanasios (2024). Improving Audio Classification Method by Combining Self-Supervision with Knowledge Distillation. Electronics. ISSN 2079-9292. 13(1). doi: 10.3390/electronics13010052.
• Zhang, Jixian; Wang, Zhemin; Vasilakos, Athanasios & Li, Weidong (2024). A resource competition-based truthful mechanism for IoV edge computing resource allocation with a lowest revenue limit. The Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA). ISSN 2192-113X. 13(1). doi: 10.1186/s13677-023-00572-x.
• Huang, Mingfeng; Liu, Anfeng; Xiong, Neal N. & Vasilakos, Athanasios (2023). Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems. Digital Communications and Networks. ISSN 2468-5925. doi: 10.1016/j.dcan.2023.11.003. Full text in Research Archive
• Kumar, Jatinder; Saxena, Deepika; Singh, Ashutosh Kumar & Vasilakos, Athanasios (2023). A Quantum Controlled-NOT Neural Network-Based Load Forecast and Management Model for Smart Grid. IEEE Systems Journal. ISSN 1932-8184. 17(4), p. 5714–5725. doi: 10.1109/JSYST.2023.3309324.
• Yue, Weiqi; Wang, Maiqiu; Zhang, Lei; Zhang, Lijuan; Huang, Jie & Wan, Jian [Show all 8 contributors for this article] (2023). A-GSTCN: An Augmented Graph Structural–Temporal Convolution Network for Medication Recommendation Based on Electronic Health Records. Bioengineering. ISSN 2306-5354. 10(11). doi: 10.3390/bioengineering10111241. Full text in Research Archive Show summary

  Medication recommendation based on electronic health records (EHRs) is a significant research direction in the biomedical field, which aims to provide a reasonable prescription for patients according to their historical and current health conditions. However, the existing recommended methods have many limitations in dealing with the structural and temporal characteristics of EHRs. These methods either only consider the current state while ignoring the historical situation, or fail to adequately assess the structural correlations among various medical events. These factors result in poor recommendation quality. To solve this problem, we propose an augmented graph structural–temporal convolutional network (A-GSTCN). Firstly, an augmented graph attention network is used to model the structural features among medical events of patients’ EHRs. Next, the dilated convolution combined with residual connection is applied in the proposed model, which can improve the temporal prediction capability and further reduce the complexity. Moreover, the cache memory module further enhances the model’s learning of the history of EHRs. Finally, the A-GSTCN model is compared with the baselines through experiments, and the efficiency of the A-GSTCN model is verified by Jaccard, F1 and PRAUC. Not only that, the proposed model also reduces the training parameters by an order of magnitude.

• Thakur, Garima; Kumar, Pankaj; Chen, Chein-Ming; Vasilakos, Athanasios; Anchna, NN & Prajapat, Sunil (2023). A Robust Privacy-Preserving ECC-Based Three-Factor Authentication Scheme for Metaverse Environment. Computer Communications. ISSN 0140-3664. 211, p. 271–285. doi: 10.1016/j.comcom.2023.09.020. Show summary

  Metaverse is revolutionizing the future iteration of technology with the potential to ameliorate efficiency and innovation drastically. It fashions engaging spaces for user interaction that simulate the real world by integrating augmented and virtual reality. Metaverse furnishes immersive interactive experiences, unrestricted time and space, visualizations, minimal learning costs, and endorses communication. A metaverse offers various services, such as virtual environments and avatars for telecommuting, education, and gaming. Yet, to access services, users must register with the server, where the registration requires user’s identity, password, and personal information. Additionally, the user interaction in metaverse is mediated through avatars over public channels, fabricating opportunities for security attacks such as replay and impersonation attacks. Consequently, we propose a secure mutual authentication scheme for safer user-server and avatar–avatar interactions utilizing Elliptic Curve Cryptography (ECC) and fuzzy extractor. The security of protocol is examined with Burrows–Abadi–Needham (BAN) logic, Real-or-Random (ROR) model, and Automated Validation of Internet Security Protocols Applications (AVISPA). We also conduct a comparative analysis of the computational and communication costs and the security features of proposed scheme with pre-existing works. Hence, the suggested protocol offers magnificent security and efficiency, making it suitable for the metaverse environment.

