Frequency hopping-based anti-jamming techniques are not always applicable in vehicular ad-hoc networks (VANETs) due to the high mobility of onboard units (OBUs) and the large-scale network topology. In this paper, we use unmanned aerial vehicles (UAVs) to relay the message of an OBU and improve the communication performance of VANETs against smart jammers that observe the ongoing OBU and UAV communication status and even induce the UAV to use a specific relay strategy and then attack it accordingly. More specifically, the UAV relays the OBU message to another roadside unit (RSU) with a better radio transmission condition if the serving RSU is heavily jammed or interfered. The interactions between a UAV and a smart jammer are formulated as an anti-jamming UAV relay game, in which the UAV decides whether or not to relay the OBU message to another RSU, and the jammer observes the UAV and VANET strategy and chooses the jamming power accordingly. The Nash equilibria of the UAV relay game are derived to reveal how the optimal UAV relay strategy depends on the transmit cost and the UAV channel model. A hotbooting policy hill climbing (PHC)-based UAV relay strategy is proposed to help VANET resist jamming in the dynamic game without being aware of the VANET model and the jamming model. Simulation results show that the proposed relay strategy can efficiently reduce the bit error rate of the OBU message and thus increase the utility of the VANET compared with a Q-learning based scheme.

Online video service becomes prevalent nowadays, as people are accustomed to getting information in form of videos. While home users can easily retrieve all kinds of contents onto their laptops or smartphones, vehicular users are constrained by intermittent connectivity to roadside units (RSUs). Besides, as the load of cellular infrastructure increases dramatically, it is envisioned that the cellular network can be offloaded by utilizing RSUs and vehicular relays. In this paper, we propose a cooperative downloading mechanism in heterogeneous vehicular networks that comprises vehicular ad hoc network (VANET) and cellular network. In this mechanism, RSUs act as traffic managers to fetch proper data from the Internet and then distribute to vehicles in an approximately optimal manner. Specifically, based on vehicular mobility prediction and inter-node throughput estimation, a storage time aggregated graph (STAG) is constructed for planning transmission scheme, then an iterative greedy-driven algorithm is designed for a suboptimal solution which has polynomial computational time complexity. Compared with max-throughput and min-delay cooperative downloading (MMCD), simulation results show that our approach outperforms MMCD by 5%~10% with respect to offload fraction.

Unmanned aerial vehicles (UAVs) that are widely utilized for video capturing, processing and transmission have to address jamming attacks with dynamic topology and limited energy. In this paper, we propose a reinforcement learning (RL)-based UAV anti-jamming video transmission scheme to choose the video compression quantization parameter, the channel coding rate, the modulation and power control strategies against jamming attacks. More specifically, this scheme applies RL to choose the UAV video compression and transmission policy based on the observed video task priority, the UAV-controller channel state and the received jamming power. This scheme enables the UAV to guarantee the video quality-of-experience (QoE) and reduce the energy consumption without relying on the jamming model or the video service model. A safe RL-based approach is further proposed, which uses deep learning to accelerate the UAV learning process and reduce the video transmission outage probability. The computational complexity is provided and the optimal utility of the UAV is derived and verified via simulations. Simulation results show that the proposed schemes significantly improve the video quality and reduce the transmission latency and energy consumption of the UAV compared with existing schemes.

The growth of smart vehicles and computation-intensive applications poses new challenges in providing reliable and efficient vehicular services. Offloading such applications from vehicles to mobile edge cloud servers has been considered as a remedy, although resource limitations and coverage constraints of the cloud service may still result in unsatisfactory performance. Recent studies have shown that exploiting the unused resources of nearby vehicles for application execution can augment the computational capabilities of application owners while alleviating heavy on-board workloads. However, encouraging vehicles to share resources or execute applications for others remains a sensitive issue due to user selfishness. To address this issue, we establish a novel computation offloading marketplace in vehicular networks where a VCG-based reverse auction mechanism utilizing integer linear programming (ILP) problem is formulated while satisfying the desirable economical properties of truthfulness and individual rationality. As ILP has high computation complexity which brings difficulties in implementation under larger and fast changing network topologies, we further develop an efficient unilateral-matching-based mechanism, which offers satisfactory sub-optimal solutions with polynomial computational complexity, truthfulness and individual rationality properties as well as matching stability. Simulation results show that, as compared with baseline methods, the proposed unilateral-matching-based mechanism can greatly improve the system efficiency of vehicular networks in all traffic scenarios.

Underwater sensor networks (UWSNs) are vulnerable to jamming attacks due to the narrow frequency bandwidth and the fast fading channels. In this paper, we propose a reinforcement learning (RL)-based antijamming relay scheme for UWSNs that enables an underwater relay to decide whether to leave the heavily jammed location and choose the relay power based on the state that consists of the bit error rate of the previous transmission, the previous relay power, the current transmit power of the sensor, and the jamming power measured by the relay node. We also propose a deep-RL-based relay scheme to further improve the relay performance for the node that supports deep learning computation. We discuss the computational complexity of the deep-RL-based relay scheme and provide the relay performance bound regarding the bit error rate, energy consumption, and utility of the relay node. Experiments taken in a nonanechoic pool with underwater transducers against smart jamming attacks verify the analysis results. According to the experimental results, the proposed relay scheme can improve the relay performance compared with the benchmark underwater relay schemes.