Joint UAV Trajectory Planning, DAG Task Scheduling, and Service Function Deployment Based on DRL in UAV-Empowered Edge Computing

Wei, Xianglin; Cai, Lingfeng; Wei, Nan; Zou, Peng; Zhang, Jin; Subramaniam, Suresh

doi:10.1109/jiot.2023.3257291

Cited by 13 publications

(4 citation statements)

References 33 publications

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“…To reduce the training time, the simulation scene in this paper is set at 50 m × 50 m × 10 m, due to the complexity of the algorithm's action and state space [31][32][33][34]. The algorithm presented in this paper applies to scenes of arbitrary size, and the simulation parameters are shown in Table 1.…”

Section: Parameter Initializationmentioning

confidence: 99%

Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach

Wang,

Qi,

Jiang

et al. 2024

Electronics

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Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV’s 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm.

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Section: Parameter Initializationmentioning

confidence: 99%

Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach

Wang,

Qi,

Jiang

et al. 2024

Electronics

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“…In [12,13], radio power transmission from UAVs to end devices was proposed to improve battery life or to enable local computing. In [14], a deep reinforcement learning-based algorithm was proposed to solve the UAV trajectory planning, mission scheduling, and deployment in complex regional scenarios.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Multi-Hop Flying Mesh Network Using Directional Antenna Based on β-GPP

Qin

Peng

et al. 2023

Drones

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Maintaining high system performance is critical for a multi-hop flying mesh network (FlyMesh) to perform missions in different environments. Although the Poisson point process (PPP) has been widely used for the performance analysis of FlyMesh, it still has flaws in describing the spatial distribution of the UAVs since it does not restrict the minimum distance between them. The spatial deployment of FlyMesh varies depending on the environment. Considering the relevance and practicality, we modeled the multi-hop FlyMesh using the β-Ginibre point process (β-GPP) and equipped each UAV with a directional antenna. Under the condition of the decode-and-forward protocol, we derived the connection probability and ergodic capacity of a multi-hop FlyMesh utilizing the Laplace transform of interference. Then, we calculated an approximate expression for the interference Laplace transform based on the diagonal approximation and further obtained the coverage probability. Finally, the numerical simulation results verified the correctness of the theoretical derivation, indicating that it is possible to optimize the system’s performance based on the expressions derived in this paper.

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“…When performing task offloading, there are different types of tasks, and edge servers with limited resources that can only deploy a subset of all the SFs [10,11]. For the SF deployment problem, Li et al [12] designed a new genetic-algorithm-based SF deployment algorithm, and designed a new edge-computing-service-deployment management platform.…”

Section: Related Workmentioning

confidence: 99%

“…It can be seen that this paper is the first to combine DAG tasks with SF and the UAV position, and optimize the task scheduling plan jointly. Task Scheduling [5,6] Minimization of energy consumption [7,8] Minimization of energy consumption and service latency [9] Minimization of energy consumption [11] Minimization of response time [12] Minimization of response time [13] Task completion time [14] Minimization of energy consumption…”

Section: Related Workmentioning

confidence: 99%

Joint Trajectory Planning, Service Function Deploying, and DAG Task Scheduling in UAV-Empowered Edge Computing

Jia

Zhao

Wei

et al. 2023

Drones

Self Cite

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Efficient task scheduling plays a key role in unmanned aerial vehicle (UAV)-empowered edge computing due to the limitation in energy supply and computation resource on the UAV platforms. This problem becomes much more complicated when the processing-dependent tasks that can be described as directed acyclic graphs (DAGs) and each of their components can only be processed on a virtual machine or container that deploys the desired service function (SF). In this paper, we first build an optimization problem that aims to minimize the completion time of all DAG tasks subject to constraints including task dependency, computation resource occupied by the UAVs, etc. To tackle this problem, a genetic algorithm-based joint deployment and scheduling algorithm, named GA-JoDeS, is put forward, since solving the established 0–1 integer programming problem in polynomial time is infeasible. Subtask offloading decision and UAV position are encoded into the chromosome in the GA-JoDeS algorithm, and the fitness value of an individual is decided by the maximum completion time of all DAG tasks. Through selection, crossover, and mutation, the GA-JoDeS algorithm evolves until it determines the individual with the optimal fitness value as the suboptimal solution to the problem. To evaluate the performance of the proposal, a series of simulations is conducted, and three traditional methods are chosen as comparison benchmarks. The results show that the GA-JoDeS algorithm can convergence quickly, and it can effectively reduce the completion time of DAG tasks with different parameter settings.

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Joint UAV Trajectory Planning, DAG Task Scheduling, and Service Function Deployment Based on DRL in UAV-Empowered Edge Computing

Cited by 13 publications

References 33 publications

Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach

Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach

Performance Analysis of Multi-Hop Flying Mesh Network Using Directional Antenna Based on β-GPP

Joint Trajectory Planning, Service Function Deploying, and DAG Task Scheduling in UAV-Empowered Edge Computing

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