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Outputs (14)

A contract theory-based incentive mechanism for UAV-enabled VR-based services in 5G and beyond (2023)
Journal Article
Dang, T. N., Manzoor, A., Tun, Y. K., Kazmi, S. M. A., Han, Z., & Hong, C. S. (2023). A contract theory-based incentive mechanism for UAV-enabled VR-based services in 5G and beyond. IEEE Internet of Things, 10(18), 16465 - 16479. https://doi.org/10.1109/JIOT.2023.3268320

The proliferation of novel infotainment services such as Virtual Reality(VR)-based services has fundamentally changed the existing mobile networks. These bandwidth-hungry services expanded at a tremendously rapid pace, thus, generating a burden of da... Read More about A contract theory-based incentive mechanism for UAV-enabled VR-based services in 5G and beyond.

Joint communication, computation, and control for computational task offloading in vehicle-assisted multi-access edge computing (2022)
Journal Article
Dang, T. N., Manzoor, A., Tun, Y. K., Kazmi, S. M. A., Haw, R., Hong, S. H., …Hong, C. S. (2022). Joint communication, computation, and control for computational task offloading in vehicle-assisted multi-access edge computing. IEEE Access, 10, 122513-122529. https://doi.org/10.1109/ACCESS.2022.3220251

Future generation of Electric Vehicles (EVs) equipped with modern technologies will impose a significant burden on computation and communication to the network due to the vast extension of onboard infotainment services. To overcome this challenge, mu... Read More about Joint communication, computation, and control for computational task offloading in vehicle-assisted multi-access edge computing.

Multiple adversarial domains adaptation approach for mitigating adversarial attacks effects (2022)
Journal Article
Rasheed, B., Khan, A., Ahmad, M., Mazzara, M., & Kazmi, S. M. (2022). Multiple adversarial domains adaptation approach for mitigating adversarial attacks effects. International Transactions on Electrical Energy Systems, 2022, 1-11. https://doi.org/10.1155/2022/2890761

Although neural networks are near achieving performance similar to humans in many tasks, they are susceptible to adversarial attacks in the form of a small, intentionally designed perturbation, which could lead to misclassifications. The best defense... Read More about Multiple adversarial domains adaptation approach for mitigating adversarial attacks effects.

PbCP: A profit-based cache placement scheme for next-generation IoT-based ICN networks (2022)
Journal Article
Serhane, O., Yahyaoui, K., Nour, B., Hussain, R., Kazmi, A., & Moungla, H. (2022). PbCP: A profit-based cache placement scheme for next-generation IoT-based ICN networks. Computer Communications, 194, 311-320. https://doi.org/10.1016/j.comcom.2022.07.044

The large number of connected devices generating a massive amount of data in the emerging technologies such as the Internet of Things (IoT), will surpass the capabilities of the current Internet infrastructure. Therefore, it is essential to design a... Read More about PbCP: A profit-based cache placement scheme for next-generation IoT-based ICN networks.

Computing on wheels: A deep reinforcement learning-based approach (2022)
Journal Article
Kazmi, S. M. A., Ho, T. M., Nguyen, T. T., Fahim, M., Khan, A., Piran, M. J., & Baye, G. (2022). Computing on wheels: A deep reinforcement learning-based approach. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22535-22548. https://doi.org/10.1109/TITS.2022.3165662

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation... Read More about Computing on wheels: A deep reinforcement learning-based approach.

ApplianceNet: A neural network based framework to recognize daily life activities and behavior in smart home using smart plugs (2022)
Journal Article
Fahim, M., Kazmi, S. M. A., & Khattak, A. M. (2022). ApplianceNet: A neural network based framework to recognize daily life activities and behavior in smart home using smart plugs. Neural Computing and Applications, 34(15), 12749-12763. https://doi.org/10.1007/s00521-022-07144-1

A smart plug can transform the typical electrical appliance into a smart multi-functional device, which can communicate over the Internet. It has the ability to report the energy consumption pattern of the attached appliance which offer the further a... Read More about ApplianceNet: A neural network based framework to recognize daily life activities and behavior in smart home using smart plugs.

Online service provisioning in NFV-enabled networks using deep reinforcement learning (2022)
Journal Article
Nouruzi, A., Zakeri, A., Javan, M. R., Mokari, N., Hussain, R., & Kazmi, S. M. A. (2022). Online service provisioning in NFV-enabled networks using deep reinforcement learning. IEEE Transactions on Network and Service Management, 19(3), 3276-3289. https://doi.org/10.1109/TNSM.2022.3159670

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of ne... Read More about Online service provisioning in NFV-enabled networks using deep reinforcement learning.

A novel deep reinforcement learning-based approach for task-offloading in vehicular networks (2022)
Conference Proceeding
Kazmi, S. M. A., Otoum, S., Hussain, R., & Mouftah, H. T. (2022). A novel deep reinforcement learning-based approach for task-offloading in vehicular networks. In 2021 IEEE Global Communications Conference (GLOBECOM) (1-6). https://doi.org/10.1109/GLOBECOM46510.2021.9685073

Next-generation vehicular networks will impose unprecedented computation demand due to the wide adoption of compute-intensive services with stringent latency requirements. Computational capacity of vehicular networks can be enhanced by integration of... Read More about A novel deep reinforcement learning-based approach for task-offloading in vehicular networks.

On the fairness of generative adversarial networks (GANs) (2022)
Conference Proceeding
Kenfack, P. J., Arapov, D. D., Hussain, R., Kazmi, S. A., & Khan, A. (2022). On the fairness of generative adversarial networks (GANs). In 2021 International Conference "Nonlinearity, Information and Robotics" (NIR) (1-7). https://doi.org/10.1109/NIR52917.2021.9666131

Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. With their ability to directly learn the probability distribution of data and then sample synthetic realistic data. Many applications have emerged, using G... Read More about On the fairness of generative adversarial networks (GANs).

API security in large enterprises: Leveraging machine learning for anomaly detection (2021)
Conference Proceeding
Baye, G., Hussain, F., Oracevic, A., Hussain, R., & Ahsan Kazmi, S. (2021). API security in large enterprises: Leveraging machine learning for anomaly detection. In 2021 International Symposium on Networks, Computers and Communications (ISNCC) (1-6). https://doi.org/10.1109/ISNCC52172.2021.9615638

Large enterprises offer thousands of micro-services applications to support their daily business activities by using Application Programming Interfaces (APIs). These applications generate huge amounts of traffic via millions of API calls every day, w... Read More about API security in large enterprises: Leveraging machine learning for anomaly detection.