In recent years, popularity of unmanned air vehicles (UAVs) is increasing due to their autonomous moving capability and applications in various domains. This raises some serious security threats that needs proper investigation to timely detect the amateur drones (ADr) for the protection of security sensitive institutions. In this paper, we propose the novel machine learning (ML) framework for ADr detection to correctly classify ADr sounds out of the various sounds like bird, airplanes, and thunderstorm in the noisy environment. To extract the necessary features from ADr sound, Mel frequency cepstral coefficients (MFCC) and linear predictive cepstral coefficients (LPCC) feature extraction techniques are implemented. After feature extraction, support vector machines (SVM) with various kernels are adopted to accurately classify these sounds. The experimental results verify that SVM cubic kernel with MFCC outperform LPCC method by achieving around 96.7% accuracy for ADr detection. Moreover, the results verified that the proposed ML scheme has over 17% detection accuracy, compared with correlation-based drone sound detection scheme that ignores ML prediction.

Sixth-generation (6G) networks pose substantial security risks because confidential information is transmitted over wireless channels with a broadcast nature, and various attack vectors emerge. Physical layer security (PLS) exploits the dynamic characteristics of wireless environments to provide secure communications, while reconfigurable intelligent surfaces (RISs) can facilitate PLS by controlling wireless transmissions. With RIS-aided PLS, a lightweight security solution can be designed for low-end Internet of Things (IoT) devices, depending on the design scenario and communication objective. This article discusses RIS-aided PLS designs for 6G-IoT networks against eavesdropping and jamming attacks. The theoretical background and literature review of RIS-aided PLS are discussed, and design solutions related to resource allocation, beamforming, artificial noise, and cooperative communication are presented. We provide simulation results to show the effectiveness of RIS in terms of PLS. In addition, we examine the research issues and possible solutions for RIS modeling, channel modeling and estimation, optimization, and machine learning. Finally, we discuss recent advances, including simultaneous transmitting and reflecting-RIS and malicious RIS.

Unmanned aerial vehicles (UAVs) have acquired remarkable popularity, thanks to their variety of applications in numerous domains spanning from surveillance, health to agriculture and smart cities. UAVs are also enabler in wireless communication that has potential features such as ubiquitous and reliable connectivity, fast and easy deployment, adaptive altitude, higher chance of line of sight (LOS) propagation path, higher mobility and flexibility. There are numerous surveys that summarized these advantages for different situations and scenarios. However, none of these surveys discussed the role of UAVs in public safety communications from the energy efficiency perspective. In this paper, we review the existing literature for UAV communication with taking into account the energy consumption criteria, and propose a multi-layered network architecture incorporating UAVs for public safety communication. Future research directions are also discussed.

The UAV or mini-drones equipped with sensors are becoming increasingly popular for various commercial, industrial, and public-safety applications. However, drones with uncontrolled deployment pose challenges for highly security-sensitive areas such as a President's house, nuclear plants, and commercial areas because they can be used unlawfully. In this article, to cope with security-sensitive challenges, we propose point-to-point and FANET architectures to assist the efficient deployment of MDrs. To capture an ADr, an MDr must have the capability to efficiently and quickly detect, track, jam, and hunt the ADr. We discuss the capabilities of the existing detection, tracking, localization, and routing schemes, and also present the limitations of these schemes as further research challenges. Moreover, the future challenges related to co-channel interference, channel model design, and cooperative schemes are discussed. Our findings indicate that MDr deployment is necessary for the care of ADrs, and intensive research and development is required to fill the gaps in the existing technologies.

Unmanned aerial vehicle (UAV) is a key enabler for communication systems beyond the fifth-generation due to its applications in almost every field including mobile communications and vertical industries. However, there exist many challenges in 3D UAV placement such as resource and power allocation, trajectory optimization, and user association. This problem becomes even more complex as UAV changes its height, which in turn varies the channel conditions and reduces the coverage on account of high co-channel interference. To maximize the user coverage in uplink transmission, we propose to jointly optimize the 3D UAV placement and path loss compensation factor. Moreover, we also optimize the latter for various UAV deployment heights in the suburban environment. Simulation results have demonstrated that the joint optimization of the UAV height and path loss compensation factor results in better coverage and throughput performance as compared to the baseline scheme.

The 5G communication systems will enable enhanced mobile broadband, ultra-reliable low-latency, and massive connectivity services. Broadband and low-latency services are indispensable to public safety (PS) communication during natural or man-made disasters. Recently, 3GPP-LTE has emerged as a promising candidate to enable broadband PS communications. In this article, first we present six major PS-LTE enabling services and the current status of PS-LTE in 3GPP releases. Then, we discuss the spectrum bands allocated for PS-LTE in major countries by ITU. Finally, we propose a disaster resilient three-layered architecture for PS-LTE (DR-PSLTE). This architecture consists of an SDN layer to provide centralized control, UAV cloudlet layer to facilitate edge computing or to enable the emergency communication link, and a radio access layer. The proposed architecture is flexible and combines the benefits of SDNs and edge computing to efficiently meet the delay requirements of various PS-LTE services. Numerical results verified that under the proposed DR-PSLTE architecture, delay is reduced by 20 percent as compared with the conventional centralized computing architecture.