• Zhu, Fa; Chen, Xingchi; Gao, Xizhan; Ye, Weidu; Zhao, Hai & Vasilakos, Athanasios (2023). Constraint-weighted support vector ordinal regression to resist constraint noises. Information Sciences. ISSN 0020-0255. 649. doi: 10.1016/j.ins.2023.119644.
• Meng, Qianqi; Cao, Yixiao; Ling, Xintong; Wang, Jiaheng & Vasilakos, Athanasios (2023). Feedback-Enhanced Data Broker Routing Protocol for Multi-hop Blockchain Radio Access Network, 2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom). IEEE conference proceedings. ISSN 979-8-3503-3333-6. doi: 10.1109/MetaCom57706.2023.00022.
• Ali, Mohammad; Sadeghi, Mohammad-Reza; Liu, Ximeng & Vasilakos, Athanasios (2023). Anonymous Aggregate Fine-Grained Cloud Data Verification System for Smart Health. IEEE Transactions on Cloud Computing. ISSN 2168-7161. 11(3), p. 2839–2855. doi: 10.1109/TCC.2022.3229269. Show summary

  With the rapid development of cloud computing and Internet of Things (IoT), smart health (s-health) is anticipated to enhance healthcare quality significantly. However, data integrity, user anonymity, and authentication concerns have not been adequately addressed in s-health. Remote data integrity checking (RDIC) and digital signature schemes have great potential to address these requirements. Nevertheless, the direct adoption of these schemes suffers from two flaws. First, they incur prohibitively high computation and communication overhead. Second, they leak sensitive health information about patients and do not provide complete anonymity. To address these issues, we introduce A3B - RDV , an aggregate anonymous attribute-based remote data verification scheme. In A3B - RDV , the integrity of an arbitrary number of cloud data files can be verified at once without downloading the whole data, thereby saving communication and computation resources. Moreover, in A3B - RDV , data owners can be authenticated by performing highly efficient operations. Also, A3B - RDV provides complete anonymity and supports dishonest-user traceability. We provide security definitions for A3B - RDV and prove its security under the hardness assumption of the bilinear Diffie-Hellman (BDH) problem. Performance comparisons and experimental results indicate that A3B - RDV is more efficient and expressive than state-of-the-art approaches.

• Singh, Jaskaran; Sharma, Keshav; Wazid, Mohammad; Das, Ashok Kumar & Vasilakos, Athanasios (2023). An Ensemble-Based IoT-Enabled Drones Detection Scheme for a Safe Community. IEEE Open Journal of the Communications Society. ISSN 2644-125X. 4, p. 1946–1956. doi: 10.1109/OJCOMS.2023.3310003. Full text in Research Archive Show summary

  With the increasing use of Internet of Things (IoT)-enabled drones for various purposes, including photography, delivery, and surveillance, concerns related to privacy and security have arisen. Drones have the potential to capture sensitive information, invade privacy, and cause security breaches. Therefore, the need for advanced technology for the automated detection of drones has become crucial. In this paper, we propose an ensemble-based IoT-enabled drones detection scheme (in short, EDDSBS). The presented model is part of a computer vision-based module and uses transfer learning for improved performance. Transfer learning allows the reuse of pre-trained models and their knowledge in a different but related domain, enabling better performance with less training data. To evaluate the performance of the proposed EDDSBS, we test it on benchmark datasets, including the Drone–vs–Bird Dataset and the UAVDT dataset. The proposed EDDSBS outperforms the existing schemes of drone detection (i.e., in terms of accuracy). The results of the presented scheme demonstrate the potential of deep learning-based technology for automated drone detection in critical areas, such as airports, military bases, and other high-security areas. Thus the paper introduces a comprehensive process methodology for drone detection that can be applied in real-world settings for a sustainable and secure environment, which is required for a safe community.

• Li, Hao; Wang, Jinwei; Xiong, Neal; Zhang, Yi; Vasilakos, Athanasios & Luo, Xiangyang (2023). A Siamese Inverted Residuals Network Image Steganalysis Scheme based on Deep Learning. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP). ISSN 1551-6857. 19(6), p. 1–23. doi: 10.1145/3579166. Show summary

  With the rapid proliferation of urbanization, massive data in social networks are collected and aggregated in real time, making it possible for criminals to use images as a cover to spread secret information on the Internet. How to determine whether these images contain secret information is a huge challenge for multimedia computing security. The steganalysis method based on deep learning can effectively judge whether the pictures transmitted on the Internet in urban scenes contain secret information, which is of great significance to safeguarding national and social security. Image steganalysis based on deep learning has powerful learning ability and classification ability, and its detection accuracy of steganography images has surpassed that of traditional steganalysis based on manual feature extraction. In recent years, it has become a hot topic of the information hiding technology. However, the detection accuracy of existing deep learning based steganalysis methods still needs to be improved, especially when detecting arbitrary-size and multi-source images, their detection efficientness is easily affected by cover mismatch. In this manuscript, we propose a steganalysis method based on Inverse Residuals structured Siamese network (abbreviated as SiaIRNet method, Siamese-Inverted-Residuals-Network Based method). The SiaIRNet method uses a siamese convolutional neural network (CNN) to obtain the residual features of subgraphs, including three stages of preprocessing, feature extraction, and classification. Firstly, a preprocessing layer with high-pass filters combined with depth-wise separable convolution is designed to more accurately capture the correlation of residuals between feature channels, which can help capture rich and effective residual features. Then, a feature extraction layer based on the Inverse Residuals structure is proposed, which improves the ability of the model to obtain residual features by expanding channels and reusing features. Finally, a fully connected layer is used to classify the cover image and the stego image features. Utilizing three general datasets, BossBase-1.01, BOWS2, and ALASKA#2, as cover images, a large number of experiments are conducted comparing with the state-of-the-art steganalysis methods. The experimental results show that compared with the classical SID method and the latest SiaStegNet method, the detection accuracy of the proposed method for 15 arbitrary-size images is improved by 15.96% and 5.86% on average, respectively, which verifies the higher detection accuracy and better adaptability of the proposed method to multi-source and arbitrary-size images in urban scenes.

• Mishra, Amit Kumar; Wazid, Mohammad; Singh, Devesh Pratap; Das, Ashok Kumar; Singh, Jaskaran & Vasilakos, Athanasios (2023). Secure Blockchain-Enabled Authentication Key Management Framework with Big Data Analytics for Drones in Networks Beyond 5G Applications. Drones. ISSN 2504-446X. 7(8). doi: 10.3390/drones7080508. Full text in Research Archive Show summary

  One of the most significant recent advances in technology is the advent of unmanned aerial vehicles (UAVs), i.e., drones. They have widened the scope of possible applications and provided a platform for a wide range of creative responses to a variety of challenges. The Internet of Drones (IoD) is a relatively new concept that has arisen as a consequence of the combination of drones and the Internet. The fifth-generation (5G) and beyond cellular networks (i.e., drones in networks beyond 5G) are promising solutions for achieving safe drone operations and applications. They may have many applications, like surveillance or urban areas, security, surveillance, retaliation, delivering items, smart farming, film production, capturing nature videos, and many more. Due to the fact that it is susceptible to a wide variety of cyber-attacks, there are certain concerns regarding the privacy and security of IoD communications. In this paper, a secure blockchain-enabled authentication key management framework with the big data analytics feature for drones in networks beyond 5G applications is proposed (in short, SBBDA-IoD). The security of SBBDA-IoD against multiple attacks is demonstrated through a detailed security analysis. The Scyther tool is used to perform a formal security verification test on the SBBDA-IoD’s security, confirming the system’s resistance to various potential attacks. A detailed comparative analysis has identified that SBBDA-IoD outperforms the other schemes by a significant margin. Finally, a real-world implementation of SBBDA-IoD is shown to evaluate its effect on several measures of performance.

• Shi, Binbin; Fan, Rongli; Zhang, Lijuan; Huang, Jie; Xiong, Neal & Vasilakos, Athanasios [Show all 8 contributors for this article] (2023). A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring. Sensors. ISSN 1424-8220. 23(10). doi: 10.3390/s23104812. Full text in Research Archive Show summary

  Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.

