Recently, task offloading in the Internet of Drones (IoD) is considered one of the most important challenges because of the high transmission delay due to the high mobility and limited capacity of drones. This particularity makes it difficult to apply the conventional task offloading technologies, such as cloud computing and edge computing, in IoD environments. To address these limits, and to ensure a low task offloading delay, in this paper we propose PSO BS-Fog, a task offloading optimization that combines a particle swarm optimization (PSO) heuristic with fog computing technology for the IoD. The proposed solution applies the PSO for task offloading from unmanned aerial vehicles (UAVs) to fog base stations (FBSs) in order to optimize the offloading delay (transmission delay and fog computing delay) and to guarantee higher storage and processing capacity. The performance of PSO BS-Fog was evaluated through simulations conducted in the MATLAB environment and compared against PSO UAV-Fog and PSO UAV-Edge IoD technologies. Experimental results demonstrate that PSO BS-Fog reduces task offloading delay by up to 88% compared to PSO UAV-Fog and by up to 97% compared to PSO UAV-Edge.

Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, as well as the stringent requirements of task offloading, such as reduced delay. To overcome these challenges, in this paper, we propose a task-offloading optimization strategy using a heuristic genetic algorithm (GA) with hybrid fog computing technology for the Internet of Drones, named GA Hybrid-Fog. The proposed solution employs a GA for task offloading from edge Unmanned Aerial Vehicles (UAVs) to both fog base stations (FBSs) and fog UAVs (FUAVs) in order to optimize offloading delays (transmission and fog computing delays) and guarantee higher storage and processing capacity. Experimental results show that GA Hybrid-Fog achieves greater improvements in task-offloading delays compared to other IoD technologies (GA BS-Fog, GA UAV-Fog, and GA UAV-Edge).

Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments.

The Internet of Drones (IoD) is increasingly adopted in mission-critical applications, but it remains vulnerable to a wide range of cyberattacks due to its open communication channels and resource-constrained nodes. Existing Intrusion Detection Systems (IDS) often neglect class imbalance and computational limitations, resulting in poor generalization and impractical deployment. This study aims to develop an efficient and deployable IDS framework for the IoD by addressing two key challenges: (i) the severe class imbalance in real-world traffic datasets, and (ii) the high computational complexity of Deep Learning (DL)-based models. We propose a two-phase methodology. In Phase 1, validated undersampling strategies are applied to rebalance the CIC-IoT2023 dataset, after which baseline classifiers are trained to assess detection performance. In Phase 2, the best-performing models serve as teacher networks in a multi-teacher knowledge distillation (MTKD) framework—implemented with average-weighted and confidence-weighted variants—to train lightweight Multi-Layer Perceptron (MLP) student models of varying capacities. These students are optimized for deployment on resource-constrained IoD edge devices. Effectiveness is assessed using both classification and computational efficiency metrics. Results show that Instance Hardness Threshold undersampling consistently outperformed Random Under Sampler across all baseline models, enhancing detection of minority attack classes. The best-performing models—LSTM and XGBoost—were selected as teachers for MTKD. Applying average-weighted and confidence-weighted MTKD to MLP1x8 increased accuracy by 0.03% and 0.09%, respectively, while confidence-weighted also reduced inference time by 0.0006 s. These gains were achieved without increasing the computational footprint, as the model retained its minimal size (0.0039 MB) and low parameter count (448). The proposed two-phase framework provides a practical solution for building lightweight and accurate IDS models suitable for real-time deployment in resource-constrained drone environments.