Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach

Wang, Yunpeng; Fang, Weiwei; Ding, Yi; Xiong, Naixue

doi:10.1007/s11276-021-02632-z

Cited by 101 publications

(37 citation statements)

References 22 publications

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“…Since UAV-MEC provides computing services to all vehicles in a TDMA mode [26], we discretize the finite flying time T into N equal time slots, that is, T N t = , where δt is the time interval that allows UAV-MEC to be in a certain place at a certain time slot. In the 3D Cartesian coordinate system, the UAV continues to fly at a fixed altitude H, where the UAV has a start coordinate [26] [27]:…”

Section: A Communication Modelmentioning

confidence: 99%

“…where B denotes the communication bandwidth, vp p denotes the transmit power of the vehicle in the upload link, 2  denotes the noise power, NLOS p denotes the transmission loss, and () m pi signifies the indicator of whether there is any block between UAV and vehicle m in time slot i (that is, 0 means no blockage and 1 means there is a blocking object) [26].…”

Section: A Communication Modelmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

Baktayan

Al-Baltah

Ghani

2022

JCOMSS

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In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delaysensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26% and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy.

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Section: A Communication Modelmentioning

confidence: 99%

Section: A Communication Modelmentioning

confidence: 99%

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

Baktayan

Al-Baltah

Ghani

2022

JCOMSS

View full text Add to dashboard Cite

show abstract

“…P f ollow ij is defined as the transition probability of node i to j under following behavior. An individual who performs following behavior selects the next node according to Equation (3).…”

Section: Following Behaviormentioning

confidence: 99%

“…Recently, the impact of various emergencies on public safety is increasing, which poses a serious threat to the environment required for citizens to engage in various activities. The simulation of pedestrian emergency evacuation involves multiple disciplines, and its research findings can be applied to various fields, such as building, science engineering, safety science, disaster prevention, highway and railway transportation, robot control, and urban planning [1][2][3]. Evacuation dynamics simulaed through computers is one of key research directions in evacuation study, which has important theoretical and practical value.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

Zong

Wang

et al. 2022

Electronics

Self Cite

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A two-layer artificial fish swarm evacuation model based on heterogeneous pheromones is presented in this paper. Firstly, the movements of evacuees are simulated by the behaviors of an artificial fish swarm, including preying, swarming, and following. Then, the positive feedback mechanism of heterogeneous pheromones is introduced to improve evacuation performance. Based on the interaction and communication mechanisms of biological groups of social networks in nature, the perceptual and cooperative model among individuals and between individuals and the environment is established. An optimization scheme based on fish swarms and heterogeneous pheromones is proposed. The simulation and experimental results show that the two-layer evacuation model can optimize the spatial-temporal distribution of people and can finally achieve better evacuation plans. The proposed model and algorithm can provide effective guidance for emergency safety responses and robot cooperative control in intelligent robot systems.

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“…UAV edge computing platform supplies fast and flexible information support for mobile services. Researchers pay more attention that Multi-UAVs could cover a wider testing scale in recent years [15]- [17].…”

Section: Related Workmentioning

confidence: 99%

Distributed Location-Aware Task Offloading in Multi-UAVs Enabled Edge Computing

Liu

et al. 2022

IEEE Access

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Edge computing is a new computing paradigm which distributes tasks to edge networks for processing, it provides effective support for various mobile applications to meet their rapid response requirement. Unmanned Aerial Vehicle (UAV) has been widely used in emergency rescue, mapping, etc., with the advantages of flexible deployment and rapid movement. However, on one hand, mobile applications will be terminated when the battery energy is exhausted. On the other hand, mobile applications will be out of service when mobile devices are out of radio coverage of UAVs. How to achieve low-cost task unloading in the resource limited and location sensitive multi-UAVs edge computing environment is rather challenging. In this paper, we propose a distributed location-aware task offloading scheme, aiming at the above issues. Specifically, we create a nonlinear task allocation problem by combining the limited energy constraints of edge nodes with the random movement of users, where the cost function is divided into static and dynamic costs, respectively. Then, we formulate this problem to a convex optimization one with linear constrains, based on regularization technology. The mathematical proof shows that the scheme can support a parameterized competitive ratio without requiring any prior knowledge of the input task. The simulation results show that the proposed scheme can achieve lower cost edge computing services.INDEX TERMS convex programming,edge computing,task allocation,UAV

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Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach

Cited by 101 publications

References 22 publications

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

Distributed Location-Aware Task Offloading in Multi-UAVs Enabled Edge Computing

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