ArticleSensors (Basel, Switzerland)2020
Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Genetic Algorithm-Based Cooperative Coding and Caching Data Dissemination Scheme in Multi-UAV-Enabled Internet of Vehicles.Sensors (Basel, Switzerland) · 2024Article
- Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks.Sensors (Basel, Switzerland) · 2023Article
- UAV-Aided Dual-User Wireless Power Transfer: 3D Trajectory Design and Energy Optimization.Sensors (Basel, Switzerland) · 2023Article
- Secrecy Energy Efficiency Enhancement in UAV-Assisted MEC System.Sensors (Basel, Switzerland) · 2023Article
- Joint Resource Management and Trajectory Optimization for UAV-Enabled Maritime Network.Sensors (Basel, Switzerland) · 2022Article
- Quality-of-Service-Centric Design and Analysis of Unmanned Aerial Vehicles.Sensors (Basel, Switzerland) · 2022Article
- Continuous Hidden Markov Model Based Spectrum Sensing with Estimated SNR for Cognitive UAV Networks.Sensors (Basel, Switzerland) · 2022Article
- A Dynamic Task Allocation Algorithm for Heterogeneous UUV Swarms.Sensors (Basel, Switzerland) · 2022Article
- Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone.Sensors (Basel, Switzerland) · 2022Article
- Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs.Sensors (Basel, Switzerland) · 2022Article
- Wireless Local Area Network Technologies as Communication Solutions for Unmanned Surface Vehicles.Sensors (Basel, Switzerland) · 2022Article
- Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users.Sensors (Basel, Switzerland) · 2021Article
- Evaluating the Quality of Experience Performance Metric for UAV-Based Networks.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Aerial ad-hoc networks have the potential to enable smart services while maintaining communication between the ground system and unmanned aerial vehicles (UAV). Previous research has focused on enabling aerial data-centric smart services while integrating the benefits of aerial objects such as UAVs in hostile and non-hostile environments. Quality of service (QoS) provisioning in UAV-assisted communication is a challenging research theme in aerial ad-hoc networks environments. Literature on aerial ad hoc networks lacks cooperative service-oriented modeling for distributed network environments, relying on costly static base station-oriented centralized network environments. Towards this end, this paper proposes a quality of service provisioning framework for a UAV-assisted aerial ad hoc network environment (QSPU) focusing on reliable aerial communication. The UAV's aerial mobility and service parameters are modelled considering highly dynamic aerial ad-hoc environments. UAV-centric mobility models are utilized to develop a complete aerial routing framework. A comparative performance evaluation demonstrates the benefits of the proposed aerial communication framework. It is evident that QSPU outperforms the state-of-the-art techniques in terms of a number of service-oriented performance metrics in a UAV-assisted aerial ad-hoc network environment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.