The development of the Internet of Vehicles (IoV) has made transportation systems into intelligent networks. However, with the increase in vehicles, an increasing number of data need to be analyzed and processed. Roadside units (RSUs) can offload the data collected from vehicles to remote cloud servers for processing, but they cause significant network latency and are unfriendly to applications that require real-time information. Edge computing (EC) brings low service latency to users. There are many studies on computing offloading strategies for vehicles or other mobile devices to edge servers (ESs), and the deployment of ESs cannot be ignored. In this paper, the placement problem of ESs in the IoV is studied, and the six-objective ES deployment optimization model is constructed by simultaneously considering transmission delay, workload balancing, energy consumption, deployment costs, network reliability, and ES quantity. In addition, the deployment problem of ESs is optimized by a many-objective evolutionary algorithm. By comparing with the state-of-the-art methods, the effectiveness of the algorithm and model is verified.
Large-Scale Many-Objective Deployment Optimization of Edge Servers
IEEE Transactions on Intelligent Transportation Systems ; 22 , 6 ; 3841-3849
01.06.2021
2483054 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Many-objective optimization based on sub-objective evolutionary algorithm
British Library Online Contents | 2015
|An Introduction to Many-Objective Evolutionary Optimization
Springer Verlag | 2020
|Multi-objective Bi-directional V2G Behavior Optimization and Strategy Deployment
Springer Verlag | 2022
|Many-objective optimization scheduling method of arrival flights
British Library Conference Proceedings | 2022
|