@inproceedings{MTMT:34777302,
	title = {Data pre-processing to improve anomaly detection in the telemetry of a server farm},
	url = {https://m2.mtmt.hu/api/publication/34777302},
	author = {Vajda, Dániel László and Do, Van Tien and Farkas, Károly},
	booktitle = {2nd Workshop on Intelligent Infocommunication Networks, Systems and Services},
	doi = {10.3311/WINS2024-012},
	unique-id = {34777302},
	abstract = {Server farm telemetry plays a crucial role in overseeingand ensuring the health, performance, and efficiency ofinterconnected servers, which deliver computing resources forapplications and services. An essential step in the telemetryprocess is the analysis of data collected from servers. Anomaly detectionis a significant task in this step: by automatically detectingsigns of abnormal behaviour, operators can prevent issues fromescalating into major operational setbacks. This paper presents anew pre-processing procedure to improve our previous anomalydetection algorithms and state-of-the-art detectors. Motivatedby the finding that periodic datasets often pose challenges toanomaly detection, our method transforms a dataset by removingmodes containing regular, periodic behaviour while preservingsigns of anomalies. Our proposed pre-processing procedureimproved the performance of all anomaly detectors we tested,while our latest detector achieved substantially better resultson periodic data than originally. We also present an extensivenumerical analysis of our pre-processing parameters and stateof-the-art anomaly detection algorithms regarding performancevia the F-score metric.},
	year = {2024},
	pages = {67-72},
	orcid-numbers = {Farkas, Károly/0000-0001-6965-2689}
}

@inproceedings{MTMT:34177678,
	title = {Deep Reinforcement Learning for Jointly Resource Allocation and Trajectory Planning in UAV-Assisted Networks},
	url = {https://m2.mtmt.hu/api/publication/34177678},
	author = {Jwaifel, Arwa Mahmoud and Do, Van Tien},
	booktitle = {Computational Collective Intelligence},
	doi = {10.1007/978-3-031-41456-5_6},
	volume = {14162 LNAI},
	unique-id = {34177678},
	year = {2023},
	pages = {71-83},
	orcid-numbers = {Jwaifel, Arwa Mahmoud/0000-0002-1186-1012}
}

@article{MTMT:33733394,
	title = {Efficient Multi-UAV Assisted Data Gathering Schemes for Maximizing the Operation Time of Wireless Sensor Networks in Precision Farming},
	url = {https://m2.mtmt.hu/api/publication/33733394},
	author = {Nguyen, Khanh-Van and Nguyen, Chi-Hieu and Do, Van Tien and Rotter, Csaba},
	doi = {10.1109/TII.2023.3248616},
	journal-iso = {IEEE T IND INFORM},
	journal = {IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS},
	volume = {19},
	unique-id = {33733394},
	issn = {1551-3203},
	year = {2023},
	eissn = {1941-0050},
	pages = {11664-11674},
	orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674}
}

@article{MTMT:32916674,
	title = {Impact of Co-channel Interference on the Performance of Cooperative Diversity Systems over α–μ Fading Channels},
	url = {https://m2.mtmt.hu/api/publication/32916674},
	author = {Jwaifel, Arwa Mahmoud and Ghareeb, Ibrahim and Do, Van Tien},
	doi = {10.1007/s10776-022-00563-w},
	journal-iso = {INT J WIREL INF NETW},
	journal = {INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS},
	volume = {29},
	unique-id = {32916674},
	issn = {1068-9605},
	abstract = {This paper investigates the impact of the co-channel interference on the performance of cooperative diversity networks. Selection combination technique is applied to the paths from multiple relay branches with no link directly connects the source and destination nodes. In addition, we derive the statistical characteristics of the upper bound of the SINR under the alpha-mu fading channel model, which then were used to derived the outage (P-out) and error (P-b(e)) probabilities for the cooperative network.},
	keywords = {Amplify-and-forward (AF); co-channel interference (CCI); Cooperative diversity systems; Selection combination (SC)},
	year = {2022},
	pages = {232-239},
	orcid-numbers = {Jwaifel, Arwa Mahmoud/0000-0002-1186-1012}
}

@article{MTMT:32532803,
	title = {Scaling UPF Instances in 5G/6G Core with Deep Reinforcement Learning},
	url = {https://m2.mtmt.hu/api/publication/32532803},
	author = {Nguyen, Tuan Hai and Do, Van Tien and Rotter, Csaba},
	doi = {10.1109/ACCESS.2021.3135315},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {9},
	unique-id = {32532803},
	issn = {2169-3536},
	abstract = {In the 5G core and the upcoming 6G core, the User Plane Function (UPF) is responsible for the transportation of data from and to subscribers in Protocol Data Unit (PDU) sessions. The UPF is generally implemented in software and packed into either a virtual machine or container that can be launched as a UPF instance with a specific resource requirement in a cluster. To save resource consumption needed for UPF instances, the number of initiated UPF instances should depend on the number of PDU sessions required by customers, which is often controlled by a scaling algorithm. In this paper, we investigate the application of Deep Reinforcement Learning (DRL) for scaling UPF instances that are packed in the containers of the Kubernetes container-orchestration framework. We propose an approach with the formulation of a threshold-based reward function and adapt the proximal policy optimization (PPO) algorithm. Also, we apply a support vector machine (SVM) classifier to cope with a problem when the agent suggests an unwanted action due to the stochastic policy. Extensive numerical results show that our approach outperforms Kubernetes’s built-in Horizontal Pod Autoscaler (HPA). DRL could save 2.7–3.8% of the average number of Pods, while SVM could achieve 0.7–4.5% saving compared to HPA.},
	year = {2021},
	eissn = {2169-3536},
	pages = {165892-165906},
	orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674}
}

