Open research questions in UAV Applications and Optimization
277 unresolved questions extracted from the limitations and future-work sections of 976 UAV Applications and Optimization papers in our library. Each links back to the study that raised it.
What the literature leaves open
Investigating the effect of different pretraining strategies on the proposed framework, - Evaluating the proposed framework on larger datasets, - Exploring the application of the proposed framework to other domains, - Investigating the impact of link variability and model uncertainty on the decision statistic
Existing recovery techniques are inefficient. High-resolution monitoring of residues is needed. Objectives for scalar offloading do not adequately represent the trade-offs between latency and accuracy in agriculture edge-cloud inference.
The highly dynamic nature of FANETs, which leads to frequent link disruptions and reduced network stability. The limited battery resources of UAV nodes, which leads to reduced network lifetime and increased energy consumption. The need to efficiently select reliable UAV-MPRs to transmit data packets and control messages in FANETs.
Energy and link expiration time aware MPR selection based modified OLSR protocol for UAV ad hoc networks · 2026 · DOIInvestigating the performance of the EL-OLSR protocol in a real-world environment. Evaluating the impact of other factors on the performance of the EL-OLSR protocol. Developing new protocols that can further improve the performance of FANETs.
Energy and link expiration time aware MPR selection based modified OLSR protocol for UAV ad hoc networks · 2026 · DOISimulation-only evaluation, - No real-world deployment or testing, - Limited to a specific type of WSN (agricultural monitoring)
Real-world deployment and testing of EH-SWADS, - Exploration of other applications of EH-SWADS beyond agricultural monitoring, - Investigation of the impact of different environmental conditions on EH-SWADS performance
the lack of a thorough review of trajectory-aware offloading decisions in UAV-aided MEC - the need for optimizing offloading decisions along with trajectory planning
Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey · 2024 · DOIConsidering the energy constraint of UAVs and delay requirement of tasks, many efforts should be devoted to pursuing lower service latency, which however is under explored in this innovational architecture.
Multi-Task Parallel Execution-Oriented Content Caching, Computation Offloading and Channel Allocation in UAV-Assisted MEC Network · 2026 · DOIHowever, both cooperative charging scheduling and insufficient charging facility problems in UAV charging scenarios have been rarely studied.
This paper addresses the challenges of unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) in high-density user mobility scenarios, a field that has not been extensively explored in current research.
However, UPT was still constrained by limited autonomous perception and path planning capabilities, insufficient universality of payload platforms, a lack of standardized device interfaces, as well as challenges related to endurance, communication, and operational stability under adverse weather conditions.
Unmanned aerial vehicle payload technology applications in agriculture and other low-altitude scenarios: a review · 2025 · DOIFuture research should focus on lightweight and multifunctional payload design, intelligent operation control, and modular and standardized integration, while building a "satellite-UAV-ground" collaborative perception and decision-making system.
Unmanned aerial vehicle payload technology applications in agriculture and other low-altitude scenarios: a review · 2025 · DOIThis scarcity results in two primary challenges: imbalanced feature sampling due to limited variability in high-dimensional operational parameters against the backdrop of substantial changes in low-dimensional sampling locations, and diminished generalizability stemming from insufficient data samples.
A Disentangled Representation Learning Framework for Low-Altitude Network Coverage Prediction · 2025 · DOIOpen issues that are briefly discussed across papers but require additional research are summarized on basis of the gaps identified.
The goals are to understand the volume and trends of research, identify use case scenarios and proposed architectures, classify the core topics addressed, explore group techniques explored, recognize task types considered, and summarize open issues needing further work.
Uncertain and incomplete data regarding the reliability of individual mission segments. Complex logical interactions between checkpoints. Limited operational data.
Traditional reliability engineering methods fall short in the context of uncertain data. There is a need for a quantitative tool for assessing the likelihood of mission success.
Further research can focus on improving the scalability of the proposed method. The approach can be extended to other applications such as package delivery or surveillance.
Traditional path planning algorithms often fail to adapt to rapidly changing conditions. Ensuring safe UAV operations is critical to preventing secondary disasters.
Future work should map the Pareto frontier between delivery time and energy use. The study suggests exploring the impact of rain, strong crosswinds, and temperatures below 10°C on performance. Urban environments and variables such as building-induced turbulence and electromagnetic interference should be studied.
Smart Energy Optimization for Autonomous Delivery Drones : Insights for E-Commerce Applications · 2026 · DOIThe limited battery range of delivery drones is a significant challenge. There is a need for smarter operational choices to improve energy efficiency without changing the hardware.
Smart Energy Optimization for Autonomous Delivery Drones : Insights for E-Commerce Applications · 2026 · DOIEvaluating the proposed framework using real-world experiments. Applying the game-theoretic strategy to other resource-constrained systems. Integrating the proposed framework with other security mechanisms to enhance the overall security of IoV systems.
