This paper presents a comprehensive survey on anti-drone systems. After drones were released for non-military usages, drone incidents in the unarmed population are gradually increasing. However, it is unaffordable to construct a military grade anti-drone system for every private or public facility due to installation and operation costs, and regulatory restrictions. We focus on analyzing anti-drone system that does not use military weapons, investigating a wide range of anti-drone technologies, and deriving proper system models for reliable drone defense. We categorized anti-drone technologies into detection, identification, and neutralization, and reviewed numerous studies on each. Then, we propose a hypothetical anti-drone system that presents the guidelines for adaptable and effective drone defense operations. Further, we discuss drone-side safety and security schemes that could nullify current anti-drone methods, and propose future solutions to resolve these challenges.

Following striking developments in Unmanned Aerial Vehicle (UAV) technology, the use of UAVs has been researched in various industrial fields. Furthermore, a number of studies on operating multiple autonomous networking UAVs suggest a potential to use UAVs in large-scale environments. To achieve efficiency of performance in multi-UAV operations, it is essential to consider a variety of factors in UAV network conditions, such as energy efficiency, network overhead, and so on. In this paper, we propose a novel scheme that improves the energy efficiency and network throughputs by controlling the topology of the network. Our proposed network topology control scheme functions between the data link layer (L3) and the network layer (L2). Accordingly, it can be considered to be layer 2.5 in the network hierarchy model. In addition, our methodology includes swarm intelligence, meaning that whole topology control can be generated with less cost and effort, and without a centralized controller. Our experimental results confirm the notable performance of our proposed method compared to previous approaches.

This paper addresses a novel approach for multi-agent control systems including Unmanned Vehicles (UV). As UV technology advances, one or a group of UVs can be used in a wide range of industrial or military applications. Centralized monitoring has limitations on various resources (e.g. communication bandwidth, propagation delay, and computational power), but can lead to an optimal solution to the movement of the swarm. The artificial potential field (APF) method is well-known for modeling decentralized behavior, but most APF research only focuses on naive actions such as collision avoidance, flocking, or path planning. In our proposed design Versatile Multi-Vehicle Control System (VMCS), we defined high-level conditions as APF and let UVs perform swarm intelligence in various mission environments. Furthermore, we devised a novel algorithm that controls the UVs’ APF topology which can significantly enhance the mission efficiency. We simulated the VMCS in 3D space and showed our scheme can control the dynamic mission scenarios for multi-UV systems.