# Reliable and Efficient Data Collection in UAV-based IoT Networks

arXiv: https://arxiv.org/abs/2311.05303 · HTML: https://mlsanity.com/paper/2311.05303/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 300 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 30 of 41)

- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks](https://mlsanity.com/paper/1810.10408/) (2018)
- [Mobile Edge Computing: A Survey on Architecture and Computation Offloading](https://mlsanity.com/paper/1702.05309/) (2017)
- [Reinforcement Learning for UAV Attitude Control](https://mlsanity.com/paper/1804.04154/) (2018)
- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledge](https://mlsanity.com/paper/1811.03307/) (2018)
- [UAV Communications for 5G and Beyond: Recent Advances and Future Trends](https://mlsanity.com/paper/1901.06637/) (2019)
- [Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale](https://mlsanity.com/paper/1801.01270/) (2018)
- [Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges](https://mlsanity.com/paper/1809.01752/) (2018)
- [Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach](https://mlsanity.com/paper/1905.04152/) (2019)
- [A Survey of Channel Modeling for UAV Communications](https://mlsanity.com/paper/1801.07359/) (2018)
- [Recurrent Neural Networks: An Embedded Computing Perspective](https://mlsanity.com/paper/1908.07062/) (2019)
- [UAV Assisted Data Collection for Internet of Things: A Survey](https://mlsanity.com/paper/2211.09555/) (2022)
- [3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning](https://mlsanity.com/paper/2106.03129/) (2021)
- [A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook](https://mlsanity.com/paper/2104.01283/) (2021)
- [The Sky Is Not the Limit: LTE for Unmanned Aerial Vehicles](https://mlsanity.com/paper/1707.07534/) (2017)
- [Autonomous UAV Navigation Using Reinforcement Learning](https://mlsanity.com/paper/1801.05086/) (2018)
- [Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems](https://mlsanity.com/paper/1907.07565/) (2019)
- [UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer](https://mlsanity.com/paper/2401.02425/) (2023)
- [Multi-tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects](https://mlsanity.com/paper/1711.08407/) (2017)
- [Fundamentals of Wireless Information and Power Transfer: From RF Energy Harvester Models to Signal and System Designs](https://mlsanity.com/paper/1803.07123/) (2018)
- [Mobile Edge Computing in Unmanned Aerial Vehicle Networks](https://mlsanity.com/paper/1910.10523/) (2019)
- [Robust Secure UAV Communications with the Aid of Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2008.09404/) (2020)
- [Sim-to-Real Deep Reinforcement Learning based Obstacle Avoidance for UAVs under Measurement Uncertainty](https://mlsanity.com/paper/2303.07243/) (2023)
- [Wireless Powered Communication Networks: An Overview](https://mlsanity.com/paper/1508.06366/) (2015)
- [Optimizing Communication and Computation for Multi-UAV Information Gathering Applications](https://mlsanity.com/paper/1610.04091/) (2016)
- [Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT](https://mlsanity.com/paper/1910.08296/) (2019)
- [Energy-Constrained UAV Data Collection Systems: NOMA and OMA](https://mlsanity.com/paper/1910.13630/) (2019)

## Built on by (most cited first, 3 of 3)

- [Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness](https://mlsanity.com/paper/2506.08532/) (2025)
- [AdaptFly: Prompt-Guided Adaptation of Foundation Models for Low-Altitude UAV Networks](https://mlsanity.com/paper/2511.11720/) (2025)
- [Context-Aware Information Transfer via Digital Semantic Communication in UAV-Based Networks](https://mlsanity.com/paper/2601.01430/) (2026)

## Abstract (arXiv)

Internet of Things (IoT) involves sensors for monitoring and wireless networks for efficient communication. However, resource-constrained IoT devices and limitations in existing wireless technologies hinder its full potential. Integrating Unmanned Aerial Vehicles (UAVs) into IoT networks can address some challenges by expanding its' coverage, providing security, and bringing computing closer to IoT devices. Nevertheless, effective data collection in UAV-assisted IoT networks is hampered by factors, including dynamic UAV behavior, environmental variables, connectivity instability, and security considerations. In this survey, we first explore UAV-based IoT networks, focusing on communication and networking aspects. Next, we cover various UAV-based data collection methods their advantages and disadvantages, followed by a discussion on performance metrics for data collection. As this article primarily emphasizes reliable and efficient data collection in UAV-assisted IoT networks, we briefly discuss existing research on data accuracy and consistency, network connectivity, and data security and privacy to provide insights into reliable data collection. Additionally, we discuss efficient data collection strategies in UAV-based IoT networks, covering trajectory and path planning, collision avoidance, sensor network clustering, data aggregation, UAV swarm formations, and artificial intelligence for optimization. We also present two use cases of UAVs as a service for enhancing data collection reliability and efficiency. Finally, we discuss future challenges in data collection for UAV-assisted IoT networks.