• Qiu, Yiqin; Tian, Hui; Li, Haizhou; Chang, Chin-Chen & Vasilakos, Athanasios (2023). Separable Convolution Network With Dual-Stream Pyramid Enhanced Strategy for Speech Steganalysis. IEEE Transactions on Information Forensics and Security. ISSN 1556-6013. 18, p. 2737–2750. doi: 10.1109/TIFS.2023.3269640.
• Wong, Renata; Chang, Weng-Long; Chung, Wen-Yu & Vasilakos, Athanasios (2023). Biomolecular and quantum algorithms for the dominating set problem in arbitrary networks. Scientific Reports. ISSN 2045-2322. 13. doi: 10.1038/s41598-023-30600-4. Full text in Research Archive Show summary

  A dominating set of a graph G=(V,E) is a subset U of its vertices V, such that any vertex of G is either in U, or has a neighbor in U. The dominating-set problem is to find a minimum dominating set in G. Dominating sets are of critical importance for various types of networks/graphs, and find therefore potential applications in many fields. Particularly, in the area of communication, dominating sets are prominently used in the efficient organization of large-scale wireless ad hoc and sensor networks. However, the dominating set problem is also a hard optimization problem and thus currently is not efficiently solvable on classical computers. Here, we propose a biomolecular and a quantum algorithm for this problem, where the quantum algorithm provides a quadratic speedup over any classical algorithm. We show that the dominating set problem can be solved in O(2n/2) queries by our proposed quantum algorithm, where n is the number of vertices in G. We also demonstrate that our quantum algorithm is the best known procedure to date for this problem. We confirm the correctness of our algorithm by executing it on IBM Quantum’s qasm simulator and the Brooklyn superconducting quantum device. And lastly, we show that molecular solutions obtained from solving the dominating set problem are represented in terms of a unit vector in a finite-dimensional Hilbert space.

• Chude-Okonkwo, Uche K.; Paul, Babu S. & Vasilakos, Athanasios (2023). Enabling Precision Medicine via Contemporary and Future Communication Technologies: A Survey. IEEE Access. ISSN 2169-3536. 11, p. 21210–21240. doi: 10.1109/ACCESS.2022.3175573. Full text in Research Archive Show summary

  Precision medicine (PM) is an innovative medical approach that considers differences in the individuals’ omics, medical histories, lifestyles, and environmental information in treating diseases. To fully achieve the envisaged gains of PM, various contemporary and future technologies have to be employed, among which are nanotechnology, sensor network, big data, and artificial intelligence. These technologies and other applications require a communication network that will enable them to work in tandem for the benefit of PM. Hence, communication technology serves as the nervous system of PM, without which the entire system collapses. Therefore, it is essential to explore and determine the candidate communication technology requirements that can guarantee the envisioned gains of PM. To the best of our knowledge, no work exploring how communication technology directly impacts the development and deployment of PM solutions exists. This survey paper is designed to stimulate discussions on PM from the communication engineering perspective. We introduce the fundamentals of PM and the demands in terms of quality of service that each of the enabling technologies of PM places on the communication network. We explore the information in the literature to suggest the ideal metric values of the key performance indicators for the implementation of the different components of PM. The comparative analysis of the suitability of the contemporary and future communication technologies for PM implementation is discussed. Finally, some open research challenges for the candidate communication technologies that will enable the full implementation of PM solutions are highlighted.

• Li, Chao; Yang, Hui; Sun, Zhengjie; Yao, Qiuyan; Bao, Bowen & Zhang, Jie [Show all 7 contributors for this article] (2023). Federated Hierarchical Trust-Based Interaction Scheme for Cross-Domain Industrial IoT. IEEE Internet of Things Journal. ISSN 2327-4662. 10(1), p. 447–457. doi: 10.1109/JIOT.2022.3200854. Show summary

