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

Swarms of collaborating Unmanned Aerial Vehicles (UAVs) that utilize ad-hocnetworking technologies for coordinating their actions offer the potential tocatalyze emerging research fields such as autonomous exploration of disasterareas, demanddriven network provisioning, and near field packet delivery inIntelligent Transportation Systems (ITSs). As these mobile robotic networks arecharacterized by high grades of relative mobility, existing routing protocolsoften fail to adopt their decision making to the implied network topologydynamics. For addressing these challenges, we present Predictive Ad-hoc Routingfueled by Reinforcement learning and Trajectory knowledge (PARRoT) as a novelmachine learning-enabled routing protocol which exploits mobility controlinformation for integrating knowledge about the future motion of the mobileagents into the routing process. The performance of the proposed routingapproach is evaluated using comprehensive network simulation. In comparison toestablished routing protocols, PARRoT achieves a massively higher robustnessand a significantly lower end-to-end latency.

PARRoT: Predictive Ad-hoc Routing Fueled by Reinforcement Learning and Trajectory Knowledge