Outputs (701)

IoT Authentication Protocols: Challenges, and Comparative Analysis (2025)
Journal Article
Alsheavi, A., Hawbani, A., Othman, W., Wang, X., Qaid, G. R. S., Zhao, L., Al-Dubai, A., Liu, Z., Ismail, A., Haveri, R. H., Alsamhi, S. H., & Al-Qaness, M. A. A. (2025). IoT Authentication Protocols: Challenges, and Comparative Analysis. ACM computing surveys, 57(5), Article 116. https://doi.org/10.1145/3703444

In the ever-evolving information technology landscape, the Internet of Things (IoT) is a groundbreaking concept that bridges the physical and digital worlds. It is the backbone of an increasingly sophisticated interactive environment, yet it is a sub... Read More about IoT Authentication Protocols: Challenges, and Comparative Analysis.

A True Random Number Generator Based On Race Hazard And Jitter Of Braided And Cross-Coupled Logic Gates Using FPGA (2024)
Journal Article
Ahmed, H. O., Kim, D., & Buchanan, B. (in press). A True Random Number Generator Based On Race Hazard And Jitter Of Braided And Cross-Coupled Logic Gates Using FPGA. IEEE Access, 12, 182943-182955. https://doi.org/10.1109/ACCESS.2024.3512419

In the contemporary digital landscape, security has become a vital element of our existence. The growing volume of sensitive information being stored and transmitted over networks necessitates the implementation of robust security measures. Cryptogra... Read More about A True Random Number Generator Based On Race Hazard And Jitter Of Braided And Cross-Coupled Logic Gates Using FPGA.

An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata (2024)
Journal Article
Rizwan, M., Hawbani, A., Xingfu, W., Anjum, A., Angin, P., Sever, Y., Chen, S., Zhao, L., & Al-Dubai, A. (online). An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata. IEEE Transactions on Big Data, https://doi.org/10.1109/TBDATA.2024.3495497

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vert... Read More about An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata.

CAST: Efficient Traffic Scenario Inpainting in Cellular Vehicle-to-Everything Systems (2024)
Journal Article
Zhao, L., Mao, C., Wan, S., Hawbani, A., Al-Dubai, A. Y., Min, G., & Zomaya, A. Y. (online). CAST: Efficient Traffic Scenario Inpainting in Cellular Vehicle-to-Everything Systems. IEEE Transactions on Mobile Computing, https://doi.org/10.1109/tmc.2024.3492148

As a promising vehicular communication technology, Cellular Vehicle-to-Everything (C-V2X) is expected to ensure the safety and convenience of Intelligent Transportation Systems (ITS) by providing global road information. However, it is difficult to o... Read More about CAST: Efficient Traffic Scenario Inpainting in Cellular Vehicle-to-Everything Systems.

Novel Lagrange Multipliers-Driven Adaptive Offloading for Vehicular Edge Computing (2024)
Journal Article
Zhao, L., Li, T., Meng, G., Hawbani, A., Min, G., Al-Dubai, A., & Zomaya, A. (2024). Novel Lagrange Multipliers-Driven Adaptive Offloading for Vehicular Edge Computing. IEEE Transactions on Computers, 73(12), 2868-2881. https://doi.org/10.1109/TC.2024.3457729

Vehicular Edge Computing (VEC) is a transportation-specific version of Mobile Edge Computing (MEC) designed for vehicular scenarios. Task offloading allows vehicles to send computational tasks to nearby Roadside Units (RSUs) in order to reduce the co... Read More about Novel Lagrange Multipliers-Driven Adaptive Offloading for Vehicular Edge Computing.

User Preferences-Based Proactive Content Caching with Characteristics Differentiation in HetNets (2024)
Journal Article
Lin, N., Wang, Y., Zhang, E., Wan, S., Al-Dubai, A., & Zhao, L. (online). User Preferences-Based Proactive Content Caching with Characteristics Differentiation in HetNets. IEEE Transactions on Sustainable Computing, https://doi.org/10.1109/TSUSC.2024.3441606

With the proliferation of mobile applications, the explosion of mobile data traffic imposes a significant burden on backhaul links with limited capacity in heterogeneous cellular networks (HetNets). To alleviate this challenge, content caching based... Read More about User Preferences-Based Proactive Content Caching with Characteristics Differentiation in HetNets.