  The Industrial Internet of Things (IIoT) is considered to be one of the most promising revolutionary technologies to increase productivity. With the refined development of manufacturing, the entire manufacturing process is split up into several areas of IoT production. Devices from different domains cooperate to perform the same task, which cause security problems in interacted communication among them. Existing authentication methods cause heavy key management overhead or rely on a trusted third party. It is imperative to protect privacy and ensure the credibility of the device during device interaction. This article proposes a federated hierarchical trust interaction scheme (FHTI) for the cross-domain industrial IoT. It builds a low-privacy network platform through blockchain and protects the data privacy of the IIoT. A hierarchical trust mechanism based on federated detection is designed to realize the unified trust evaluation of cross-domain devices. A trusted cross-domain method based on device trust value is designed to ensure the security and trustworthiness of cross-domain devices. The simulation results show that the FHTI scheme can improve the speed of identity authentication and the detection accuracy of malicious devices.

• Yu, Jiayi; Li, Zeyuan; Xiong, Naixue; Zhang, Shaobo; Liu, Anfeng & Vasilakos, Athanasios (2023). A reliability and truth-aware based online digital data auction mechanism for cybersecurity in MCS. Future Generation Computer Systems. ISSN 0167-739X. 141, p. 526–541. doi: 10.1016/j.future.2022.11.028. Show summary

  Mobile Crowd Sensing (MCS) platform recruits participants to sense and report data, thus to build and provide services to consumers, which is a promising computing paradigm. However, malicious attacks based on false data may damage network security, and some low-trust participants may not complete tasks or submit false data since it costs resources to sense data. Both these behaviors do great harm to cybersecurity and reduce the benefit of the platform. Even if many incentive mechanisms have been proposed, there are three critical properties that are not well considered, i.e., truth-reduction, reliability-zero and time-discounting properties, which correspond to the effects of malicious attacks and the uncertain behaviors of participants. In this paper, we take these properties into account and propose a Time, Reliability and Truth-aware Online Auction (TRT-OA) mechanism to ensure cybersecurity and maximize the benefit of the platform, introducing a function T˜ and a RT coefficient to select more secure and profitable participants. We prove that TRT-OA mechanism achieves computational efficiency, budget feasibility, truthfulness, individual rationality and strategyproofness. By comparing it with OMG and TDMC, we show that the benefit of TRT-OA mechanism increases by 44.06%.

• Li, Guangquan; Wang, Ting; Chen, Qi; Shao, Peng; Xiong, Naixue & Vasilakos, Athanasios (2022). A Survey on Particle Swarm Optimization for Association Rule Mining. Electronics. ISSN 2079-9292. 11(19). doi: 10.3390/electronics11193044. Full text in Research Archive
• Yang, Hui; Yao, Qiuyan; Bao, Bowen; Yu, Ao; Zhang, Jie & Vasilakos, Athanasios (2022). Multi-Associated Parameters Aggregation-Based Routing and Resources Allocation in Multi-Core Elastic Optical Networks. IEEE/ACM Transactions on Networking. ISSN 1063-6692. 30(5), p. 2145–2157. doi: 10.1109/TNET.2022.3164869. Show summary

  Space division multiplexing (SDM), as a potential means of enhancing the capacity of optical transmission systems, has attracted widespread attention. However, the adoption of SDM technology has also additionally increased resource dimensions, introduced complex crosstalk, and made it difficult to integrate multi-dimensional fragments. These factors force the transmission constraints to be more complicated. Especially, some factors have a mutual restraint relationship, and excessive consideration of certain factors will cause the deterioration of other ones. Therefore, how to comprehensively consider the associated factors to achieve trade-offs and improve network performance is a problem worthy of study. This paper exploits the advantages of self-organizing feature mapping (SOFM) model to process multi-dimensional data with relevant features. Firstly, multiple constraints will be input into SOFM as mode vectors from the core level. Then, by judging the similarity between the competition layer neuron and the pattern vector, the position of the winning neuron is located, which determines the transmission level of each core. Finally, a routing, core, and spectrum allocation scheme is proposed by preferentially locating the core with higher transmission quality. Along the selected core, the available slots will be classified twice respectively by the number of adjacent cores and crosstalk direction to quickly find the spectrum blocks with relatively small crosstalk. Results indicate the scheme can reduce blocking probability and the resource fragmentation. Further, it can increase the resource utilization within tested network load.

