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Your search keyword '"Cao, Haotong"' showing total 84 results
Start Over Author "Cao, Haotong" Publisher ieee
84 results on '"Cao, Haotong"'

21. Dynamic Virtual Resource Allocation Mechanism for Survivable Services in Emerging NFV-Enabled Vehicular Networks.

Author

Cao, Haotong, Zhao, Haitao, Luo, Daniel Xiapu, Kumar, Neeraj, and Yang, Longxiang

Abstract

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism. [ABSTRACT FROM AUTHOR]

Published
2022
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22. Intelligent Virtual Resource Allocation of QoS-Guaranteed Slices in B5G-Enabled VANETs for Intelligent Transportation Systems.

Author

Cao, Haotong, Garg, Sahil, Kaddoum, Georges, Hassan, Mohammad Mehedi, and AlQahtani, Salman A.

Abstract

5G communication technologies and networks help researchers and engineers look into intelligent transportation systems (ITS) with a new eye, including vehicular ad hoc networks (VANET) application. Network function virtualization (NFV) and network slicing (NS) are accepted as two most promising technologies towards the agile and elastic network architecture of 5G and beyond 5G (B5G). However, previous researchers studied NFV and NS separately. In addition, learning technologies, such as reinforcement leaning (RL), graph-based learning, emerge so as to enhance the network intelligence and resource allocation in recent years. Inspired from these, we jointly explore intelligent resource allocation issue within B5G-enabled VANETs. At first, the novel virtual resource allocation framework supporting NFV and NS for providing quality of service (QoS)-guaranteed slices is constructed. Then, we formulate the virtual resource allocation of slices as the optimization problem, having the goals of providing guaranteed QoS performance and maximizing the net profit. Considering the non convex attributes of the formulated optimization problem, we propose one intelligent and feasible algorithm instead, including the details of the proposed intelligent algorithm. We record the results in order to validate the feasibility and highlights of our proposed algorithm. For example, our intelligent algorithm has the slice acceptance advantage of 5%, comparing with the best existing work. [ABSTRACT FROM AUTHOR]

Published
2022
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27. Energy-Saving Deployment Optimization and Resource Management for UAV-Assisted Wireless Sensor Networks With NOMA.

Author

Zhai, Daosen, Wang, Chen, Zhang, Ruonan, Cao, Haotong, and Yu, Fei Richard

Subjects
WIRELESS sensor networks, INDUSTRIAL efficiency, RESOURCE management, UPLOADING of data, POWER transmission, NUMERICAL analysis
Abstract

Energy-saving techniques are vital for the battery-powered sensor devices (SDs), which affect their lifetime. In this paper, we propose an air-and-ground cooperative wireless sensor network (AGWSN), wherein several UAVs are deployed as aerial access points (AAPs) to assist the terrestrial access point (TAP) for data collecting. The positions of the AAPs can be modified to approach the cell-edge SDs, therefore reducing the energy of the SDs expended in uploading data. To fully exploit the potential of the AGWSN, we formulate a joint AAP position optimization, channel allocation, and power control problem to minimize the total power consumption of all SDs subject to their decoding threshold. To solve the formulated problem, we first analyze the optimal user pairing rule in each cell and based on the rule propose a maximum-weighted-independent-set inspired algorithm for the AAP position optimization. Then, we remodel the channel allocation problem as an interference minimization problem and devise a K-CUT based algorithm to solve it. We further propose a low-complex iterative algorithm to obtain the optimal transmission power for each SD. The performance of the proposed algorithms is evaluated via theoretical analysis and numerical simulation. Simulation results indicate that if the intracell and intercell interference are not well coordinated, the superiorities of the AGWSN cannot be developed, and its performance is even worse than the traditional terrestrial network (TTN). Cooperated with our algorithms, the AGWSN significantly outperforms the TTN in terms of total power consumption and probability of successful decoding. [ABSTRACT FROM AUTHOR]

Published
2022
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