The Internet of Vehicles (IoV) has become a fundamental component of intelligent transportation systems, enabling advanced services such as traffic management, collision avoidance, and driver assistance. However, the high level of openness and connectivity in IoV makes it highly vulnerable to Distributed Denial of Service (DDoS) attacks. Such attacks can severely disrupt vehicular communications and compromise user safety. Meanwhile, existing intrusion detection systems (IDSs) suffer from limitations in adapting to IoV’s dynamic topology and resource-constrained environment. To address these challenges, this paper proposes a novel ensemble learning–based IDS framework for accurate detection of multiple DDoS attack types. The framework integrates two optimized convolutional neural network (CNN) models to enhance detection performance. In addition, a dynamic deployment algorithm is designed to determine the optimal placement of the IDS among fog servers, unmanned aerial vehicles (UAVs), and roadside units (RSUs) based on real-time network conditions. To further improve resource efficiency, a game-theoretic strategy is employed to minimize energy consumption while maintaining high detection accuracy. Extensive experiments conducted on three benchmark datasets—VDoS-LRS, CICDDoS2019, and VDDD—demonstrate that the proposed IDS achieves over 99% detection accuracy and significantly outperforms existing methods in terms of precision and computational efficiency. These results confirm the effectiveness of combining ensemble deep learning with game-theoretic strategy for securing IoV against DDoS attacks.

The advent of 6G technology demands flexible, scalable wireless architectures to support ultra-low latency, high connectivity, and high device density. The Open Radio Access Network (O-RAN) framework, with its open interfaces and virtualized functions, provides a promising foundation for such architectures. However, traditional fixed base stations alone are not sufficient to fully capitalize on the benefits of O-RAN due to their limited flexibility in responding to dynamic network demands. The integration of Unmanned Aerial Vehicles (UAVs) as mobile RUs within the O-RAN architecture offers a solution by leveraging the flexibility of drones to dynamically extend coverage. However, UAV operating in diverse environments requires frequent retraining, leading to significant energy waste. We proposed transfer learning based on Dueling Double Deep Q network (DDQN) with multi-step learning, which significantly reduces the training time and energy consumption required UAVs to adapt to new environments. We designed simulation environments and conducted ray tracing experiments using Wireless InSite with real-world map data. In the two simulated environments, training energy consumption was reduced by 30.52% and 58.51%, respectively. Furthermore, tests on real-world maps of Ottawa and Rosslyn showed energy reductions of 44.85% and 36.97%, respectively.

Integrating unmanned aerial vehicles (UAVs) into wireless communication as aerial platforms to mount small cell base stations has grown rapidly in recent years. One of the main objectives of UAV integration into wireless networks is to optimize UAV deployment while meeting user expectations with the fewest UAVs. To ensure that users receive the requested data rate, management of UAV placement and user association is necessary due to the limited capacity of aerial base stations. Besides the user-base station distance, environmental conditions and propagation mode affect the data rate received by the users. When accounting for uncertain conditions, network management decisions become more realistic and productive. This article considers a random propagation mode for each link depending on the environmental conditions of the desired area. We exploit the stochastic programming framework to reflect propagation mode uncertainty in the optimization problem, which impacts the received data rate and path loss. The suggested mathematical formulation determines the minimum number of required UAVs, their 3-D positions, and the best user association strategy. The proposed model also includes interference-aware constraints for optimal radio resource allocation to base stations. The nonlinear path loss and line-of-sight (LoS) probability distribution functions in terms of the base station positions lead to a nonlinear formulation. We obtain a mixed-binary linear formulation by replacing nonlinear functions with their piecewise linear approximations and solve the model accurately using the CPLEX solver. The implementation results show that stochastic approaches provide more accurate diagnoses of the environment, as well as superior performance to deterministic optimization.

In recent years, using the Unmanned Aerial Vehicle (UAV) as a Base Stations (BS) to cover users in wireless networks has increased dramatically. One of the main goals of integrating UAVs into wireless networks is to deploy UAVs in such a way that user expectations are met with the fewest number of UAVs. To achieve this aim, the coverage area of each UAV should include as many users as possible. Furthermore, the resources assigned to the backhaul links for such UAV deployments must fulfill the requirements of users served by each UAV. In this paper the goal is to position the least number of UAVs in a 3D position to cover cellular network users. To provide appropriate quality of service, we consider a maximum path loss allowed for the network. The path loss of potential links is affected by the propagation environment and might vary depending on network structure. To reflect this uncertainty, path loss is expressed as a random variable with a probability distribution based on environmental characteristics. As a result, we're dealing with an optimization problem with uncertain information. We use stochastic programming to work with uncertain information and formulate the UAV positioning and data rate assignment problem. The implementation results of our proposed mixed-binary linear mathematical model and Monte Carlo simulation in various scenarios show its optimum performance in different dimensions.

Using unmanned aerial vehicles (UAVs) to cover users in wireless networks has increased in recent years. Deploying UAVs in appropriate positions is important to cover users and nodes properly. In this article, we propose an efficient approach to determine the minimum number of required UAVs and their optimal positions. To this end, we use an iterative algorithm that updates the number of required UAVs at each iteration. To determine the optimal position for the UAVs, we present a mathematical model and solve it accurately after linearizing. One of the inputs of the mathematical model is a set of candidate points for UAV deployments in 2-D space. The mathematical model selects a set of points among candidate points and determines the altitude of each UAV. To provide a suitable set of candidate points, we also propose a candidate point selection method: the MergeCells method. The simulation results show that the proposed approach performs better than the 3-D P-median approach introduced in the literature. We also compare different candidate point selection approaches, and we show that the MergeCells method outperforms other methods in terms of the number of UAVs, user data rates, and simulation time.

Wireless sensor networks (WSN) consist of some nodes that have limited processing power, memory and energy source. These constraints cause the algorithms that presented in this field focus on these constraints. Data aggregation is any process in which information is gathered and expressed in a summary form. Data aggregation has been put forward as an essential paradigm for wireless routing in sensor networks. The idea is to combine the data coming from different sources, eliminating redundancy, minimizing the number of transmissions and thus saving energy. For this purpose, sensor nodes must form aggregation tree, then forward sensed data to the root of this tree. Data is aggregated in intermediate nodes and the results are sent toward the root. In this paper we propose an energy aware algorithm for construction the aggregation tree. The proposed algorithm considers both the energy and distance parameters to construct the tree. Simulation results show that the proposed algorithm has better performance in terms of energy efficiency and number of failed nodes which increases the network lifetime.