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Machine Learning and Computer Vision for Smart City Applications
We have explored the integration of machine learning (ML) and computer vision to develop innovative solutions for smart city applications. A significant focus of my work has been on surveillance systems, particularly anomaly detection, where I designed and implemented models capable of identifying unusual activities in real-time. These systems leverage advanced computer vision techniques to process and analyze video data effectively. To address the challenges posed by resource-constrained devices, such as edge or IoT devices commonly deployed in smart cities, I developed lightweight, energy-efficient algorithms that maintain high accuracy while optimizing computational and memory requirements. My contributions not only enhance urban safety and monitoring but also underscore the importance of scalable and sustainable technological solutions for modern smart city ecosystems.
Selected Publications
In preparation
Generative AI for Wireless Systems
Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms. Federated learning can be a promising solution for enabling IoT-based smart applications. In this article, we present the primary design aspects for enabling federated learning at the network edge. We model the incentive-based interaction between a global server and participating devices for federated learning via a Stackelberg game to motivate the participation of the devices in the federated learning process. We present several open research challenges with their possible solutions. Finally, we provide an outlook on future research.
Selected Publications
Vision Language Models for Edge Networks: A Survey, (Submitted to IEEE IoT Journal)
Joint Learning and Task-Offloading for Large Language Models for Edge Networks: A Matching Game-Based Scheme, In preparation.
Metaverse and Digital Twins for Internet of Things
L. U. Khan,et al. "Metaverse for Wireless Systems: Architecture, Advances, Standardization, and Open Challenges." arXiv preprint arXiv:2301.11441 (2023).
The burgeoning landscape of emerging wireless applications serves as a pivotal catalyst driving the evolution of innovative wireless system designs. These designs may find their foundation in the metaverse, an immersive digital realm that incorporates virtual representations of physical world systems, bolstered by complementary technologies such as optimization theory, machine learning, and blockchain. Within the metaverse and digital framework, preemptive intelligent analytics are deployed, proactively optimizing wireless system resources well in advance of user requests, thereby enhancing overall resource management efficiency. Furthermore, the metaverse paradigm facilitates self-sustainability, reducing the need for extensive intervention by network operators in the operation of wireless systems. However, it is essential to acknowledge that the metaverse, while offering numerous advantages, confronts its share of challenges. Consequently, there exists a compelling imperative for research that delves into pivotal enablers and design considerations encompassing both the metaverse's role in wireless systems and the reciprocal impact of wireless technology on the metaverse. Additionally, careful scrutiny of metaverse-based system architecture, management protocols, reliability mechanisms, and security measures becomes paramount. Moreover, standardization initiatives aimed at establishing a robust framework for metaverse-enabled wireless systems are equally essential. These standards will play an integral role in ensuring interoperability, scalability, and the seamless integration of metaverse technologies into the wireless landscape.
Selected Publications
L. U. Khan, M Guizani,, Resource Optimized Network Virtualization Empowered Metaverse for Wireless Networks, IEEE International Conference on Communications (IoTSN-Symposium), 2024
F. AlKhoori, L. U. Khan, M Guizani, M Takac,, Latency-Aware Placement of Vehicular Metaverses using Virtual Network Functions, Simulation Modelling Practice and Theory, 2024
M Alghfeli, L.U. Khan, M Guizani, B Ouni, , A Joint Sensing, Communication, and Task Offloading Framework for Vehicular Metaverse,IEEE Wireless Communications and Networking Conference, 2024
L.U. Khan, A Elhagry, M Guizani, Edge Intelligence Empowered Vehicular Metaverse: Key Design Aspects and Future Directions, IEEE Internet of Things Magazine, 2024
L. U. Khan, Z Han, D Niyato, M Guizani, CS Hong, Metaverse for Wireless Systems: Vision, Enablers, Architecture, and Future Directions, IEEE Wireless Communications Magazine, 2024
L. U. Khan, Mohsen Guizani, Ibrar Yaqoob, Aiman Erbad, Ala Al-Fuqaha, Zhu Han, Network Virtualization Empowered Metaverse: A Hierarchical Matching Approach, TexhArixv, 2023
L. U. Khan, Z. Han, D. Niyato, E. Hossain, C. S. Hong, Metaverse for Wireless Systems: Vision, Enablers, Architecture, and Future Directions, arXiv, 2022.
L. U. Khan, I. Yaqoob, K. Salah, C. S. Hong, D. Niyato, Z. Han, M. Guizani, Machine learning for metaverse-enabled wireless systems: vision, requirements, and challenges. arXiv preprint arXiv:2211.03703, 2023.
W. Wang, Y. Yang, L. U. Khan, D. Niyato, Z. Han, M. Guizani, Digital Twin for Wireless Networks: Security Attacks and Solutions, IEEE Wireless Communications (In-Press), 2023.
L. U. Khan, Z. Han, W. Saad, E. Hossain, M. Guizani, C. S. Hong, Digital twin of wireless systems: Overview, taxonomy, challenges, and opportunities, IEEE Communications Surveys & Tutorials, 2022
L. U. Khan, W. Saad, D. Niyato, Z. Han, C. S. Hong, Digital-twin-enabled 6G: Vision, architectural trends, and future directions, IEEE Communications Magazine, vol. 60, No. 1, 2022
Federated Learning for Internet of Things
L. U. Khan et al., Federated learning for edge networks: Resource optimization and incentive mechanism." IEEE Communications Magazine 58, no. 10 (2020): 88-93.
L. U. Khan et al, "Federated learning for internet of things: Recent advances, taxonomy, and open challenges." IEEE Communications Surveys & Tutorials 23.3 (2021): 1759-1799.
The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithms for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end devices and the aggregation server. Mainly, there are two aspects: (a) federated learning for wireless and (b) wireless for federated. Federated learning for wireless deals with solving wireless system problems (e.g., resource management) using federated learning models. On the other hand, wireless for federated learning deals with wireless resource management for enabling efficient federated learning for sharing of updates during the distributed training phase.
Selected Publications
L. U. Khan, M. Guizani, A. Al-Fuqaha, C. S. Hong, D. Niyato, Z. Han, A Joint Communication and Learning Framework for Hierarchical Split Federated Learning, IEEE Internet of Things Journal, 2023
L. U. Khan, E. Mustafa, J. Shuja, F. Rehman, K. Bilal, Z. Han, C. S. Hong, Federated learning for digital twin-based vehicular networks: Architecture and challenges. IEEE Wireless Communications, 2023
L. U. Khan, W. Saad, Z. Han, E. Hossain, C. S. Hong, Federated learning for internet of things: Recent advances, taxonomy, and open challenges, IEEE Communications Surveys & Tutorials, vol. 23, No. 3, 2021
L. U. Khan, Y. K. Tun, M. Alsenwi, M. Imran, Z. Han, C. S. Hong, A dispersed federated learning framework for 6G-enabled autonomous driving cars, IEEE Transactions on Network Science and Engineering, 2022
L. U. Khan, W. Saad, Z. Han, C. S. Hong, Dispersed federated learning: Vision, taxonomy, and future directions. IEEE Wireless Communications, vol. 28, No. 5, 2021
L. U. Khan, S. R. Pandey, N. H. Tran, W. Saad, Z. Han, M. N. Nguyen, C. S. Hong, Federated learning for edge networks: Resource optimization and incentive mechanism, IEEE Communications Magazine, vol. 58, no. 10, 2020
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