The Internet of Vehicles (IoV) can offer safe and comfortable driving experience, by the enhanced advantages of space–air–ground-integrated networks (SAGINs), i.e., global seamless access, wide-area coverage, and flexible traffic scheduling. However, due to the huge popular traffic volume, limited cache/power resources, and the heterogeneous network infrastructures, the burden of backhaul link will be seriously enlarged, degrading the energy efficiency of IoV in SAGIN. In this article, to implement the popular content severing multiple vehicle users (VUs), we consider a cache-enabled satellite-UAV-vehicle-integrated network (CSUVIN), where the geosynchronous Earth orbit (GEO) satellite is regard as a cloud server, and unmanned aerial vehicles are deployed as edge caching servers. Then, we propose an energy-aware coded caching strategy employed in our system model to provide more multicast opportunities, and to reduce the backhaul transmission volume, considering the effects of file popularity, cache size, request frequency, and mobility in different road sections (RSs). Furthermore, we derive the closed-form expressions of total energy consumption both in single-RS and multi-RSs scenarios with asynchronous and synchronous services schemes, respectively. An optimization problem is formulated to minimize the total energy consumption, and the optimal content placement matrix, power allocation vector, and coverage deployment vector are obtained by well-designed algorithms. We finally show, numerically, our coded caching strategy can greatly improve energy efficient performance in CSUVINs, compared with other benchmarked caching schemes under the heterogeneous network conditions.

Mobile edge computing provides storage and computation resources of proximal devices to satisfy the real-time and low-energy communication demands of the Internet of Things (IoT). However, in the areas without terrestrial base station infrastructures, the IoT sensors have trouble implementing reliable and stable connections, which results in the difficulties of data gathering and data caching. In this paper, we consider a space-air-ground integrated mobile edge caching IoT system composed of satellite and unmanned aerial vehicles (UAVs), where LEO satellite broadcasts data, and UAVs collect the data from decentralized ground sensors. Since the sensors’ low-power property leads data loss, fault-tolerant codes are employed for availability protection. We first derive the exact expressions of system availability and communication cost for data repair and collection. Then, to address the problems of the lower availability, we exploit an intelligent optimization to determine the erasure coding parameters. Lastly, we further optimize the system parameters, i.e., communication ranges and unit power costs of UAV and decentralized sensors, to minimize the total communication cost. Simulation results show that, compared to MDS codes and regenerating codes, adaptive minimum storage regenerating (AMSR) codes with optimized parameters can significantly reduce total communication cost and maintain availability of the system.

Unmanned aerial vehicles (UAVs) have been recently considered as a flying platform to provide wide coverage and relaying services for mobile users (MUs). Mobile edge computing (MEC) is developed as a new paradigm to improve quality of experience of MUs in future networks. Motivated by the high flexibility and controllability of UAVs, in this study, the authors study a multi‐UAV‐enabled MEC system, in which UAVs have computation resources to offer computation offloading opportunities for MUs, aiming to reduce MUs' total consumptions in terms of time and energy. Considering the rich computation resource in the remote cloud centre, they propose the MUs‐Edge‐Cloud three‐layer network architecture, where UAVs play the role of flying edge servers. Based on this framework, they formulate the computation offloading issue as a mixed‐integer non‐linear programming problem, which is difficult to obtain an optimal solution in general. To address this, they propose an efficient Q ‐learning based computation offloading algorithm (QCOA) to reduce the complexity of optimisation problem. Numerical results show that the proposed QCOA outperforms benchmark offloading policies (e.g. random offloading, traversal offloading). Furthermore, the proposed three‐layer network architecture achieves a 5% benefits compared with the traditional two‐layer network architecture in terms of MUs' energy and time consumptions.

Wireless sensor networks are generally used to assist in collecting and transmitting data where humans cannot directly explore. But in a scenario with complex terrestrial environment, the ground communication links between sensors become so weak to provide reliable and high-speed services. Unmanned aerial vehicles (UAVs) can be used as flying relays to enhance connective reliability of terrestrial wireless sensor networks. However, in a UAV-assisted wireless senor network, if the UAV shares the same spectrum with sensors, the interference degrades the quality of communication links when sensors exist in pairs under co-channel conditions. Motivated thereby, we manage the interference by optimizing the transmit power of all communication nodes and planning the trajectory of UAV to achieve the goal of maximizing the sum throughput of the target sensor. Due to the nonconvexity of the optimization problems, we utilize difference of two convex functions (D.C.) programming and successive convex approximation to obtain the suboptimal solutions. Simulation results prove that the minimum signal-to-interference-plus-noise ratio (SINR) required by sensor pairs, flight altitude and maximum transmit power of the UAV can be carefully selected to maximize the sum throughput of target sensor, when the UAV’s trajectory is pre-planned. The successive trajectory planning algorithm is also employed to significantly improve the sum throughput.

The integrated satellite-terrestrial network can provide broadband wireless access in a wide coverage, efficient, and cost-effective manner, and is regarded as one of the most promising infrastructures for future heterogeneous network toward fifth generation. With the development of next generation of high throughput satellites (HTS), the application of the millimeter-wave (mmWave) band HTS is viewed as a vital role in the future integrated satellite-terrestrial network. Considering the rain attenuation is the dominant fading factor of the mmWave channel, we first propose a practical time-varying rain attenuation prediction model based on the autoregressive-moving-average (ARMA) model. Then, we develop an adaptive coding transmission (ACT) scheme based on the analog fountain codes combine the ARMA mmWave channel prediction model. The key parameters are selected based on a tradeoff between the decoding failure probability, block length, and overhead for our ARMA-based ACT scheme. Simulation results show that our proposed ACT scheme can effectively improve the throughput.

Network selection is one of important techniques in cognitive radio networks. Multiple authorized networks can provide secondary users with more spectrum resources by network selection. Network selection is the key to spectrum sharing between cognitive radio networks and multiple primary networks.Traditional network selection algorithms are off-line selection methods which are based on prior knowledge of primary networks. However, in the complex network environment, it is impossible to get the prior knowledge from multiple primary networks, so the off-line network selection methods are lack of efficiency. In order to meet these challenges, this paper aims at improving the quality of service of cognitive users, and based on reinforcement learning method and the achievements of dynamic spectrum access of cognitive radio in single primary network environment, proposed a deep reinforcement learning based online network selection method of cognitive radio networks with multiple primary networks.