Optimal access mode selection and resource allocation for cellular‐VANET heterogeneous networks

Qi, Yanjia; Wang, Hongyu; Zhang, Lingyan; Wang, Bing

doi:10.1049/iet-com.2017.0408

Cited by 23 publications

(11 citation statements)

References 21 publications

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“…Unless otherwise specified, referring to [41], [47], [57], the system parameters of FCSD are set as follows: We assume that the fog nodes are 10. i.e., N = 10.…”

Section: A Parameter Settingsmentioning

confidence: 99%

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

et al. 2020

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Swarm of drones, as an intensely significant category of swarm robots, is widely used in various fields, e.g., search and rescue, detection missions, military, etc. Because of the limitation of computing resource of drones, dealing with computation-intensive tasks locally is difficult. Hence, the cloud-based computation offloading is widely adopted, nevertheless, for some latency-sensitive tasks, e.g., object recognition, path planning, etc., the cloud-based manner is inappropriate due to the excessive delay. Even in some harsh environments, e.g., disaster area, battlefield, etc., there is no wireless infrastructure existed to combine the drones and cloud center. Thus, to solve the problem encountered by cloud-based computation offloading, in this paper, Fog Computing aided Swarm of Drones (FCSD) architecture is proposed. Considering the uncertainty factors in harsh environments which may threaten the success of FCSD processing tasks, not only the latency model, but also the reliability model of FCSD is constructed to guarantee the high reliability of task completion. Moreover, in view of the limited battery life of the drone, we formulated the problem as the task allocation problem which minimized the energy consumption of FCSD under the constraints of latency and reliability. Furthermore, to speed up the process of the optimization problem solving to improve the practicality, relying on the recent advances in distributed convex optimization, we develop a fast Proximal Jacobi Alternating Direction Method of Multipliers (ADMM) based distributed algorithm. Finally, simulation results validate the effectiveness of our proposed scheme. INDEX TERMS Swarm of drones, distributed fog computing, latency, reliability, energy consumption.

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“…Unless otherwise specified, referring to [41], [47], [57], the system parameters of FCSD are set as follows: We assume that the fog nodes are 10. i.e., N = 10.…”

Section: A Parameter Settingsmentioning

confidence: 99%

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

et al. 2020

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“…With rapid development of wireless communication technology, users' demands for communication businesses have shifted to diversity, personalization, and broad bands, which require the integration and collaboration of many wireless networks in providing users high-quality personalized services at any time and place [1,2]. Increasing the utilization of network resources and providing users the best network access selection and the best service quality in heterogeneous wireless network environment have thus become important research topics [3,4].…”

Section: Introductionmentioning

confidence: 99%

Network Access Selection Algorithm Based on Balanced Profits between Users and Network

Chen

Juanping

Yuan

et al. 2019

Wireless Communications and Mobile Computing

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A network access selection algorithm based on the intuitionistic fuzzy analytic hierarchy process (AHP) and bilateral profit drive is proposed in this study for addressing problems regarding user–network bilateral profits. User preference, business demands, and network parameter changes are comprehensively considered in the algorithm. First, the initial weights centered at users are gained by intuitionistic fuzzy AHP. Second, the network participates in network access selection as a subject with competitive consciousness, and the entire selection process is transformed into a multiobjective optimization problem by the construction of a competitive model, thereby obtaining dynamic competitive weights. Third, the initial weights centered at users and the dynamic competitive weights are combined to obtain comprehensive weights. In this way, the dynamic adjustment of comprehensive weights is realized. Finally, candidate networks are ordered according to a comprehensive performance evaluation, and the optimal one is selected. The proposed algorithm is validated by simulation results to be valid in reducing the blocking rate of networks and optimizing network resource allocation. Therefore, it not only protects user–network bilateral profits but also maximizes comprehensive profits.

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“…Resource allocation in resource-limited vehicular networks with a selected set of served services is an important topic and has been well studied [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. The optimization objective in References [ 10 , 11 , 12 , 13 ] was the maximization of the network capacity.…”

Section: Introductionmentioning

confidence: 99%

“…The scheme in Reference [ 15 ] selected the optimal receiver to establish device-to-device links and assigned the proper channels by solving a maximum weighted independent set problem. The network utility was optimized in References [ 16 , 17 , 18 ]. The utility function in Reference [ 16 ] considered several key factors related to video quality, including the location, velocity, intensity, and smoothness requirements.…”

Section: Introductionmentioning

confidence: 99%

“…The cloud resource allocation proposed in Reference [ 17 ] was modeled as a non-cooperative cloud resource allocation game based on the Gauss–Seidel iteration method, in which the utility reflects the performance of the user including the rate and delay. In Reference [ 18 ], the access mode selection and resource allocation scheme were formulated as a user aggregate utility maximization problem, taking into consideration the transmission delay, various network topologies, and power reduction. In Reference [ 19 ], the users’ secrecy rate was optimized by a joint power and subcarrier allocation based on a maximum-minimum fairness criterion.…”

Section: Introductionmentioning

confidence: 99%

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A Low-Cost Resource Re-Allocation Scheme for Increasing the Number of Guaranteed Services in Resource-Limited Vehicular Networks

Meng

Yuan

et al. 2018

Sensors

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Vehicular networks are becoming increasingly dense due to expanding wireless services and platooning has been regarded as a promising technology to improve road capacity and on-road safety. Constrained by limited resources, not all communication links in platoons can be allocated to the resources without suffering interference. To guarantee the quality of service, it is required to determine the set of served services at which the scale of demand exceeds the capability of the network. To increase the number of guaranteed services, the resource allocation has to be adjusted to adapt to the dynamic environment of the vehicular network. However, resource re-allocation results in additional costs, including signal overhead and latency. To increase the number of guaranteed services at a low-cost in a resource-limited vehicular network, we propose a time dynamic optimization method that constrains the network re-allocation rate. To decrease the computational complexity, the time dynamic optimization problem is converted into a deterministic optimization problem using the Lyapunov optimization theory. The simulation indicates that the analytical results do approximate the reality, and that the proposed scheme results in a higher number of guaranteed services as compared to the results of a similar algorithm.

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Optimal access mode selection and resource allocation for cellular‐VANET heterogeneous networks

Cited by 23 publications

References 21 publications

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

Network Access Selection Algorithm Based on Balanced Profits between Users and Network

A Low-Cost Resource Re-Allocation Scheme for Increasing the Number of Guaranteed Services in Resource-Limited Vehicular Networks

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