• Wang, Kuan; Wu, Jun; Zheng, Xi; Li, Jianhua; Yang, Wu & Vasilakos, Athanasios (2022). Cloud-Edge Orchestrated Power Dispatching for Smart Grid With Distributed Energy Resources. IEEE Transactions on Cloud Computing. ISSN 2168-7161. doi: 10.1109/TCC.2022.3185170. Show summary

  Cloud and edge computing are gradually used to achieve complex energy operation control and massive information processing in conventional power grid. Meanwhile, with the tremendous number of distributed energy resources and power equipment integrated into the smart grid enabled by cloud and edge computing, the centralized distribution network cannot realize the flexible and realtime energy supply due to the unpredictable and wide distribution of distributed energy resources, which will further deteriorate the stability of smart grid. To solve those problems, this paper proposes energy centric smart grid to achieve power dispatching with the help of cloud-edge computing. Our solution uses energy caching and energy multiple addressing of the edge router to eliminate the intermittency of renewables and speed up energy response. For the stability of energy market and to encourage users to participate in power dispatching, a cloud-edge computing-driven energy cache orchestration mechanism is designed. The empirical results show that the response time is greatly reduced to meet the stringent quality of service requirement in smart grid integrated with distributed energy resources.

• Abdar, Moloud; Khosrav, Abbas; Shariful Islam, Sheikh Mohammed; Acharya, U. Rajendra & Vasilakos, Athanasios (2022). The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process. IEEE Systems, Man, and Cybernetics Magazine. ISSN 2380-1298. 8(3), p. 28–40. doi: 10.1109/MSMC.2022.3150144. Show summary

  Different terms such as trust, certainty, and uncertainty are of great importance in the real world and play a critical role in artificial intelligence (AI) applications. The implied assumption is that the level of trust in AI can be measured in different ways. This principle can be achieved by distinguishing uncertainties in predicting AI methods used in medical studies. Hence, it is necessary to propose effective uncertainty quantification (UQ) and measurement methods to have trustworthy AI (TAI) clinical decision support systems (CDSSs). In this study, we present practical guidelines for developing and using UQ methods while applying various AI techniques for medical data analysis.

• Mo, Haimiao; Ding, Shuai; Yang, Shanlin; Vasilakos, Athanasios & Zheng, Xi (2022). Collaborative Three-Tier Architecture Noncontact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms. IEEE Transactions on Instrumentation and Measurement. ISSN 0018-9456. 71. doi: 10.1109/TIM.2022.3205644. Show summary

  Monitoring respiratory rate is crucial for helping identify respiratory disorders. Devices for conventional respiratory monitoring are inconvenient and scarcely available. Recent research has demonstrated the ability of non-contact technologies, such as photoplethysmography and infrared thermography, to gather respiratory signals from face and monitor breathing. However, current non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. Furthermore, frequent contact between users and the cloud in real-world medical application settings might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency. To reduce data transmission and network latency, our three-tier architecture layer-by-layer decomposes the computing tasks of respiration monitoring. Moreover, we improved the accuracy of respiratory monitoring by designing a target tracking algorithm and an algorithm for eliminating false peaks to extract highquality respiratory signals. By gathering the data and choosing several regions of interest on face, we were able to extract the respiration signal and investigate how different regions affected the monitoring of respiration. The results of the experiment indicate that when the nasal region is used to extract the respiratory signal, it performs experimentally best. Our approach performs better than baseline approaches while transferring fewer data.

