Biblio

Recently, unmanned aerial vehicle (UAV) swarm has been advocated to provide diverse data-centric services including data relay, content caching and computing task offloading in vehicular networks due to their flexibility and conveniences. Since only offloading computing tasks to edge computing devices (ECDs) can not meet the real-time demand of vehicles in peak traffic flow, this paper proposes to combine edge computing and UAV swarm for cooperative task offloading in vehicular networks. Specifically, we first design a cooperative task offloading framework that vehicles' computing tasks can be executed locally, offloaded to UAV swarm, or offloaded to ECDs. Then, the selection of offloading strategy is formulated as a mixed integer nonlinear programming problem, the object of which is to maximize the utility of the vehicle. To solve the problem, we further decompose the original problem into two subproblems: minimizing the completion time when offloading to UAV swarm and optimizing the computing resources when offloading to ECD. For offloading to UAV swarm, the computing task will be split into multiple subtasks that are offloaded to different UAVs simultaneously for parallel computing. A Q-learning based iterative algorithm is proposed to minimize the computing task's completion time by equalizing the completion time of its subtasks assigned to each UAV. For offloading to ECDs, a gradient descent algorithm is used to optimally allocate computing resources for offloaded tasks. Extensive simulations are lastly conducted to demonstrate that the proposed scheme can significantly improve the utility of vehicles compared with conventional schemes.

UAANET (UAV Ad hoc Network) is defined as an autonomous system made of swarm of UAVs (Unmanned Aerial Vehicle) and GCS (Ground Control Station). Compared to other types of MANET (Mobile Ad hoc network), UAANET have some unique features and bring several challenges. One of them is the design of routing protocol. It must be efficient for creating routes between nodes and dynamically adjusting to the rapidly changing topology. It must also be secure to protect the integrity of the network against malicious attackers. In this paper, we will present the architecture and the performance evaluation (based on both real-life experimental and emulation studies) of a secure routing protocol called SUAP (Secure UAV Ad hoc routing Protocol). SUAP ensures routing services between nodes to exchange real-time traffic and also guarantees message authentication and integrity to protect the network integrity. Additional security mechanisms were added to detect Wormhole attacks. Wormhole attacks represent a high level of risk for UAV ad hoc network and this is the reason why we choose to focus on this specific multi node attack. Through performance evaluation campaign, our results show that SUAP ensures the expected security services against different types of attacks while providing an acceptable quality of service for real-time data exchanges.

Unmanned Aerial Vehicles (UAVs) are autonomous aircraft that, when equipped with wireless communication interfaces, can share data among themselves when in communication range. Compared to single UAVs, using multiple UAVs as a collaborative swarm is considerably more effective for target tracking, reconnaissance, and surveillance missions because of their capacity to tackle complex problems synergistically. Success rates in target detection and tracking depend on map coverage performance, which in turn relies on network connectivity between UAVs to propagate surveillance results to avoid revisiting already observed areas. In this paper, we consider the problem of optimizing three objectives for a swarm of UAVs: (a) target detection and tracking, (b) map coverage, and (c) network connectivity. Our approach, Dual-Pheromone Clustering Hybrid Approach (DPCHA), incorporates a multi-hop clustering and a dual-pheromone ant-colony model to optimize these three objectives. Clustering keeps stable overlay networks, while attractive and repulsive pheromones mark areas of detected targets and visited areas. Additionally, DPCHA introduces a disappearing target model for dealing with temporarily invisible targets. Extensive simulations show that DPCHA produces significant improvements in the assessment of coverage fairness, cluster stability, and connection volatility. We compared our approach with a pure dual- pheromone approach and a no-base model, which removes the base station from the model. Results show an approximately 50% improvement in map coverage compared to the pure dual-pheromone approach.