@article{MTMT:32409790,
	title = {Correction to: Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks. (Wireless Networks, (2021), 27, 4, (3073-3089), 10.1007/s11276-021-02569-3)},
	url = {https://m2.mtmt.hu/api/publication/32409790},
	author = {Nguyen, Khanh-Van and Nguyen, Chi-Hieu and Phi, Le Nguyen and Do, Van Tien and Chlamtac, Imrich},
	doi = {10.1007/s11276-021-02607-0},
	journal-iso = {WIREL NETW},
	journal = {WIRELESS NETWORKS},
	volume = {27},
	unique-id = {32409790},
	issn = {1022-0038},
	abstract = {An incorrect version of Figure 7 appeared in our paper entitled "Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks" published in Wireless Networks.},
	year = {2021},
	eissn = {1572-8196},
	pages = {3091-3091}
}

@article{MTMT:32150027,
	title = {Performance Analysis of Cognitive Radio Networks With Burst Dynamics},
	url = {https://m2.mtmt.hu/api/publication/32150027},
	author = {Xu, Q. and Li, S. and Do, Van Tien and Jia, K. and Yang, N.},
	doi = {10.1109/ACCESS.2021.3103321},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {9},
	unique-id = {32150027},
	issn = {2169-3536},
	year = {2021},
	eissn = {2169-3536},
	pages = {110627-110638}
}

@article{MTMT:32073418,
	title = {Optimizing the resource usage of actor-based systems},
	url = {https://m2.mtmt.hu/api/publication/32073418},
	author = {Nguyen, Tuan Hai and Do, Van Tien and Rotter, Csaba},
	doi = {10.1016/j.jnca.2021.103143},
	journal-iso = {J NETW COMPUT APPL},
	journal = {JOURNAL OF NETWORK AND COMPUTER APPLICATIONS},
	volume = {190},
	unique-id = {32073418},
	issn = {1084-8045},
	abstract = {Runtime environments for IoT data processing systems based on the actor model often apply a thread pool to serve data streams. In this paper, we propose an approach based on Reinforcement Learning (RL) to find a trade-off between the the resource (thread pool in server machines) usage and the quality of service for data streams. We compare our approach and the Thread Pool Executor of Akka, an open-source software toolkit. Simulation results show that our approach outperforms ThreadPoolExecutor with the timeout rule when the thread start times are not negligible. Furthermore, the tuning of our approach is not tedious as the application of the timeout rule requires.},
	keywords = {reinforcement learning; resource management; IoT; actor},
	year = {2021},
	eissn = {1095-8592},
	orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674}
}

@article{MTMT:32055132,
	title = {A Queueing Model for Threshold-based Scaling of UPF Instances in 5G Core},
	url = {https://m2.mtmt.hu/api/publication/32055132},
	author = {Rotter, Csaba and Do, Van Tien},
	doi = {10.1109/ACCESS.2021.3085955},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {9},
	unique-id = {32055132},
	issn = {2169-3536},
	year = {2021},
	eissn = {2169-3536},
	pages = {81443-81453},
	orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674}
}

@article{MTMT:32048751,
	title = {Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks},
	url = {https://m2.mtmt.hu/api/publication/32048751},
	author = {Nguyen, Khanh-Van and Nguyen, Chi-Hieu and Le, Nguyen Phi and Do, Van Tien and Chlamtac, Imrich},
	doi = {10.1007/s11276-021-02569-3},
	journal-iso = {WIREL NETW},
	journal = {WIRELESS NETWORKS},
	volume = {27},
	unique-id = {32048751},
	issn = {1022-0038},
	abstract = {A quest for geographic routing schemes of wireless sensor networks when sensor nodes are deployed in areas with obstacles has resulted in numerous ingenious proposals and techniques. However, there is a lack of solutions for complicated cases wherein the source or the sink nodes are located close to a specific hole, especially in cavern-like regions of large complex-shaped holes. In this paper, we propose a geographic routing scheme to deal with the existence of complicated-shape holes in an effective manner. Our proposed routing scheme achieves routes around holes with the (1+$$\\epsilon$$)-stretch. Experimental results show that our routing scheme yields the highest load balancing and the most extended network lifetime compared to other well-known routing algorithms as well.},
	year = {2021},
	eissn = {1572-8196},
	pages = {3073-3089}
}