Transforming EU Governance: The Digital Integration Through EBSI and GLASS (2024)
Presentation / Conference Contribution
Kasimatis, D., Buchanan, W. J., Abubakar, M., Lo, O., Chrysoulas, C., Pitropakis, N., Papadopoulos, P., Sayeed, S., & Sel, M. (2024, June). Transforming EU Governance: The Digital Integration Through EBSI and GLASS. Presented at 39th IFIP International Conference, Edinburgh, UK

Traditionally, government systems managed citizen identities through disconnected data systems, using simple identifiers and paper-based processes, limiting digital trust and requiring citizens to request identity verification documents. The digital... Read More about Transforming EU Governance: The Digital Integration Through EBSI and GLASS.

A Novel Autonomous Adaptive Frame Size for Time-Slotted LoRa MAC Protocol (2024)
Journal Article
Alahmadi, H., Bouabdallah, F., Al-Dubai, A., & Ghaleb, B. (2024). A Novel Autonomous Adaptive Frame Size for Time-Slotted LoRa MAC Protocol. IEEE Transactions on Industrial Informatics, 20(10), 12284-12293. https://doi.org/10.1109/tii.2024.3417308

LoRa networks represent a promising technology for IoT applications due to their long range, low cost, and energy efficiency. However, their ALOHA-based access method and duty cycle restrictions can limit their scalability and reliability in high-den... Read More about A Novel Autonomous Adaptive Frame Size for Time-Slotted LoRa MAC Protocol.

Integrating Reality: A Hybrid SDN Testbed for Enhanced Realism in Edge Computing Simulations (2024)
Presentation / Conference Contribution
Almaini, A., Koßmann, T., Folz, J., Schramm, M., Heigl, M., & Al-Dubai, A. (2024, June). Integrating Reality: A Hybrid SDN Testbed for Enhanced Realism in Edge Computing Simulations. Presented at UNet24: The International Conference on Ubiquitous Networking, Marrakesh, Morocco

Recent advancements in Software-Defined Networking (SDN) have facilitated its deployment across diverse network types, including edge networks. Given the broad applicability of SDN and the complexity of large-scale environments, establishing a compre... Read More about Integrating Reality: A Hybrid SDN Testbed for Enhanced Realism in Edge Computing Simulations.

Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System (2024)
Journal Article
Cheng, H., Tan, Z., Zhang, X., & Liu, Y. (online). Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System. Chinese Journal of Electronics, https://doi.org/10.23919/cje.2023.00.012

Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been... Read More about Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System.

ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT (2024)
Presentation / Conference Contribution
Huma, Z. E., Ahmad, J., Hamadi, H. A., Ghaleb, B., Buchanan, W. J., & Jan, S. U. (2024, February). ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT. Presented at 2024 2nd International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates

The Internet of Things (IoT) has become an integral part of modern societies, with devices, networks, and applications offering industrial, economic, and social benefits. However, these devices and networks generate vast amounts of data, making them... Read More about ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT.

Adaptive Mobile Chargers Scheduling Scheme based on AHP-MCDM for WRSN (2024)
Journal Article
Makanda, K., Hawbani, A., Wang, . X., Naji, A., Al-Dubai, A., Zhao, L., & Alsamhi, S. H. (online). Adaptive Mobile Chargers Scheduling Scheme based on AHP-MCDM for WRSN. IEEE Transactions on Sustainable Computing, https://doi.org/10.1109/TSUSC.2024.3391316

Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications.
However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the curre... Read More about Adaptive Mobile Chargers Scheduling Scheme based on AHP-MCDM for WRSN.

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review (2024)
Journal Article
Alabsi, A., Hawbani, A., Wang, X., Al-Dubai, A., Hu, J., Aziz, S. A., Kumar, S., Zhao, L., Shvetsov, A. V., & Alsamhi, S. H. (online). Wireless Power Transfer Technologies, Applications, and Future Trends: A Review. IEEE Transactions on Sustainable Computing, https://doi.org/10.1109/TSUSC.2024.3380607

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy- limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., ene... Read More about Wireless Power Transfer Technologies, Applications, and Future Trends: A Review.

Can Federated Models Be Rectified Through Learning Negative Gradients? (2024)
Presentation / Conference Contribution
Tahir, A., Tan, Z., & Babaagba, K. O. Can Federated Models Be Rectified Through Learning Negative Gradients?. Presented at 13th EAI International Conference, BDTA 2023, Edinburgh

Federated Learning (FL) is a method to train machine learning (ML) models in a decentralised manner, while preserving the privacy of data from multiple clients. However, FL is vulnerable to malicious attacks, such as poisoning attacks, and is challen... Read More about Can Federated Models Be Rectified Through Learning Negative Gradients?.

Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices (2023)
Presentation / Conference Contribution
Spalding, A., Tan, Z., & Babaagba, K. O. (2023, November). Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices. Presented at The International Symposium on Intelligent and Trustworthy Computing, Communications, and Networking (ITCCN-2023), Exeter, UK

Data recovery for forensic analysis of both hard drives and solid state media presents its own unique set of challenges. Hard drives face mechanical failures and data fragmentation , but their sequential storage and higher success rates make recovery... Read More about Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices.

TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication (2023)
Presentation / Conference Contribution
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (2023, November). TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication. Presented at The 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2023), Exeter, UK

We are increasingly required to prove our identity when using smartphones through explicit authentication processes such as passwords or physiological biometrics, e.g., authorising online banking transactions or unlocking smartphones. However, these... Read More about TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication.

Machine Un-learning: An Overview of Techniques, Applications, and Future Directions (2023)
Journal Article
Sai, S., Mittal, U., Chamola, V., Huang, K., Spinelli, I., Scardapane, S., Tan, Z., & Hussain, A. (2024). Machine Un-learning: An Overview of Techniques, Applications, and Future Directions. Cognitive Computation, 16, 482-506. https://doi.org/10.1007/s12559-023-10219-3

ML applications proliferate across various sectors. Large internet firms employ ML to train intelligent models using vast datasets, including sensitive user information. However, new regulations like GDPR require data removal by businesses. Deleting... Read More about Machine Un-learning: An Overview of Techniques, Applications, and Future Directions.

Navigating Industry 5.0: A Survey of Key Enabling Technologies, Trends, Challenges, and Opportunities (2023)
Journal Article
Tallat,, R., Hawbani, A., Wang, X., Al-Dubai, A., Zhao, L., Liu, Z., Min, G., Zomaya, A., & Alsamhi, S. (2024). Navigating Industry 5.0: A Survey of Key Enabling Technologies, Trends, Challenges, and Opportunities. Communications Surveys and Tutorials, IEEE Communications Society, 26(2), 1080 - 1126. https://doi.org/10.1109/comst.2023.3329472

This century has been a major avenue for revolutionary changes in technology and industry. Industries have transitioned towards intelligent automation, relying less on human intervention, resulting in the fourth industrial revolution, Industry 4.0. T... Read More about Navigating Industry 5.0: A Survey of Key Enabling Technologies, Trends, Challenges, and Opportunities.

Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection (2023)
Journal Article
Manikandaraja, A., Aaby, P., & Pitropakis, N. (2023). Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection. Computers, 12(10), Article 195. https://doi.org/10.3390/computers12100195

Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices. Because machine learning and artificial intelligence can detect malware better than traditional... Read More about Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection.

A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing (2023)
Journal Article
Zhao, L., Zhao, Z., Zhang, E., Hawbani, A., Al-Dubai, A., Tan, Z., & Hussain, A. (2023). A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing. IEEE Journal on Selected Areas in Communications, 41(11), 3386-3400. https://doi.org/10.1109/jsac.2023.3310062

Vehicle Edge Computing (VEC) is a promising paradigm that exposes Mobile Edge Computing (MEC) to road scenarios. In VEC, task offloading can enable vehicles to offload the computing tasks to nearby Roadside Units (RSUs) that deploy computing capabili... Read More about A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing.