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Unmanned aerial vehicles (UAVs) have attracted much attention for civil and military uses because of their high mobility and adaptable deployment capabilities in open spaces. They facilitate agile communications and ubiquitous connectivity. UAVs benefit from dominant line-of-sight communication links but are more susceptible to adversary eavesdropping attacks. Since upper-layer cryptography methods may be insufficient, physical-layer security (PLS) is an attractive alternative. PLS capitalizes on the inherent randomness of wireless channels to improve information confidentiality, particularly in UAV systems. This article provides a comprehensive overview of PLS in the context of UAV systems, examining various communication channels, including ground-to-air (G2A), ground-to-ground (G2G), air-to-ground (A2G), and air-to-air (A2A). First, we give a general review of UAV communications, emphasizing its functions as user equipment, base station (BS), and mobile relay. Following that, we provide an overview of PLS and its forms. The survey organizes the most advanced PLS techniques against passive wireless eavesdropping regarding the channels they are designed to protect. These schemes are further categorized into optimization and secrecy performance analysis. We examine technical aspects within each sub-category and classify core contributions based on approaches and objectives to improve secrecy performance. Moreover, we added summary tables of the identified schemes considering optimization and secrecy performance analysis domains for each category, providing an in-depth understanding of their technical aspects. In addition, we discuss open research issues and identify future research directions to address evolving threats and requirements in UAV communications to improve information confidentiality.

Unmanned aerial vehicles (UAV) have recently proved their ability to afford reliable and cost-effective solutions for many real-world scenarios. The autonomy, mobility and flexibility nature along with communications interoperability have made UAV able to provide a large variety of services. Awareness of context changes and adaptability to current services requirements are the key to UAVs' deployment success. For this reason, machine learning (ML) has been widely used in overcoming the challenges that UAV faces in mobility, communication and resources management. This paper will mainly focus on the proposed UAV-centric ML solutions and their satisfaction with network requirements taking into consideration UAVs' roles, collaboration, cooperation and network changing contexts. Solutions proposed in air to air, air to ground and ground to air communications as well as UAVs-enabled mobile edge computing are investigated for possible future insights. Future works will indeed highlight the need of UAVs' cooperation in the emergent 5G/6G networks. [ABSTRACT FROM AUTHOR]

Geometry-based stochastic modeling (GBSM) for UAV air-to-ground (A2G) channel usually assumes fixed velocity and moving direction, which can't describe the non-stationary characteristics of the UAV air-ground propagation environment. Facing the UAV 3D air-to-ground channel model based on double cylindrical scatterer, this paper proposed to introduce a Gauss Markov mod el with dynamic speed and moving direction into the A2G to simulate the changes of the UAV in the real environment. In order to make the model simulate different motions, it introduced an expansion factor to switch the motion trajectory of the model. In addition, this paper used the geometric analysis method to derive the space-time correlation function and Doppler power spectral density based on the time-varying speed and direction of motion, and studied the influence of the UAV horizontal and vertical movement on the channel statistical characteristics. The simulation results show that the extended model only cause non-stationarity in the time domain without affecting the air space, and verifies the generality of the extended model. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have attracted much attention for civil and military uses because of their high mobility and adaptable deployment capabilities in open spaces. They facilitate agile communications and ubiquitous connectivity. UAVs benefit from dominant line-of-sight communication links but are more susceptible to adversary eavesdropping attacks. Since upper-layer cryptography methods may be insufficient, physical-layer security (PLS) is an attractive alternative. PLS capitalizes on the inherent randomness of wireless channels to improve information confidentiality, particularly in UAV systems. This article provides a comprehensive overview of PLS in the context of UAV systems, examining various communication channels, including ground-to-air (G2A), ground-to-ground (G2G), air-to-ground (A2G), and air-to-air (A2A). First, we give a general review of UAV communications, emphasizing its functions as user equipment, base station (BS), and mobile relay. Following that, we provide an overview of PLS and its forms. The survey organizes the most advanced PLS techniques against passive wireless eavesdropping regarding the channels they are designed to protect. These schemes are further categorized into optimization and secrecy performance analysis. We examine technical aspects within each sub-category and classify core contributions based on approaches and objectives to improve secrecy performance. Moreover, we added summary tables of the identified schemes considering optimization and secrecy performance analysis domains for each category, providing an in-depth understanding of their technical aspects. In addition, we discuss open research issues and identify future research directions to address evolving threats and requirements in UAV communications to improve information confidentiality. [ABSTRACT FROM AUTHOR]

As a special form of microgrid, an apartment to grid (A2G) system is conceptually designed and proposed for demand and cost sensitive energy management based on the energy implications and electricity consumption characteristics of apartment complexes in Korea. The A2G system aggregates generators as well as loads, and its energy management decisions are made by A2G System Manager with energy profiles from the aggregated components in the system. Fundamental factors such as energy policy, energy optimization criteria, and fair benefit sharing issues are dealt with exemplary scenarios and case studies. Load shifting/shaping can be easily achieved by the coordination of the A2G system components which are already closely and densely aggregated unlike those components in a typical microgrid as well as V2G. Then, a network of A2Gs, namely an A2G Network is introduced as a business model to promote the benefit of aggregation. The main contribution of the proposed A2G system comes from it's innate nature, aggregation of its components; both economic benefits as well as system stability could potentially be achieved through more effective load shifting/shaping by the coordination of the existing aggregated A2G system components. [ABSTRACT FROM AUTHOR]