Vehicular networks enable vehicles to generate and broadcast messages in order to improve traffic safety and efficiency. However, due to the non-trusted environments, it is difficult for vehicles to evaluate the credibilities of received messages. In this paper, we propose a decentralized trust management system in vehicular networks based on blockchain techniques. In this system, vehicles can validate the received messages from neighboring vehicles using Bayesian Inference Model. Based on the validation result, the vehicle will generate a rating for each message source vehicle. With the ratings uploaded from vehicles, Roadside Units (RSUs) calculate the trust value offsets of involved vehicles and pack these data into a “block”. Then, each RSU will try to add their “blocks” to the trust blockchain which is maintained by all the RSUs. By employing the joint Proof-of-Work and Proof-of-Stake consensus mechanism, the more total value of offsets (stake) is in the block, the easier RSU can find the nonce for the hash function (proof-of-work). In this way, all RSUs collaboratively maintain an updated, reliable, and consistent trust blockchain. Simulation results reveal that the proposed system is effective and feasible in collecting, calculating, and storing trust values in vehicular networks.

This article studies direct communications between user equipments in the LTE-advanced cellular networks. Different from traditional device-to-device communication technologies such as Bluetooth and WiFi-direct, the operator controls the communication process to provide better user experience and make profit accordingly. The related usage cases and business models are analyzed. Some technical considerations are discussed, and a resource allocation and data transmission procedure is provided.

Millimeter-wave communication is a promising technology for future 5G cellular networks to provide very high data rate (multi-gigabits-per-second) for mobile devices. Enabling D2D communications over directional mmWave networks is of critical importance to efficiently use the large bandwidth to increase network capacity. In this article, the propagation features of mmWave communication and the associated impacts on 5G cellular networks are discussed. We introduce an mmWave+4G system architecture with TDMA-based MAC structure as a candidate for 5G cellular networks. We propose an effective resource sharing scheme by allowing non-interfering D2D links to operate concurrently. We also discuss neighbor discovery for frequent handoffs in 5G cellular networks.

We propose a design of an ultra-broadband absorber based on a thin metamaterial nanostructure composed of a periodic array of titanium-silica (Ti-SiO₂) cubes and an aluminum (Al) bottom film. The proposed structure can achieve nearly perfect absorption with an average absorbance of 97% spanning a broad range from visible to near-infrared (i.e., from 354 nm to 1066 nm), showing a 90% absorption bandwidth over 712 nm, and the peak absorption is up to 99.8%. The excitation of superior surface plasmon resonance combined with the resonance induced by the metal-insulator-metal Fabry-Perot (FP) cavity leads to this broadband perfect absorption. The polarization and angle insensitivity is demonstrated by analyzing the absorption performance with oblique incidences for both TE- and TM-polarized waves. In addition, we discuss the impact of various metal materials and geometry structure on absorption performance in detail. The proposed broadband metamaterial absorber shows a promising prospect in applications such as solar cell, infrared detection, and imaging. Moreover, the use of a thin titanium cap and an aluminum film instead of noble metals has the potential to reduce production cost in applications.

By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet of Things (IoT) devices can be processed and analyzed at the network edge. However, the MEC system usually only has the limited virtual resources, which are shared and competed by IoT edge applications. Thus, we propose a resource allocation policy for the IoT edge computing system to improve the efficiency of resource utilization. The objective of the proposed policy is to minimize the long-term weighted sum of average completion time of jobs and average number of requested resources. The resource allocation problem in the MEC system is formulated as a Markov decision process (MDP). A deep reinforcement learning approach is applied to solve the problem. We also propose an improved deep Q-network (DQN) algorithm to learn the policy, where multiple replay memories are applied to separately store the experiences with small mutual influence. Simulation results show that the proposed algorithm has a better convergence performance than the original DQN algorithm, and the corresponding policy outperforms the other reference policies by lower completion time with fewer requested resources.