Joint Task Offloading Based on Distributed Deep Reinforcement Learning-Based Genetic Optimization Algorithm for Internet of Vehicles

Jin, Hulin; Kim, Yong-Guk; Jin, Zhiran; Fan, Chunyang; Xu, Yonglong

doi:10.1007/s10723-024-09741-x

Cited by 1 publication

(1 citation statement)

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“…Ref. [20] optimizes the task offloading problem by a distributed deep reinforcement learning (DDRL)-based genetic optimization algorithm (GOA). In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to solve the problem of task offloading and multi-resource management [21].…”

Section: Introductionmentioning

confidence: 99%

Cloud–Fog Collaborative Computing Based Task Offloading Strategy in Internet of Vehicles

Zhu,

Liu,

Zhu

et al. 2024

Electronics

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Vehicle terminals in the mobile internet of vehicles are faced with difficulty in the requirements for computation-intensive and delay-sensitive tasks, and vehicle mobility also causes dynamic changes in vehicle-to-vehicle (V2V) communication links, which results in a lower task offloading quality. To solve the above problems, a new task offloading strategy based on cloud–fog collaborative computing is proposed. Firstly, the V2V-assisted task forwarding mechanism is introduced under cloud–fog collaborative computing, and a forwarding vehicles predicting algorithm based on environmental information is designed; then, considering the parallel computing relationship of tasks in each computing node, a task offloading cost model is constructed with the goal of minimizing delay and energy consumption; finally, a multi-strategy improved genetic algorithm (MSI-GA) is proposed to solve the above task offloading optimization problem, which adapts the chaotic sequence to initialize the population, comprehensively considers the influence factors to optimize the adaptive operator, and introduces Gaussian perturbation to enhance the local optimization ability of the algorithm. The simulation experiments show that compared with the existing strategies, the proposed task offloading strategy has the lower task offloading cost for a different number of tasks and fog nodes; additionally, the introduced V2V auxiliary task forwarding mechanism can reduce the forwarding load of fog nodes by cooperative vehicles to forward tasks.

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Section: Introductionmentioning

confidence: 99%