As a promising communication paradigm in the 5G era, IEEE 802.11ad 60GHz mmWave communication has many desirable properties, including high bandwidth, narrow beam, high transmission quality, and low mutual-interference. However, recently revealed Man-in-the-middle (MITM) attack can endanger the security and privacy of mmWave communication systems. Existing MITM attack detection solutions, established on ideal assumptions, neglect the fact that a MITM attacker can change its transmitting power and make it hard to be detected. In this backdrop, a cross-layer location consistency-based MITM detection and localization algorithm is introduced in this paper. Firstly, we propose a positioning algorithm based on the information of the physical layer with a directional antenna sector propagation model; secondly, based on the information at the MAC layer, we propose a MITM attack detection scheme and a sector consistency check algorithm; thirdly, a MITM attack detection and localization algorithm using cross-layer information is proposed. Simulation results have shown that the proposed algorithm can effectively detect the MITM attack and its positioning error is less than 0.53m in typical parameter settings.

Networking Radar-Communication Integrated System (RCIS) promises an improved performance of radar and communication system platform by expanding scanning range and sharing information with each other. But equipping RCIS with directional antennas, in return, will worsen network's connectivity. In this paper, we aim to address the problem of neighbor discovery stage in networking process. Then, we are to present a directional neighbor discovery algorithm called scan based networking radar algorithm, or, SBA-RA. The difference between SBA-RA and previous researches which assume no prior knowledge of neighbors, is that SBA-RA makes full use of the approximate location information of neighbors obtained from RCIS platform to accelerate neighbor discovery. What is more, the performance of SBA-RA in RCIS is evaluated, and then compared with Scan-Based Algorithm (SBA) and Completed Random Algorithm (CRA) in terms of time consumption. And the experiments indicate SBA-RA outweighs the others. In the end, future possibilities to improve SBA-RA are discussed.

Millimeter wave (mmWave) unmanned aerial vehicle (UAV)-aided networks have enormous application potential due to their large bandwidth and ultra-high speed, being regarded as an effective technology for improving the reliability of military and civilian fields. However, due to their complex electromagnetic spectrum environment and the sensitivity of mmWaves to blocking effects, its performance analysis faces certain difficulties. This article investigates the coverage and network capacity of mmWave UAV-aided networks under significant blocking effects and complex electromagnetic environments; for this purpose, we equipped each UAV with mmWave antennas featuring adjustable beamwidth and direction. A Matérn hard-core point process (MHCPP) with repulsion constraints was also employed to reflect the minimum distance constraints to isolate the mutual interference between UAVs. Then, using a stochastic geometric analysis, we derived the coverage and capacity characteristics and further obtained a closed-form expression for the network coverage probability. Finally, the simulation results showed that the network throughput could reach 86% when the density of UAVs was half of that of ground base stations (GBSs) in the city center, validating the efficiency and accuracy of our theoretical derivations.

In recent years, with the acceleration process of smart cities, the Artificial Intelligence of Things (AIoT) has become a novel approach for emergency rescue. However, due to small capacity and heterogeneity, AIoT nodes are facing various energy limitations and complex routing challenges. Therefore, emergency AIoT often have issues such as data transmission failures, energy consumption, reduced network lifespan and latency. To overcome these problems, this study proposes an energy efficiency emergency air-ground integrated heterogeneous AIoT (E3-HetAIoT) structure to improve the energy management efficiency of smart cities. Firstly, we adopt a cell-based clustering mechanism, and the multi-objective zebra optimization algorithm (M-ZOA) is used to select cluster heads (CHs) and super nodes (UNs). The UN uses an urgency level-driven energy efficient sleep scheduling (U-ESS) mechanism to balance the remaining energy of sensors, especially in scheduling sleep time slots for sensors have energy below typical threshold. Secondly, an air-ground integrated data transmission mechanism is adopted, in which the Generator de Bits Pseudo Aleatorios (GBPA) is used to eliminate redundant data in CH and improve security. Then, the data packets are divided into normal packets and emergency ones. The normal packets wait for transmission of unmanned aerial vehicle (UAV) U-iAgents, the trajectory of U-iAgents are dynamically predicted by Dueling Double Deep Q-Network (Dueling-DDQN), meanwhile emergency data packets are immediately transmitted through inter cluster routing. Simulation results demonstrate that compared to existing algorithms, our proposed E3-HetAIoT framework achieves lower energy consumption and higher network lifetime, respectively.

Maintaining high system performance is critical for a multi-hop flying mesh network (FlyMesh) to perform missions in different environments. Although the Poisson point process (PPP) has been widely used for the performance analysis of FlyMesh, it still has flaws in describing the spatial distribution of the UAVs since it does not restrict the minimum distance between them. The spatial deployment of FlyMesh varies depending on the environment. Considering the relevance and practicality, we modeled the multi-hop FlyMesh using the $\beta$-Ginibre point process ($\beta$-GPP) and equipped each UAV with a directional antenna. Under the condition of the decode-and-forward protocol, we derived the connection probability and ergodic capacity of a multi-hop FlyMesh utilizing the Laplace transform of interference. Then, we calculated an approximate expression for the interference Laplace transform based on the diagonal approximation and further obtained the coverage probability. Finally, the numerical simulation results verified the correctness of the theoretical derivation, indicating that it is possible to optimize the system’s performance based on the expressions derived in this paper.