Analysis on the Driving and Braking Control Logic Algorithm for Mobility Energy Efficiency in Electric Vehicle

Jamil, Harun; Naqvi, Syed Shehryar Ali; Iqbal, Naeem; Khan, Murad Ali; Qayyum, Faiza; Muhammad, Faseeh; Khan, Salabat; Kim, Do-Hyeun

doi:10.1007/s40866-023-00190-1

Cited by 1 publication

(1 citation statement)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be used for autonomous driving, smart city, and digital twin construction in the future. Additionally, more effective offloading strategies deserve to be formulated by combining task execution and energy consumption because energy saving is essential [38][39][40]…”

Section: Discussionmentioning

confidence: 99%

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network

Liu,

Zheng,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24–29% compared with the existing state-of-the-art techniques.

show abstract

Section: Discussionmentioning

confidence: 99%