Deep reinforcement learning-based computation offloading and resource allocation in security-aware mobile edge computing

Ke, Hongchang; Wang, Hui; Zhao, Hongwei; Sun, Weijia

doi:10.1007/s11276-021-02643-w

Cited by 28 publications

(11 citation statements)

References 39 publications

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“…UE associated with the same SBS allocates orthogonal spectrum, so there is no intra cell interference between UE which is associated with the same SBS. [ 19 – 21 ]. The uplink transmission rate is shown in the following formula:

where B nk represents the channel bandwidth allocated by SBS k to UE n , P n represents the transmission power of UE n , h nk is the channel gain between SBS k and UE n , and N 0 is the power spectral density.…”

Section: Methodsmentioning

confidence: 99%

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task

Luo

2022

Computational Intelligence and Neuroscience

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This paper analyzes the application of MEC multiserver heuristic joint task in resource allocation of the educational resource database. After constructing the scenario of educational resource database, a mathematical model is constructed from the dimensions of local execution strategy, unloading execution, and given educational resource allocation, in order to optimize the optimal allocation of educational resources through MEC. The results show that the DOOA scheme has good performance in terms of calculation cost and timeout rate. Compared with other benchmark schemes, the DQN-based unloading scheme has better performance, can effectively balance the load, and is better than the random unloading scheme and the SNR-based unloading scheme in terms of delay and calculation cost. The results show that the total hits of all category 1 users' content requests account for the proportion of the total content requests. The images have a small downward trend at the 15000 and 30000 time slots and then continue to rise. This shows that the proposed scheme can automatically adjust the caching strategy to adapt to the changes of content popularity, which proves that the agent can correctly perceive the changing trend of content popularity when the popularity of network content is unknown and improve the caching strategy accordingly to improve the cache hit rate. Therefore, the allocation of educational resources based on the MEC multiserver heuristic joint task is more reasonable and can achieve the optimal solution.

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Section: Methodsmentioning

confidence: 99%

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task

Luo

2022

Computational Intelligence and Neuroscience

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“…Optimization objectives [37] Multidimensional optimization of offloading strategy formulation, load balancing and computing resource allocation A low complexity heuristic algorithm is proposed obtain the offloading and ensure the load balance among multiple MECs. Lagrange dual decomposition method is used to solve the computational resource allocation sub-problem Minimize the weighted sum of total delay and energy consumption of all devices in MEC network [38] Base station user matching and power allocation A cache-based NOMA downlink heterogeneous network architecture is proposed, and the messaging user coordination strategy is formulated to reasonably allocates Improve network cache hit rate and minimize energy consumption [39] Calculate privacy disclosure during uninstall A wireless communication and computing model of partial computing offload and resource allocation considering time-varying channel state, bandwidth constraints, random arrival of workload and privacy protection is established, and a scheme of local computing offload and resource allocation based on distributed reinforcement learning is proposed Minimize the calculation and offloading delay and energy consumption, while protecting users' privacy [40] Task calculation offloading problem A new MEC edge computing offload system based on D2D communication is proposed, and the problem of computing offload is solved by game theory…”

Section: Author Contributionsmentioning

confidence: 99%

Review on Offloading of Vehicle Edge Computing

Wang

Jiang

et al. 2022

JAIT

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Vehicle edge computing (VEC) is a new technology that can extend computing and storage functions to the edge of the Internet of Things (IoT) systems. For limited computing power and delay sensitive mobile applications on the Internet of Vehicles (IOV). It is important to offload computing tasks to the end of the VEC network. Still, High mobility data security and privacy resource management and the randomness of IOV brought about new problems to the offloading of VEC. To this end, this study focuses on the offloading of computing tasks in VEC. We survey principal offloading schemes and methods in the VEC field and classify the current offloading of computing tasks into different categories. We also discuss the prospect of VEC. This survey could give a reference for researchers to find and understand the important characteristics of VEC, which helps choose the optimal solutions for the offloading of computing tasks in VEC.

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“…Ke et al put forward that in order to prevent the degradation of individual artificial fish in swimming, the behavior of artificial fish should be improved as follows. When the best artificial fish in the population performs foraging, chasing after the tail, and swarming, if the target position is not as good as its own position, the artificial fish will not move [ 17 ]. The research of Llerena and Gondim shows that D2D users use resources orthogonal to Cu users to communicate on D2D direct link.…”

Section: Related Workmentioning

confidence: 99%

“…When the best artificial fish in the population performs foraging, chasing after the tail, and swarming, if the target position is not as good as its own position, the artificial fish will not move [17]. e research of Llerena and Gondim shows that D2D users use resources orthogonal to Cu users to communicate on D2D direct link.…”

Section: Related Workmentioning

confidence: 99%

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Fang

Zhang

2022

Computational Intelligence and Neuroscience

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The construction and operation of China’s rail transit system have entered a high-speed development stage, and the rapid increase of train speed and mileage has brought greater challenges to the safety and reliability of the rail transit system. Network planning evaluation is the key to the early decision-making of urban rail transit project, which directly determines the success or failure of the whole project. How to scientifically and reasonably evaluate the urban rail transit information resource network planning has become a difficult problem for many urban planners to solve. Therefore, this paper studies the optimization of the communication resource allocation algorithm and the comprehensive evaluation of its application for urban rail transit planning. In this paper, based on CVNN structure, the network prototype is an extension of RVNN structure. In the abstract, its processing unit is composed of a pair of real-number processors that can realize certain operations. HNN is a fully connected recurrent neural network based on the idea of the energy function, which is helpful to understand the calculation mode of HNN, and the research shows that HNN can solve many combinatorial optimization problems. In addition, the combination of neural network and genetic algorithm with simulated annealing mechanism can also bring new directions for research. On the basis of experimental analysis, it can be concluded that in general, the error reduction rate of the optimization scheme designed in this paper can reach 58.6% on average. In practical application, the accuracy of the optimal bit error rate is 52.4%.

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Deep reinforcement learning-based computation offloading and resource allocation in security-aware mobile edge computing

Cited by 28 publications

References 39 publications

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task

Review on Offloading of Vehicle Edge Computing

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

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