This paper considers a mobile edge computing (MEC) system where an unmanned aerial vehicle (UAV) offloads demanding computational tasks to the access point (AP), and AP cooperatively sends jamming noise to protect the confidentiality and secrecy of data transmission. In this system, the computation from UAV is portioned into two parts with one executed locally on UAV and the other offloaded to MEC for processing. To minimize the total energy-consumption of UAV for computing and offloading, we jointly optimize computation and communication resource allocation, subject to secure offloading rate and computation-latency constraints. Although the formulated problem is non-convex, we transform it into a convex problem by analytical means. Numerical results verify the superiority of our proposed scheme in terms of energy-consumption and quantify the performance of our proposed offloading scheme in various scenarios.

Due to the great potential of the combination of machine learning technology and unmanned aerial vehicle (UAV) enabled wireless communications, various optimization algorithms on resource allocation have been proposed for the Internet of Things. UAVs not only can perform the missions under the extreme conditions but also enhance the overall performance of the system as an aerial relay assisting transmission in the public and civil domains, which have been received extensive attentions. However, with the limited capacity and power constraints, they are difficult to support the transmission for the big data information users. In addition, the lack of spectrum resource poses challenges to satisfy the quality of service (QoS) of mobile users in wireless networks. To contribute to these urgent problems, this article first studies the potential and effective applications of UAVs, by introducing the Chinese remainder theorem (CRT) and nonorthogonal multiple access (NOMA) technologies into UAV relay networks. Two scenarios with/without direct transmissions between the source and destination nodes are investigated, following the decomposition and reconstruction mechanisms to satisfy the big data information transmission. Considering the user fairness, we further discuss the effect of the UAV numbers to the overall system capacity. To maximize the system capacity, the designs of transmission protocol and receiver are also discussed, in various channel conditions. Finally, a low complexity and efficient two‐stage power allocation scheme is established for the perspective of users and UAV relays.

The high‐speed dynamic environment and massive information transmitted via wireless communications in the vehicular ad hoc networks (VANETs) pose a great challenge to privacy and security. To overcome this issue, use of the content‐centric networking (CCN) provides a potential and practical solution. In‐network caching is a main feature for future smart cities, in which the content is mainly placed in network nodes. Therefore, how to effectively select the cache locality and cache content is essential to improve the overall network performance, which is an inevitable trend. With these observations, this article proposes a caching strategy based on the node value and content popularity (NVCP) for the massive VANET scenario. In the proposed NVCP scheme, different from the traditional caching strategies, we evaluate the node value from three aspects: the connectivity, intermediary, and eigenvector centralities, synthetically, since the content with different types of popularity is placed in nodes with different values, resulting in the redundancy deterioration and diversity improvement for the content. The proposed caching strategy is evaluated by the stochastic network topology with multifactors, which provides different impacts on the system performance. Simulation results show that the NVCP outperforms the traditional cache strategies for 6G‐CCN in terms of the cache hit ratio, average hop count, and transmission latency. Moreover, placing the content in the neighbor nodes is also introduced to further improve the utilization of the cache space and achieve better cache performance.