| 1. |
- Chen, Chen, et al.
(author)
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A V2V Emergent Message Dissemination Scheme for 6G-Oriented Vehicular Networks
- 2023
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In: Chinese journal of electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1022-4653 .- 2075-5597. ; 32:6, s. 1179-1191
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Journal article (peer-reviewed)abstract
- To ensure traffic safety and improve traffic efficiency, vehicular networks come up with multiple types of messages for safety and efficiency applications. In sixth-generation (6G) systems, these messages should be timely and error-free disseminated through vehicle-to-vehicle (V2V) communication to ensure traffic safety and efficiency. V2V supports direct communication between two vehicle user equipments, regardless of whether a base station is involved. We propose a packet delivery ratio (PDR)-based message dissemination scheme (PDR-MD) between V2V in 6G-oriented vehicular networks to select relay vehicles when broadcasting emergent messages. This scheme grasps the balance between vehicle distance and PDR so as to reduce transmission delay while ensuring reliable PDR. We compared the PDR-MD scheme with other probabilistic broadcasting schemes. The experimental results show that the PDR-MD protocol can maintain close to 95% and above PDR in transmitting emergent messages, and the transfer rate stays below 40%.
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| 2. |
- Feng, Jie, et al.
(author)
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Task Scheduling and Resource Management in MEC-Enabled Computing Networks
- 2022
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In: Mobile Networks And Management, MONAMI 2021. - Cham : Springer Nature. ; , s. 127-137
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Conference paper (peer-reviewed)abstract
- The rapid development of the fifth generation (5G) promotes a variety of new applications, which will pose a huge challenge to the computing resources of networks. Computing networks is a promising technology, which can provide ubiquitous computing resources for applications in 5G. However, resource optimization in computing networks is still an open problem. In this paper, we propose a novel resource allocation framework for computing networks to investigate the energy consumption minimization problem in terms of delay constraint. To tackle the problem, we propose a dynamic task scheduling and resource allocation algorithm to utilizing the Lyapunov optimization method, which doesn't need to know any prior knowledge of networks. In order to reduce the complexity of solving the problem, we decompose the original problem into several sub-problem to solve. Particulary, the solutions of transmit power and subcarrier assignment are obtained by using the Lagrangian dual decomposition method. The solutions of computation time, postponing time, and CPU-cycle frequency are achieved in the closed form. Simulation results show that the performance of the proposed algorithms and can achieve the tradeoff between the average delay and the average energy consumption.
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| 3. |
- Ju, Ying, et al.
(author)
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DRL-based Beam Allocation in Relay-aided Multi-user MmWave Vehicular Networks
- 2022
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In: IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS). - : Institute of Electrical and Electronics Engineers (IEEE).
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Conference paper (peer-reviewed)abstract
- Millimeter wave (mmWave) communication can realize high transmission rates in vehicular networks. Nevertheless, severe blocking effects and high mobility of vehicles would seriously affect downlink services for vehicles. To ensure communication quality and stability, this paper jointly explores beam allocation and relay selection in mmWave vehicular networks from the perspective of artificial intelligence-driven model. We utilize queuing theory to simulate dynamic distributions of vehicles and firstly propose a deep reinforcement learning (DRL) based joint beam allocation and relay selection scheme to mitigate the blocking effects and optimize the total communication capacity. When the expected downlink is blocked, mmWave base station (mmBS) can select appropriate idle vehicles as the relay nodes for service. Besides, we set the capacity threshold when designing the scheme to guarantee each target vehicle can obtain the ideal service. Through proper training, mmBS can intelligently find an optimal solution for the constantly updated vehicular networks based on the location of vehicles. Simulation results demonstrate the effectiveness of our scheme, which can restrain the transmission outage caused by random blockage and improve the total communication capacity of vehicular networks.
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| 4. |
- Ju, Ying, et al.
(author)
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Joint Secure Offloading and Resource Allocation for Vehicular Edge Computing Network : A Multi-Agent Deep Reinforcement Learning Approach
- 2023
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In: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 24:5, s. 5555-5569
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Journal article (peer-reviewed)abstract
- The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share the frequency spectrum preoccupied with the vehicle-to-vehicle (V2V) communication links. We use Wyner's wiretap coding scheme to obtain the achievable secrecy rate and guarantee that confidential information cannot be decoded by multiple mobile eavesdroppers. We aim at minimizing the system processing delay while securing the wireless offloading process, by jointly optimizing the transmit power, the frequency spectrum selection and the computation resource allocation. We formulate the optimization problem as a multi-agent collaborative optimal decision problem and solve it with a double deep Q-learning algorithm. Besides, we set a punishment mechanism for the rate degradation to guarantee the communication quality of each V2V link. Simulation results demonstrate that multiple VU agents adopting the SORA scheme can rapidly adapt to the highly dynamic VEC networks and cooperate to improve the system delay performance while increasing the secrecy probability.
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| 5. |
- Ju, Ying, et al.
(author)
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Reliability-Security Tradeoff Analysis in mmWave Ad Hoc-based CPS
- 2024
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In: ACM transactions on sensor networks. - : Association for Computing Machinery (ACM). - 1550-4867 .- 1550-4859. ; 20:2
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Journal article (peer-reviewed)abstract
- Cyber-physical systems (CPS) offer integrated resolutions for various applications by combining computer and physical components and enabling individual machines to work together for much more excellent benefits. The ad hoc-based CPS provides a promising architecture due to its decentralized nature and destructiveresistance. A growing number of information leakage events in CPSs and the following serious consequences have aroused ubiquitous concern about information security. In this article, we combine physical layer security solutions and millimeter-wave (mmWave) techniques to safeguard the ad hoc network and investigate the reliability-security tradeoff by taking user demands for the network into account, where eavesdroppers attempt to intercept messages. For the secrecy enhancements, we adopt an artificial noise (AN) assisted transmission scheme, in which AN is employed to create non-cancellable interference to eavesdroppers. The reliability and security are correspondingly characterized by the connection outage probability and secrecy outage probability, and their analytical expressions of them are attained through theoretical analysis for the purpose of the tradeoff issue discussion. Our results reveal that secrecy performance in mmWave ad hoc networks gains significant improvement through the use of AN. It also shows that given total transmit power, there exists a tradeoff between reliability and security to achieve optimal outage performance.
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| 6. |
- Liu, Lei, et al.
(author)
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Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing
- 2023
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In: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 24:12, s. 15513-15526
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Journal article (peer-reviewed)abstract
- Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.
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