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.

Delivering real-time multimedia content for safety applications over vehicular networks presents significant challenges due to rapidly changing network topology, high node mobility, and fluctuating traffic demands, compounded by substantial data volumes and network resource limitations. This paper proposes the Multi-Path Transmission Protocol for Video Streaming (MPTP-VS) to address these issues by optimizing video transmission in road incident scenarios. Utilizing a Fog Computing architecture, the protocol aims to enhance video delivery efficiency while maintaining high Quality of Service (QoS) and Quality of Experience (QoE) through effective management of network resources such as bandwidth, jitter, and latency. Leveraging Ant Colony Optimization (ACO), MPTP-VS establishes an adaptive multi-path routing strategy that dynamically discovers network topology and promptly transmits data, ensuring seamless video streaming without prior knowledge of the network topology. The protocol’s performance was evaluated using a series of experiments within an Omnet++ simulation environment, focusing on video streaming in Vehicular Fog Computing (VFC) environments during road incidents. Key performance metrics, including latency, response time, path discovery time, and throughput, were compared against those of Ad Hoc On-Demand Distance Vector (AODV) and Dynamic MANET On-demand (DYMO) protocols. The evaluation of QoE included the analysis of visual quality under conditions of rapid vehicle movement, measuring received quality, Peak Signal to Noise Ratio (PSNR), and resolution. Additionally, the QoE performance of MPTP-VS was compared with current Content Delivery Network (CDN) systems employing Adaptive Bitrate Streaming (ABS), which dynamically adjusts video quality according to network conditions. The findings demonstrate the effectiveness of the proposed protocol and its potential to significantly enhance video streaming performance in vehicular environments.

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles.