Multi-Objective Optimization for Resource Allocation in Vehicular Cloud Computing Networks

Wei, Wenting; Yang, Ruying; Gu, Huaxi; Zhao, Weike; Chen, Chen; Wan, Shaohua

doi:10.1109/tits.2021.3091321

Cited by 77 publications

(30 citation statements)

References 34 publications

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“…The weight coefficient α of vx1 and vx2 is calculated by each wheel speed credibility, as shown in Eq. (10). Because when only a single wheel is stable, the algorithm can still output an accurate result, the range of f1 is [0.5,1].…”

Section: Principle Of Reference Speed Estimationmentioning

confidence: 99%

“…In addition, the new electric drive [5] or X-by-wire platform has the characteristics of faster response and more accurate execution [6], which also puts forward higher requirements for vehicle state identification algorithm. Estimation based on traditional single sensor information cannot meet the above requirements, so it is necessary to use information fusion technology [7,8] to obtain more detailed and accurate conclusions from multi-source information of the single vehicle and even from internet of vehicles [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Vehicle State Estimation Based on Multidimensional Information Fusion

et al. 2022

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As the core input parameters of various control systems, the real-time and accurate acquisition of reference speed, mass and road slope is the key factor to improve the performance of intelligent vehicle dynamics control. Therefore, the parameter estimation method based on multi-dimensional information fusion is proposed in this paper. A comprehensive evaluation of wheel dynamics state is realized by information fusion, which is quantified in terms of wheel speed credibility. Then the calculated dynamic speed and auxiliary speed are weighted coupled to achieve accurate estimation of reference speed, which avoids the influence of unstable wheels. Similarly, the method to calculate the confidence factor of mass estimation is established in order to screen the vehicle state suitable for estimation. And the online estimation of mass is realized based on recursive least square method. Meanwhile, the road slope estimation algorithm based on interactive multiple model has been designed, which achieves the weighted fusion of Kalman filter observer based on kinematics and unscented Kalman filter observer based on dynamics. Finally, the road tests were carried out on representative working conditions. The maximum error between the actual speed and the reference speed does not exceed 0.68m/s. The relative error of mass estimation is not more than 1.95%, and the absolute error of slope estimation is less than 1.84%, which proves that the proposed estimation algorithm has high comprehensive performance. More importantly, it is not limited to specific working conditions, which means a great significance for the development of intelligent vehicles.

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Section: Principle Of Reference Speed Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle State Estimation Based on Multidimensional Information Fusion

et al. 2022

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“…Xie et al [14] proposed a cloud scheduling algorithm driven by dynamic basic paths, which computes the dynamic essential path of the prescheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. References [15][16][17] describe how to implement intrusion prevention and optimization on the cloud.…”

Section: Related Workmentioning

confidence: 99%

Efficient Delay-Aware Task Scheduling for IoT Devices in Mobile Cloud Computing

Jin

Han

et al. 2022

Mobile Information Systems

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The development of Internet of Things (IoT) technology depends on technologies such as high-efficiency storage and high computing power. Mobile cloud computing (MCC) technology will be an important foundation for the development of IoT. The efficient scheduling of tasks in IoT devices in MCC environment is challenging. The requirements for task scheduling in MCC are becoming more and more complex. As the core problem in MCC, task scheduling aims to allocate tasks reasonably, achieve optimal scheduling strategies, and complete tasks effectively. In this paper, efficient delay-aware task scheduling algorithm (EDTSA) is proposed, with the optimization goal of minimizing task running time. The matching of tasks and virtual machines is modeled as a bipartite graph. The problem is divided into multiple subproblems to solve the optimal solution separately. The combined solution is used as the initial solution of the local search algorithm. The efficiency of the local search depends on the quality and nature of the initial solution. We can generate multiple initial solutions according to different division criteria. The initial solution is the combination of the optimal solutions of the subproblems, so the quality of the initial solution has been greatly improved and generating multiple initial solutions according to the division can reduce the probability of falling into the local optimal solution. This algorithm also divides the neighborhood to reduce unnecessary searches. Finally, we verify the efficiency and practicability of the algorithm through experiments.

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“…To enable users to access service nodes in a timely manner and quickly meet user requirements, service content is distributed to appropriate access sites. In general, the access site close to the user is preferred to be processed in time by reducing transmission distance, and tasks are going to be executed in multiaccess edge computing (MEC) servers [4]. MEC refers to process-ing, analyzing, and storing data closer to where it is generated to enable rapid, near real-time analysis and response, which can alleviate the backhaul link pressure with the traditional network architecture.…”

Section: Introductionmentioning

confidence: 99%

Cost-Aware Placement Optimization of Edge Servers for IoT Services in Wireless Metropolitan Area Networks

Shao

Shen

Gong

et al. 2022

Wireless Communications and Mobile Computing

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Edge computing migrates cloud computing capacity to the edge of the network to reduce latency caused by congestion and long propagation distance of the core network. And the Internet of things (IoT) service requests with large data traffic submitted by users need to be processed quickly by corresponding edge servers. The closer the edge computing resources are to the user network access point, the better the user experience can be improved. On the other hand, the closer the edge server is to users, the fewer users will access simultaneously, and the utilization efficiency of nodes will be reduced. The capital investment cost is limited for edge resource providers, so the deployment of edge servers needs to consider the trade-off between user experience and capital investment cost. In our study, for edge server deployment problems, we summarize three critical issues: edge location, user association, and capacity at edge locations through the research and analysis of edge resource allocation in a real edge computing environment. For these issues, this study considers the user distribution density (load density), determines a reasonable deployment location of edge servers, and deploys an appropriate number of edge computing nodes in this location to improve resource utilization and minimize the deployment cost of edge servers. Based on the objective minimization function of construction cost and total access delay cost, we formulate the edge server placement as a mixed-integer nonlinear programming problem (MINP) and then propose an edge server deployment optimization algorithm to seek the optimal solution (named Benders_SD). Extensive simulations and comparisons with the other three existing deployment methods show that our proposed method achieved an intended performance. It not only meets the low latency requirements of users but also reduces the deployment cost.

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Multi-Objective Optimization for Resource Allocation in Vehicular Cloud Computing Networks

Cited by 77 publications

References 34 publications

Vehicle State Estimation Based on Multidimensional Information Fusion

Vehicle State Estimation Based on Multidimensional Information Fusion

Efficient Delay-Aware Task Scheduling for IoT Devices in Mobile Cloud Computing

Cost-Aware Placement Optimization of Edge Servers for IoT Services in Wireless Metropolitan Area Networks

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