Dr Shidrokh Goudarzi
Shidrokh Goudarzi is a lecturer in Computer Science at the School of Computing and Engineering at the University of West London. Prior to this, she was working at the Centre for Vision, Speech, and Signal Processing (CVSSP), University of Surrey. She was a senior lecturer at the Universiti Kebangsaan Malaysia (UKM). She received her PhD degree in communication systems and wireless networks. She serves as a reviewer for some journals.
Her research interests are in wireless networks, artificial intelligence, machine learning, next-generation networks, Internet of Things (IoT) and mobile/distributed/cloud computing.
Research
As a researcher, she has designed novel architectures comprised of smart things supported by efficient, secure and resilient distributed communications and coordination services. She has also developed smart models for the Internet of Things (IoT) enabling technologies including heterogeneous wireless networks and vehicular networks, and her research results have directly contributed to the technology industry. She has conducted multiple studies that contribute significantly to the IoT technologies, machine learning, and artificial intelligence for smart city resilience applications.
She has on the editorial board and serves as a reviewer in high-ranked journals such as London Journals Press (UK), IEEE Transactions on Industrial Informatics, IEEE Transactions on Network Science and Engineering, IEEE Network, Computational Intelligence, Canadian Journal of Electrical and Computer Engineering, KSII Transactions on Internet and Information Systems Journal, Journal of Engineering and Technological Sciences, Mathematical Problems in Engineering, Advances in Mechanical Engineering, International Journal of Communication Systems, and IEEEAccess.
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Research and publications
Current publications:
Goudarzi, Shidrokh, Seyed Ahmad Soleymani, Wenwu Wang, and Pei Xiao. "UAV-Enabled Mobile Edge Computing for Resource Allocation Using Cooperative Evolutionary Computation." IEEE Transactions on Aerospace and Electronic Systems (2023).
Soleymani, Seyed Ahmad, Shidrokh Goudarzi, Mohammad Hossein Anisi, Haitham Cruickshank, Anish Jindal, and Nazri Kama. "TRUTH: Trust and Authentication Scheme in 5G-IIoT." IEEE Transactions on Industrial Informatics 19, no. 1 (2022): 880-889.
Goudarzi, Shidrokh, Seyed Ahmad Soleymani, Mohammad Hossein Anisi, Mohammad Abdollahi Azgomi, Zeinab Movahedi, Nazri Kama, Hazlifah Mohd Rusli, and Muhammad Khurram Khan. "A privacy-preserving authentication scheme based on Elliptic Curve Cryptography and using Quotient Filter in fog-enabled VANET." Ad Hoc Networks 128 (2022): 102782.
Goudarzi, Shidrokh, Mohammad Hossein Anisi, Seyed Ahmad Soleymani, Masri Ayob, and Sherali Zeadally. "An IoT-based prediction technique for efficient energy consumption in buildings." IEEE Transactions on Green Communications and Networking 5, no. 4 (2021): 2076-2088.
Goudarzi, Shidrokh, Mohammad Hossein Anisi, Domenico Ciuonzo, Seyed Ahmad Soleymani, and Antonio Pescape. "Employing unmanned aerial vehicles for improving handoff using cooperative game theory." IEEE Transactions on Aerospace and Electronic Systems 57, no. 2 (2020): 776-794.
Goudarzi, Shidrokh, Mohammad Hossein Anisi, Hamed Ahmadi, and Leila Musavian. "Dynamic resource allocation model for distribution operations using SDN." IEEE Internet of Things Journal 8, no. 2 (2020): 976-988.
Goudarzi, Shidrokh, Mohammad Hossein Anisi, Nazri Kama, Faiyaz Doctor, Seyed Ahmad Soleymani, and Arun Kumar Sangaiah. "Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm." Energy and Buildings 196 (2019): 83-93.
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Conferences
Goudarzi, Shidrokh, Wenwu Wang, Pei Xiao, Lyudmila Mihaylova, and Simon Godsill. "UAV-enabled Edge Computing for Optimal Task Distribution in Target Tracking." In 2022 25th International Conference on Information Fusion (FUSION), pp. 1-7. IEEE, 2022.
Zhao, Jinzheng, Peipei Wu, Shidrokh Goudarzi, Xubo Liu, Jianyuan Sun, Yong Xu, and Wenwu Wang. "Visually Assisted Self-supervised Audio Speaker Localization and Tracking." In 2022 30th European Signal Processing Conference (EUSIPCO), pp. 787-791. IEEE, 2022.
Liu, Xingchi, Qing Li, Jiaming Liang, Jinzheng Zhao, Peipei Wu, Chenyi Lyu, Shidrokh Goudarzi et al. "Advanced machine learning methods for autonomous classification of ground vehicles with acoustic data." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, vol. 12113, pp. 524-533. SPIE, 2022.
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Research degree supervision
Advanced Communication Systems- Federated Learning-Driven Resource Allocation for Optimal V2X Communication
This approach leverages federated learning—a decentralized machine learning technique—to optimize resource allocation in vehicle-to-everything (V2X) communication networks. By enabling vehicles to collaboratively learn from local data without sharing it, this method enhances the efficiency of resource distribution while preserving privacy, thus improving overall communication performance and responsiveness in dynamic driving environments. - Energy-Efficient Edge Computing Strategies in UAV-Assisted VANETs
This research focuses on developing strategies for energy-efficient edge computing in vehicular ad-hoc networks (VANETs) that utilize unmanned aerial vehicles (UAVs). By processing data closer to the source (at the network's edge), this approach reduces latency and energy consumption, enabling quicker decision-making and reducing the load on centralized servers. This is particularly useful in scenarios like traffic management and emergency response. - Dynamic Spectrum Management Using Reinforcement Learning in ORAN Networks
This topic involves applying reinforcement learning techniques to dynamically manage the spectrum in Open Radio Access Networks (ORAN). By intelligently adjusting frequency allocations based on real-time network conditions and demands, this approach can enhance the efficiency and capacity of wireless communication, leading to improved connectivity and reduced interference among users. - Privacy-Preserving Models for Resource Allocation in UAV-Enabled Networks
This research area focuses on designing models that ensure privacy while allocating resources in networks supported by UAVs. By employing cryptographic techniques and privacy-preserving algorithms, this work aims to protect sensitive data during resource allocation processes, thereby maintaining user confidentiality and fostering trust in UAV-assisted communications. - Improving V2X Communication Reliability Through Intelligent Resource Management
This topic addresses strategies to enhance the reliability of V2X communications through intelligent resource management techniques. By utilizing advanced algorithms to manage bandwidth, power, and other resources dynamically, this research aims to ensure stable and dependable communication channels for vehicles, improving safety and efficiency in intelligent transportation systems.
If you are interested in pursuing your PhD in these areas, please contact Dr Shidrokh Goudarzi at shidrokh.goudarzi@uwl.ac.uk.