• Dharminder, Dharminder; Das, Ashok Kumar; Saha, Sourav; Bera, Basudeb & Vasilakos, Athanasios (2022). Post-Quantum Secure Identity-Based Encryption Scheme using Random Integer Lattices for IoT-enabled AI Applications. Security and Communication Networks. ISSN 1939-0114. 2022. doi: 10.1155/2022/5498058. Full text in Research Archive
• Ali, Mohammad; Sadeghi, Mohammad-Reza; Liu, Ximeng; Miao, Yinbin & Vasilakos, Athanasios (2022). Verifiable online/offline multi-keyword search for cloud-assisted Industrial Internet of Things. Journal of Information Security and Applications. ISSN 2214-2134. 65. doi: 10.1016/j.jisa.2021.103101. Show summary

  Attribute-based encryption (ABE) and attribute-based keyword search (ABKS) facilitate fine-grained access and search control for cloud-assisted Industrial Internet of Things (IIoT). However, existing schemes suffer from the following drawbacks: (1) their computational overhead in data outsourcing and retrieval is exceptionally high; (2) they obtain wrong search results if one or more of the queried keywords are wrongly selected; (3) in most existing ABKS, the untrustworthiness of the cloud server is not taken into account, and the correctness of the search results is not verified; (4) existing verifiable ABKS schemes do not support search results verification without the main data, and verifiers have to first download the search results and then check their correctness. In this paper, we design a verifiable online/offline multi-keyword search (VMKS) scheme providing high-level solutions to the aforementioned problems. We use the healthcare setting as a case study, and we demonstrate how our VMKS can be deployed in cloud-assisted Healthcare IIoT (HealthIIoT). We prove the security of our VMKS in the standard model and under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem. Empirical results demonstrate that our VMKS speeds up encryption and verification processes by more than 70% and 90%, respectively. Moreover, our scheme reduces the communication overhead in the verification phase by more than 80%.

• Sun, Zhengjie; Yang, Hui; Li, Chao; Yao, Qiuyan; Wang, Danshi & Zhang, Jie [Show all 8 contributors for this article] (2021). Cloud-Edge Collaboration in Industrial Internet of Things: A Joint Offloading Scheme based on Resource Prediction. IEEE Internet of Things Journal. ISSN 2327-4662. 9(18), p. 17014–17025. doi: 10.1109/JIOT.2021.3137861.
• Bera, Basudeb; Saha, Sourav; Das, Ashok Kumar & Vasilakos, Athanasios (2021). Designing Blockchain-Based Access Control Protocol in IoT-Enabled Smart-Grid System. IEEE Internet of Things Journal. ISSN 2327-4662. 8(7), p. 5744–5761. doi: 10.1109/JIOT.2020.3030308.
• Hamdan, Mosab; Hassan, Entisar; Abdelaziz, Ahmed; Elhigazi, Abdallah; Mohammed, Bushra & Khan, Suleman [Show all 8 contributors for this article] (2021). A comprehensive survey of load balancing techniques in software-defined network. Journal of Network and Computer Applications. ISSN 1084-8045. 174.
• Vasilakos, Athanasios (2021). Latency performance modeling and analysis for hyperledger fabric blockchain network. Information Processing & Management. ISSN 0306-4573.
• Vasilakos, Athanasios (2021). Fine-Grained Powercap Allocation for Power-Constrained Systems Based on Multi-Objective Machine Learning. IEEE Transactions on Parallel and Distributed Systems. ISSN 1045-9219. 32(7), p. 1789 –1801.
• Ni, Jianbing; Zhang, Kuan & Vasilakos, Athanasios (2021). Security and Privacy for Mobile Edge Caching: Challenges and Solutions. IEEE wireless communications. ISSN 1536-1284. 28(3), p. 77 –83.
• Wang, Sawei; Jin, Xin; Mao, Shuai; Vasilakos, Athanasios & Tang, Yang (2021). Model-Free Event-Triggered Optimal Consensus Control of Multiple Euler-Lagrange Systems via Reinforcement Learning. IEEE Transactions on Network Science and Engineering (IEEE TNSE). ISSN 2327-4697. 8(1), p. 246 –258.
• wu, weiwei; Wang, Wanyang; Fang, Xiaolin; Luo, Junzhou & Vasilakos, Athanasios (2021). Electricity Price-aware Consolidation Algorithms for Time-sensitive VM Services in Cloud Systems. IEEE Transactions on Services Computing. ISSN 1939-1374. 14(6), p. 1726–1738.

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• Vasilakos, Athanasios (2022). Cloud-Edge Collaboration in Industrial Internet of Things: A Joint Offloading Scheme Based on Resource Prediction. IEEE Internet of Things Journal. ISSN 2327-4662.

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