English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

It is a challenging problem to characterize the air-to-ground (A2G) channeland identify the best deployment location for 3D UAVs with the QoS awareness.To address this problem, we propose a QoS-aware UAV 3D coverage deploymentalgorithm, which simulates the three-dimensional urban road scenario, considersthe UAV communication resource capacity and vehicle communication QoSrequirements comprehensively, and then obtains the optimal UAV deploymentposition by improving the genetic algorithm. Specifically, the K-meansclustering algorithm is used to cluster the vehicles, and the center locationsof these clusters serve as the initial UAV positions to generate the initialpopulation. Subsequently, we employ the K-means initialized grey wolfoptimization (KIGWO) algorithm to achieve the UAV location with an optimalfitness value by performing an optimal search within the grey wolf population.To enhance the algorithm's diversity and global search capability, we randomlysubstitute this optimal location with one of the individual locations from theinitial population. The fitness value is determined by the total number ofvehicles covered by UAVs in the system, while the allocation scheme'sfeasibility is evaluated based on the corresponding QoS requirements.Competitive selection operations are conducted to retain individuals withhigher fitness values, while crossover and mutation operations are employed tomaintain the diversity of solutions. Finally, the individual with the highestfitness, which represents the UAV deployment position that covers the maximumnumber of vehicles in the entire system, is selected as the optimal solution.Extensive experimental results demonstrate that the proposed algorithm caneffectively enhance the reliability and vehicle communication QoS.

QoS-Aware 3D Coverage Deployment of UAVs for Internet of Vehicles in Intelligent Transportation