This paper addressed the key limitations of existing IDSs in the IoV. These limitations include poor detection of diverse DDoS attack types and weak adaptability to high network density, dynamic topology, and limited computational resources. Such issues reduce IDS effectiveness in real-time vehicular environments. To overcome these challenges, an ensemble learning– based IDS was proposed. The framework integrates two optimized CNN models for both binary and multi-class classification. The system effectively detects twelve types of DDoS attacks and adapts to changing IoV conditions. A dynamic deployment algorithm was also introduced to select the best IDS location among fog nodes, RSUs, and UAVs. This ensures continuous detection during congestion and partial system failures. In addition, a game-theoretic optimization strategy was designed to activate the IDS only when an attack is likely. This approach reduces energy con- sumption and limits unnecessary processing overhead. The proposed system was evaluated using three bench- mark datasets: VDoS-LRS, CICDDoS2019, and VDDD. The experimental results showed detection accuracy of over 99%. The proposed method outperformed existing approaches in terms of accuracy, scalability, adaptability, and efficiency. The simulation results also confirmed the effectiveness of the game-theoretic mechanism in limiting attacker advantage. For future work, advanced feature selection techniques such as information gain and Fast Correlation-Based Filter (FCBF) will be investigated. More efficient image-based packet representation methods will also be explored. These improvements aim to reduce per-packet classification time and enhance real-time performance in large-scale IoV systems. Authors’ contributions S.H. and Z.J. designed the study. Z.J. and S.S. and A.M. implemented the proposed model and conducted the experi- ments. S.H. and Z.J. analyzed the results and prepared figures. Z.J. and S.S. and A.M. wrote the main manuscript text. All authors reviewed and approved the final manuscript. Funding The authors declare that no funds, grants, or other support were received for conducting this study. Data availability No datasets were generated or analysed during the current study.
The training was performed entirely in simulation with a 2D discrete environment. Transfer learning from simulation to real physical drone platforms, robustness to sensor noise, actuator delays, and performance degradation due to sim-to-real gap in autonomous drone navigation using DQN has not been addressed.
Optimized Autonomous Drone Navigation Using Deep Q-Network Based Reinforcement Learning · 2026 · DOIThe paper does not specify grid environment sizes tested or how DQN performance scales with increasing grid dimensions (from the NxN notation used). Scalability to large navigation spaces (e.g., 100x100 or 1000x1000 grids) and the resulting memory/computational requirements for experience replay buffers and Q-value approximation remains uncharacterized.
Optimized Autonomous Drone Navigation Using Deep Q-Network Based Reinforcement Learning · 2026 · DOI
Most-cited papers in UAV Applications and Optimization
- Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges · IEEE Access · 2019 · 2,251 citations
- Energy Minimization for Wireless Communication With Rotary-Wing UAV · IEEE Transactions on Wireless Communications · 2019 · 2,045 citations
- Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks · IEEE Transactions on Wireless Communications · 2018 · 1,837 citations
- Securing UAV Communications via Joint Trajectory and Power Control · IEEE Transactions on Wireless Communications · 2019 · 573 citations
- Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks · IEEE Transactions on Wireless Communications · 2019 · 525 citations
- Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges · IEEE Sensors Journal · 2021 · 507 citations
- What Will the Future of UAV Cellular Communications Be? A Flight From 5G to 6G · IEEE Communications Surveys & Tutorials · 2022 · 491 citations
- Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks · IEEE Transactions on Wireless Communications · 2019 · 422 citations
- Trajectory Design for Completion Time Minimization in UAV-Enabled Multicasting · IEEE Transactions on Wireless Communications · 2018 · 419 citations
- Multi-Agent Deep Reinforcement Learning for Task Offloading in UAV-Assisted Mobile Edge Computing · IEEE Transactions on Wireless Communications · 2022 · 417 citations
Most recent work
- Sec-GNN-Driven Joint Multidimensional Anti-Eavesdropping Optimization for Secure UAV-Satellite Communications · IEEE Internet of Things Journal · 2026
- An Optimized Collaborative Routing Model for Trucks and Heterogeneous Drones in Delivery and Pickup Services · IEEE Transactions on Intelligent Transportation Systems · 2026
- Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks · IEEE Transactions on Mobile Computing · 2026
- Sum Secrecy Rate Enhancement in Low-Altitude Intelligent Networks With Mixed Obstacles · IEEE Internet of Things Journal · 2026
- An Incentive Assignment Scheme of UAV Clients for Federated Intelligent Inspection Based on Communication-Sensing-Computing Integration · IEEE Transactions on Mobile Computing · 2026
- Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning With Large Language Models · IEEE Transactions on Mobile Computing · 2026
- Dubins Path Planning of Heterogeneous UAV Collaborative Data Collection for IoT Network · IEEE Transactions on Intelligent Transportation Systems · 2026
- Recent Advances in Transformer and Large Language Models for UAV Applications · ACM Computing Surveys · 2026
- Refining pesticide use to reduce yield loss: How drone plant protection transforms smallholder pest management · Food Policy · 2026
- Trajectory planning for multi-UAV collaborative target tracking based on policy search optimized DMPC · Aerospace Science and Technology · 2026
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