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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistedInternet-of-Things (IoT) system in a sophisticated three-dimensional (3D)environment, where the UAV's trajectory is optimized to efficiently collectdata from multiple IoT ground nodes. Unlike existing approaches focusing onlyon a simplified two-dimensional scenario and the availability of perfectchannel state information (CSI), this paper considers a practical 3D urbanenvironment with imperfect CSI, where the UAV's trajectory is designed tominimize data collection completion time subject to practical throughput andflight movement constraints. Specifically, inspired from the state-of-the-artdeep reinforcement learning approaches, we leverage the twin-delayed deepdeterministic policy gradient (TD3) to design the UAV's trajectory and presenta TD3-based trajectory design for completion time minimization (TD3-TDCTM)algorithm. In particular, we set an additional information, i.e., the mergedpheromone, to represent the state information of UAV and environment as areference of reward which facilitates the algorithm design. By taking theservice statuses of IoT nodes, the UAV's position, and the merged pheromone asinput, the proposed algorithm can continuously and adaptively learn how toadjust the UAV's movement strategy. By interacting with the externalenvironment in the corresponding Markov decision process, the proposedalgorithm can achieve a near-optimal navigation strategy. Our simulationresults show the superiority of the proposed TD3-TDCTM algorithm over threeconventional non-learning based baseline methods.

Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach